deliverable d3.2 evaluations of the concepts for the 5g ... · pdf fileproject name:...

Document Number: H2020-ICT-671650-mmMAGIC/D3.2

Project Name: Millimetre-Wave Based Mobile Radio Access Network for Fifth Generation Integrated

Communications (mmMAGIC)

Deliverable D3.2

Evaluations of the concepts for the 5G architecture and integration

Date of delivery: 30/06/2017 Version: 1.0 Start date of Project: 01/07/2015 Duration: 24 months

Document: H2020- ICT-671650-mmMAGIC/D3.2

Date: 30/06/2017 Security: Public

Status: Final Version: 1.0

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Deliverable /D3.2 Evaluations of the concepts for the 5G architecture and

integration Project Number: ICT-671650

Project Name: Millimetre-Wave Based Mobile Radio Access Network for Fifth Generation Integrated Communications

Document Number: H2020-ICT-671650-mmMAGIC/D3.2

Document Title: Evaluations of the concepts for the 5G architecture and integration

Editor(s): Hardy Halbauer (ALUD), Isabelle Siaud (Orange)

Authors: Hardy Halbauer (ALUD), Patrik Rugeland, Richard Tano, Miurel Tercero (Ericsson); Arnesh Vijay (Nokia PL), Yilin Li (Huawei), Miltiadis Filippou, Honglei Miao (Intel), Joerg Widmer, Claudio Fiandrino (IMDEA), Isabelle Siaud, Anne-Marie Ulmer-Moll (Orange), Mehrdad Shariat (SRUK), Javier Lorca (Telefonica), Yaning Zou (TUD)

Dissemination Level: PU

Contractual Date of Delivery: 30/06/2017

Security: Public

Status: Final

Version: 1.0

File Name: mmMAGIC_D32_v1.0




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Abstract

This deliverable D3.2 focuses on concepts for 5G systems operating in mm-wave frequency bands and the integration into the overall 5G system. The concepts rely on architectural enablers (essential for the integration) and RAN functionalities (for improving performance) designed to handle mm-wave specific challenges. The identified enablers are multi-connectivity, cell clustering, RRC_Inactive mobility state, self-backhauling, network slicing and interference coordination. The considered RAN functionalities are power/energy efficiency KPIs, transport layer performance improvements, reference signals supporting RRC mobility states, low frequency assisted initial access and functionalities for improvements of physical layer and user movement prediction and localization. These enablers and RAN functionalities are described and evaluated with respect to performance and ability to meet the architectural requirements of the addressed use cases. Energy efficiency is considered in a wider context of a multi-RAT framework, which can integrate various types of RATs. Finally, the interrelation and dependencies of the enablers and functions are highlighted, as well as the impact of interactions between them under specific conditions. As the final deliverable of WP3, D3.2 summarizes the WP3 outcome and shows the impact of the enablers and functionalities on target KPIs and their benefits for the overall system design.

Keywords

Multiple interface management, link adaptation Key Performance Indicator, mm-wave, 5G New Radio, network integration, deployments, architectural enablers, multi-connectivity, cell clustering, mobility state, mobility handling, self-backhauling, interference coordination, network slicing, multi-layer multi-RAT, energy efficiency

Acknowledgements

We would like to thank Danish Aziz (Nokia Bell Labs) and Markus Mueck (Intel) for their detailed review of this deliverable and the valuable comments to the editors.




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Executive summary

In the previous publications and deliverables pertaining to this project, a range of architectural concepts imperative for mm-wave Radio Access Technology (RAT) integration into the 5G eco-system were introduced.

Ever since, the concepts have evolved to the stage of evaluations, in terms of suitability and adaptability for the next generation mobile radio systems. Based on this scope, D3.2 provides a detailed assessment and evaluation of the presented concepts. The evaluations address the suitability of the concepts to current architecture and the envisaged deployment scenarios. Furthermore, the deliverable identifies architectural limitations and subsequently recommends new enablers, which can potentially enhance the existing architectural and functional features to support mm-wave integration, including the power, energy and cost efficiency considerations.

First, a vertical multi-RAT multi-layer management concept for future 5G is introduced. It leverages a multi-RAT multi-layer cross-layer entity to aggregate information from existing functional blocks and layers in terms of data rate, latency, reliability and key performance indicators (KPIs) designed in the project. This is a strong framework to achieve a close integration of mm-wave RAT and to reach optimum network operation.

Following that, the assessment of different enabling concepts for mm-wave integration is presented. Here, in six major subsections the topics of multi-connectivity, cell clustering, mobility state transition (i.e. RRC Inactive), self-backhauling, network slicing and interference coordination are covered. These concepts are essential enablers to support mm-wave integration. They cover a wide range of solutions. Multi-connectivity and the specific solution of tight interworking between LTE-NR system and RRC diversity are solutions to provide reliable coverage in several integration scenarios. The topic of cell clustering addresses user and control plane design for redundant coverage, with specificities to increase the reliability of mm-wave links. Then mobility state handling in the light of new state transition follows. Here, the analytical evaluation of the control plane latency requirement and the necessity for flexible discontinuous reception (DRX) mechanism for the new state is described. Under self-backhauling, the access-integrated backhaul and joint optimisation of the access and backhaul links are addressed. The topics of network slicing and interference coordination conclude this section.

In addition to the enablers, RAN and transport functionalities for network integration of mm-wave RAT have been introduced, which help to achieve performance targets for different use cases. Various attributes of power efficiency KPIs are evaluated, with results presented on transmit power gain in a multi-RAT context and the transport layer management under outage-prone mm-wave RAN. Additionally, the design of reference signals to support active and idle mobility with low frequency assisted initial access is presented. Further ahead, the section presents techniques for mobility improvements and evaluates some physical layer improvement techniques to increase the area capacity and coverage.

The concepts and functionalities studied in the project interact and impact each other when integrated in an overall system - this aspect is finally addressed. With the example of a dense deployment scenario the overall system model is shown, which is used to study the performance of multi-RAT scenarios to achieve mm-wave RAT network integration into the 5G system.

Finally, in the conclusion an overview on the main conceptual contributions from this work package as an enabler for next generation mobile radio systems is given and the impact and relevance of functionalities for different use cases are shown.




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Contents

1 Introduction ...................................................................................................................... 1 2 Multi-RAT/RAN management ........................................................................................... 2

2.1 Multi-RAT multi-layer management concept .............................................................. 2 2.2 The multi-RAN/RAT IP layer architecture .................................................................. 6

3 Enabling concepts for integration of 5G mm-wave systems.............................................. 7 3.1 Multi-Connectivity ...................................................................................................... 8

3.1.1 Impact of new functional blocks on generic architecture ..................................... 8 3.1.2 LTE-NR tight interworking system evaluations ................................................... 9 3.1.3 RRC diversity ................................................................................................... 13 3.1.4 Performance and architecture evaluations for ultra-reliable and low latency

systems ............................................................................................................ 15 3.1.5 Multi-band system integration .......................................................................... 17

3.2 Cell clustering ......................................................................................................... 20 3.3 RRC_Inactive .......................................................................................................... 20

3.3.1 Consideration of mobility state transition .......................................................... 20 3.3.2 Fulfilling the CP latency requirements .............................................................. 21 3.3.3 Assessing the best DRX scheme for RRC_INACTIVE ..................................... 25

3.4 Self-Backhauling ..................................................................................................... 26 3.4.1 Access-integrated backhaul in fixed wireless access ....................................... 26 3.4.2 Joint optimization of access and backhaul ....................................................... 29

3.5 Network slicing ........................................................................................................ 31 3.6 Interference coordination ......................................................................................... 32

4 RAN and Transport functionalities for network integration .............................................. 35 4.1 Power and Energy efficiency KPIs .......................................................................... 35 4.2 Transport layer performance studies under outage-prone mm-wave RAN .............. 37

4.2.1 Optimising transport via FEC............................................................................ 37 4.2.2 Optimization of RLC buffer size and timers ...................................................... 39

4.3 Design of reference signals to support active and idle mode mobility ...................... 40 4.4 Low frequency assisted initial access ...................................................................... 44

4.4.1 Downlink synchronization ................................................................................. 45

4.4.2 System information transmission ...................................................................... 45

4.4.3 Uplink synchronization ..................................................................................... 45 4.5 Mobility improvements............................................................................................. 46

4.5.1 User movement prediction ............................................................................... 46 4.5.2 User localization and environment mapping ..................................................... 46

4.6 Physical layer improvements ................................................................................... 48 4.6.1 Multi antenna techniques to increase area capacity ......................................... 48

5 Network integration ........................................................................................................ 50 5.1 Integration concept .................................................................................................. 50 5.2 Multi-RAT Network densification for PE deployment issues .................................... 51

5.2.1 System model .................................................................................................. 51 5.2.2 Multi-RAT and multiple access point scenarios ................................................ 54 5.2.3 Performance .................................................................................................... 55

6 Conclusions ................................................................................................................... 59 7 References ..................................................................................................................... 62 8 Annex ............................................................................................................................. 67

8.1 Annex I Section 3.4 RRC_Inactive .......................................................................... 67 8.2 Annex II: Control plane multi-connectivity – RRC diversity – simulation assumptions .. ................................................................................................................................ 69 8.3 Dynamic evaluations – Outdoor comparison ........................................................... 69 8.4 Annex IV: Distribution of inter-cell interference and signal-to-interference ratio in a

representative indoor mm-wave scenario .................................................................. 70




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List of Figures

Figure 2-1 Generic multi-layer architecture using 5G RAT link adaptation metrics for multi RAT management ........................................................................................................................... 3

Figure 2-2 The E-UTRAN architecture..................................................................................... 4

Figure 2-3 Abstraction layer-3 for multi-RAT management, source [SUMP16] ........................ 5

Figure 2-4 Overall architecture ................................................................................................ 6

Figure 3-1 Split bearer via MCG .............................................................................................. 8

Figure 3-2 SCG bearer ............................................................................................................ 9

Figure 3-3 Split bearer via SCG............................................................................................... 9

Figure 3-4 Split bearer illustration for control plane in 5G. ...................................................... 9

Figure 3-5 Network layout for LTE-NR tight interworking simulations [ATS+15]..................... 10

Figure 3-6 Simulation results for LTE at 2.6 GHz standalone, NR at 15 GHz standalone, and non-standalone deployment with LTE at 2.6 GHz and NR at 15 GHz. (a) 5th percentile; (b) median; (c) 95th percentile user throughput. ......................................................................... 10

Figure 3-7 Simulation results for LTE at 3.5 GHz standalone, NR at 26 GHz standalone, and non-standalone deployment with LTE at 3.5 GHz and NR at 26 GHz. Average throughput plotted against Active users per cell (cell area 0.0115 km2). ............................................................. 11

Figure 3-8 10th percentile user throughput plotted against active users per cell (cell area 0.0115 km2). ..................................................................................................................................... 12

Figure 3-9 Comparisons of average number of downlink transmissions and retransmissions for the standalone and non-standalone deployments (a) DC LTE-NR and LTE only; (b) DC LTE-NR and NR only. ................................................................................................................... 13

Figure 3-10 Architectural alternatives for RRC messaging. Rel-12/13 (left): RRC messaging only via MeNB and RRC Diversity (right): RRC messages can be sent either via MeNB or SeNB. Solid line – control plane connection, dashed line X2/Xn interface. ....................................... 13

Figure 3-11 Rate of RRC measurement reports received faster on the SeNB link. ................ 14

Figure 3-12 Packet latency for users with bad propagation conditions................................... 15

Figure 3-13 The outage probability map in single-connectivity mode in mmWave network with ISD equal to 200 meters. ....................................................................................................... 16

Figure 3-14 The outage probability map in dual-connectivity mode in mmWave network with ISD equal to 200 meters. (a) Multi-connectivity approach #1. (b) Multi-connectivity approach #2. 16

Figure 3-15 The outage probability in the area between two nodes (located: x = 0 m, y = 0 m and x = 500 m, y = 0 m) in mmWave network with ISD equal to 200 meters. (a) Multi-connectivity approach #1. (b) Multi-connectivity approach #2. .................................................................. 17

Figure 3-16 Relative attenuation due to Oxygen and hydrometeors, source [ETSI WP2+15]. 18

Figure 3-17 V and E band frequency plane with a common transmission channel size. ........ 18

Figure 3-18 V and E bands power regulations [ETSI_WP1+15]. ........................................... 19

Figure 3-19 Addition of cells served by secondary APs to the cluster set. ............................. 20

Figure 3-20 EMM State transition diagram for 5G systems .................................................... 21

Figure 3-21 Example signal flow diagram for transition from RRC Inactive to RRC Connected (without context fetch). .......................................................................................................... 22




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Figure 3-22 Signal flow diagram for transition from RRC Inactive to RRC Connected (with context fetch). ........................................................................................................................ 23

Figure 3-23 Optimized RAN area update ............................................................................... 25

Figure 3-24 DRX for next generation systems ....................................................................... 25

Figure 3-25 Concept of Self-backhauling. .............................................................................. 26

Figure 3-26 Illustration of a multi-hop FWA system with access-integrated backhaul. The APs serve the CPEs located at the residential houses and provide backhaul to neighboring APs. AP1 has a dedicated fiber backhaul. ..................................................................................... 27

Figure 3-27 Suburban scenario deployment (buildings and trees are shown in yellow and green respectively, APs are shown as red triangles). ...................................................................... 28

Figure 3-28 Access and backhaul radio resource utilization portions for the sectors with resource utilization higher than 50% for simulation .............................................................................. 28

Figure 3-29 Radio resource utilization for all the sectors for 25 Mbps and 40 Mbps aggregated CPE traffic demand ............................................................................................................... 29

Figure 3-30 Illustration of a HetNet with mm-Wave wireless BH and access. ........................ 30

Figure 3-31 Comparison of edge/average user throughputs for 100 users at carrier frequency of 28GHz and bandwidth of 1GHz ......................................................................................... 30

Figure 3-32 Comparison of average user throughputs for different number of users at carrier frequency of 28GHz and bandwidth of 1GHz. ........................................................................ 31

Figure 3-33 Slice selection at initial attach when Slice ID is provided by the UE to RAN ....... 32

Figure 3-34 Cumulative distribution of S/I at 10 GHz, 30 GHz, and 70 GHz. ......................... 33

Figure 4-1 SNR-to-CQI mapping and SE based MCS decision for LTE, source [SUM16]...... 35

Figure 4-2 Definition of the α-metric. ..................................................................................... 37

Figure 4-3 Application throughput of LT code and TCP for a file size of 52 MB. .................... 38

Figure 4-4 Sequence number per file received over time for TCP (a) vs. LT (b). ................... 39

Figure 4-5 Performance comparison of TCP protocols. ......................................................... 40

Figure 4-6 TCP performance after RLC buffer size optimization. ........................................... 40

Figure 4-7 Average SINR variations over UE route at 15 GHz. ............................................. 41

Figure 4-8 Results from a field test with beam switching. Approximately at t = 35 s, the gNB changes its Tx beam. When this happens, the propagation delay changes abruptly with 0.7 µs. This is larger than the cyclic prefix at 120 kHz sub-carrier spacing, which would make it difficult for the UE to synchronize to the newly received beam. ......................................................... 41

Figure 4-9 Daily traffic profile for typical European country [AGD+11]. .................................. 43

Figure 4-10 Daily average area power consumption at a) 95 and b) 1200 Mbps/km2. 5th percentile downlink user throughput at c) 95 and d) 1200 Mbps/km2. .................................... 44

Figure 4-11 Low frequency-assisted initial access to a heterogeneous network. ................... 45

Figure 4-12 (a) Path reconstruction in a scenario with 4 APs. (b) Localization error for different schemes. ............................................................................................................................... 47

Figure 4-13 Downlink user throughput for average and 5th percentile user using MIMO at 30 GHz. ................................................................................................................................. 49

Figure 5-1 GMTE platform with visualisation of the -metric variations in a geographical zone (GLB sub-metric). .................................................................................................................. 52




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Figure 5-2 A visualization of the GLB metric in the GMTE platform using University of Bristol measurements carried out at 82 GHz .................................................................................... 53

Figure 5-3 IMT2020 spectrum bands for 5G scenarios, [Nok,15] ........................................... 54

Figure 5-4 -metric variations upon the 5GHz IEEE802.11 ac/60 GHz UWB multi-RAT scenario .............................................................................................................................................. 56

Figure 5-5 Multi-band system integration application ............................................................. 57

Figure 5-6. metric variations on embedded IEEE802.11 ad transmission modes at 82 GHz. .............................................................................................................................................. 57

Figure 8-1 Simulation results with only outdoor users in a 3GPP dense urban scenario with LTE at 3.5 GHz standalone, NR at 26 GHz standalone, and non-standalone deployment with LTE at 3.5 GHz and NR at 26 GHz (a) 10%-ile throughput; (b) Comparisons of average number of DL transmissions between DC LTE-NR and NR only. ................................................................. 70

Figure 8-2 Desired signal received power level at 10 GHz (top), 30 GHz (middle), and 70 GHz (bottom). Vertical dimension corresponds to y axis, and horizontal dimension to x axis. ....... 71

Figure 8-3 Interfering signal received power levels at 70 GHz, according to several representative positions and orientations of the interfering node. Vertical dimension corresponds to y-axis, and horizontal dimension to x-axis. .................................................... 72




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List of Abbreviations

3G 3rd Generation

3GPP 3rd Generation Partnership Program

4G 4th Generation

5G 5th Generation

AI Air Interface

AMC Adaptive Modulation and coding

AP Access point

ARQ Automatic Repeat Request

AS Access Stratum

AWGN Additive white Gaussian noise

BER Bit Error Rate

BPSK Binary Phase Shift Keying

BRS Beam Reference Signal

BS Base Station

CAP Central Access Point

CDF Cumulative distribution function

CEPT European Conference of Postal and Telecommunications Administrations

CN Core Network

CoMP Coordinated Multipoint

CP Control plane

C-RNTI Cell Radio Network Temporary Identifier

CSL Communication Services Layer

CSI Channel State Information

CSI-RS Channel State Information Reference Signal

CQI Channel Quality Indicator

CW Congestion Window

D2D Device-to-Device

DC Dual connectivity

DIC Dynamic Interleaving Coding

DL Downlink

DRB Data Radio Bearer

DRX Discontinuous Reception

DSL Digital Subscriber Line

ECC Electronic Communications Committee

ECM EPS Connection Management

EGC Equal Gain Combining

EIRP Equivalent IsotropicallyRadiated Power

EMM EPS Mobility Management

eNB Evolved Node B

EPS Evolved Packet system

ETSI European Telecommunications Standards Institute

E-UTRAN Evolved UMTS Terrestrial Radio Access Network

FDD Frequency division duplex

FEC Forward Error Correction

FR Fast Recovery

FS Fast switch

FST Fast Session Transfer

FTP File Transfer Protocol

FWA Fixed Wireless Access

GLB Green link budget

GMTE Green multi-technology engineering

gNB NR base station

GPRS General Packet Radio Service

GPS Global Positioning System

GRE Generic Routing Encapsulation

GTP GPRS Tunnelling Protocol

HARQ Hybrid ARQ

HetNet Heterogeneous network

HH Hard Handover

HO Handover

HW Hardware

IEEE Institute of Electrical and Electronics Engineers

IP Internet Protocol

ISD Inter Site distance

ITU International Telecommunication Union

KPI Key Performance Indicator

LA Link Adaptation

L1 Layer one (PHY)

L2 Layer two (MAC/RLC/PDCP)

L3 Layer three (RRC/NAS)

LA Link Adaptation

LBI Low-band integration

LDPC Low-Density Parity-Check

LLC Logical Link Control

LOS Line of sight

LTE Long Term Evolution

MAC Medium Access Control

MBSI Multi-Band System Integration

MC Multi-Connectivity

MCG Master Cell Group

MCM Multipath Channel Margin

MCS Modulation Coding Scheme

MeNB Master eNB

MIM Multiple Interface Management

MIMO Multiple-Input Multiple Output

ML-MR Multi-layer Multi-RAT

MME Mobility Management Entity

MO Mobile Originated

MRC Maximum Ratio Combining

MT Mobile Terminated

MTC Machine Type Communication

MU-MIMO Multi User MIMO

MURI Multi-Radio Interface

NAS Non-Access Stratum

NB NodeB

NG Next Generation

NGMN Next Generation Mobile Networks

NLOS Non-Line-of-Sight

Document: H2020-ICT-671650-mmMAGIC/D3.2



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NR New Radio

OFDM Orthogonal Frequency Division Multiplexing

OOB Out of band

P2P Point-to-Point

PCFICH Physical Control Format Indicator Channel

PDCCH Physical Downlink Control Channel

PDCP Packet Data Convergence Protocol

PDU Protocol Data Unit

PE Power Efficiency

PGW Packet gateway

PHY Physical layer

PLCP Physical Layer Convergence Protocol

PLM Path Loss Margin

PLMN Public land mobile network

PRACH Physical Random access channel

PRB Physical Resource Block

QCL Quasi co-located

QoE Quality of Experience

QoS Quality of Service

RACH Random Access channel

RAN Radio Access Network

RAR Random access response

RAT Radio Access Technology

RB Resource Block

RCF Radio Control Framework

RF Radio frequency

RLC Radio Link Control

RLC UM RLC Unacknowledged mode

RLC-AM RLC Acknowledged mode

RLF Radio Link Failure

RNC Radio Network Controller

RRC Radio Resource Control

RRFI Reconfigurable Radio Frequency Interface

RRM Radio Resource Management

RRS Reconfigurable Radio System

RRU Remote Radio Unit

RS Reference Signal

RSRP Reference Signal Received Power

RSRQ Reference Signal Received Quality

RSSI Received Signal Strength Indicator

RTO Retransmission Timeout

RTT Round-trip time

RX Reception

SAP Service Access Point

S/I Signal to Interference ratio

SBH Self-backhaul

SC Selection Combining

SCC Spatially Coupled Combining

SCG Secondary Cell Group

SDMA Space-division multiple access

SDU Service Data Unit

SE Spectral Efficiency

SeNB Secondary eNB

SF Service Flow

SGW Serving gateway

SINR Signal to Interference plus Noise ratio

SNR Signal to Noise ratio

SS Synch Signal

SW Software

TA Tracking area

TAU Tracking area update

TB Transport Block

TCP Transmission Control Protocol

TDD Time-division duplex

TDMA Time-division multiple access

TM Transmission mode

TRxP Transmission/reception point

TT Transmission technique

TTI Transmission time interval

TX Transmission

UC Use case

UDP User Datagram Protocol

UE User equipment

UL Uplink

UMTS Universal Mobile Telecommunications System

UP User plane

URA Unified Radio Applications

URAI Unified Radio Application Interface

USS Uplink Synch Signal

VoA Vector of arrival

SA2 System Access




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Definitions of technical terms

Term Definition

Immersive 5G experience

Immersive multi-media experiences including 4k/8k UHD video, virtual reality experiences and real time mobile gaming

Access link The wireless link connecting the access point and user equipment

Access point (AP)

The base station in mm-wave network

Analog beamforming

Beamforming function is implemented in the analog part.

Antenna element

A single antenna in an array of antennas

Antenna gain The ratio of the power produced by the antenna from a far-field source on the antenna's beam axis to the power produced by a hypothetical lossless isotropic antenna, which is equally sensitive to signals from all directions

Backhaul These are portions of the network comprising intermediate connections between the core networks and the sub-networks (i.e. radio access nodes) attached to the macro-cell, and connections in-between the radio access nodes

Backhaul network

Serves as the transport medium for a mobile radio access network (RAN) and connects the access points to core network. In case of multi-hop backhaul, connections between access points are also part of the backhaul network

Baseline use case

A use case which will be the focus of research and KPI extensions from this baseline can generate the conditions for other use cases.

Carrier aggregation

This is a data transmission technique, where the network utilizes two or more carrier frequencies to transmit and receive data to/from the UE in downlink and uplink

Control Plane

The part of the network that carries control information (also known as signalling) to provide functionalities such as connectivity management, mobility management, radio resource control etc

Co-primary users

Primary users occupying same frequency bands with similar level of priority

Dedicated Backhauling

The connection between specific radio access nodes and the core network are only used for backhauling

Deployment Characterization of the network layout ( i.e., physical and logical locations); but also RAN configuration like antennas, Tx power, frequency band, bandwidth, system features and supporting architectural solution

Digital beamforming

Beamforming function is implemented in the BB part.

Distributed MIMO

Multiple antennas for one node of communications are distributed among multiple widely-separated RAN or backhaul nodes, and independent large-scale fading and small scale fading are experienced for each link in between.

Dynamic Backhauling

The backhaul network topology can be adaptively changed based on certain load balancing and service-aware routing schemes to avoid congestion and optimize quality of service statistics, especially in case of multi-hop and mesh topologies

Enabler Solutions essentially needed to fulfil technical challenges and system requirements covered by different functionalities identified in the project.

Edge-less This defines new ways of connection and interaction; whereby, the devices are no longer just end-points, but are integral part of the mobile network, experiencing uniform coverage (where there is no perception of cell-edges); combined with greater capacity and high quality uninterrupted mobile services

Fixed-beam No beamforming functions implemented in either BB or RF

Fronthaul Connections between a network architecture of centralized baseband controller and remote standalone radio heads at cell sites

Fronthaul Connections between a network architecture of centralized baseband controller and remote standalone radio heads at cell sites

High GHz range

70-100 GHz

Hybrid beamforming

Architectures mixing digital and analog beamforming techniques in order to provide a performance trade-off on flexibility, multiplexing, power consumption and cost.




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Hybrid Network

A network topology that uses a combination of two or more different topologies, in such a way that the resulting network exhibits an advanced functionality than those of the standard networks

In-Band Backhauling

Wireless backhaul connection which occupies the same frequency band as the access network

Integrated backhaul

The wireless backhaul technology is combined/integrated with other traditional backhauling technologies such as optical fibre systems

Key performance indicator (KPI)

A quantifiable measurement, agreed beforehand, that reflects the critical success factors of a proposed solution; it reflects the goals captured by each use case. The KPIs are linked to the use case, so as to link the proposed solutions with the usage driven test cases

Layer Model Provides a functional division of communication tasks. The task of each layer is to provide services for the layer immediately above it, using the services provided by the underlying layer to do so. The services within a layer are mapped to entities

Linearity range

The spectrum range within which the transmission/reception chains operate in a linear manner, i.e., the output power is linear with input power.

Low GHz range

6-30 GHz

Massive MIMO

Massive MIMO (also known as Large-Scale Antenna Systems, Very Large MIMO, Hyper MIMO, Full-Dimension MIMO and ARGOS) uses a very large number of service antennas (e.g., hundreds or thousands) that are operated fully coherently and adaptively.

Mid GHz range

30-50 GHz

mm-wave spectrum

General definition of mm-wave spectrum includes frequencies between 30 and 300 GHz. However, as studied in mmMAGIC, it refers strictly to 6-100 GHz excluding 50-70 GHz.

MU MIMO MU MIMO is designed to deliver parallel high rate data to multiple UEs, each with one or more antennas.

Multi-antenna

A number of service antennas are implemented and operate coherently and adaptively

Multi-Connectivity

This is a key technology to fulfil 5G requirements on data-rate, latency, reliability and availability. The term multi-connectivity can accommodate a broad range of techniques, but with one objective: for a given UE, radio resources in the network are configured from two or more different access points.

Multi-node With adaptive joint transmission/reception in coordinated multipoint, the antennas of several RAN or backhaul nodes are regarded as part of one distributed antenna array - in reception and transmission.

Network architecture

Network architecture defines a number of logical RAN and backhaul node and their functionalities, the way that they are grouped and the interfaces between end users, RAN nodes, and backhaul nodes; between the core and RAN node, between RAN nodes, and between backhaul nodes.

Network element

A facility or equipment used as a manageable logical entity uniting one or more physical devices. Network element is a system that can be managed, monitored, or controlled in a telecommunications network, that has one or more standard interfaces, and is identified by a unique management address

Network layers

In a single-RAT scenario, a network layer could refer to either macro cell or small cells deployed in the same area. On the other hand, in a co-deployed multi-RAT scenario, a network layer refers to a communication network supporting a unique radio access technology

Network segments

Group of network nodes of a communication network, which is physically or logically separated from the rest of the network

Network-in-a-node

The application server running at the access point or very close to it

Out-band Backhauling

Wireless backhaul connection which occupies different frequency band to that of the access network

Propagation environment

Defines the medium and propagation conditions between the access point (AP) and the User Equipment (UE)




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Radio Access Network Architecture

RAN architecture defines a number of logical RAN node and their functionalities, the way that they are grouped and the interfaces between end users and RAN node; between the core and RAN node, and between RAN nodes

Radio Access Technology

Technology that is used to connect different terminals and applications to telecommunication networks by using radio frequency signals

Radio Interface

Interface between the mobile station (MS) and the radio equipment in the network, defined by functional characteristics, common radio (physical) interconnection characteristics, and other characteristics, as appropriate. Radio interface spans over PHY and MAC layers

Remote Radio Head

Radio element of a base station separated from the baseband controller

Requirement Each use case is characterized by different needs in terms of KPIs. The quantified needs are called requirements

Self backhauling

The radio access node autonomously establish backhaul connectivity to the existing network infrastructure and start operation in a plug-and-play fashion

Spectrum suitability

The applicability of a particular spectrum for one or one group of use cases and KPIs.

Standalone Network

A network which is capable of operating independently of any other device or system

Sub-6 GHz Refer to frequencies that are lower than the 6-100 GHz frequencies investigated in mmMAGIC. This term is used in contrast to mm-wave network especially in the context of overlay network, where mm-wave network is assisted by sub-6 GHz network.

Test scenario

Practical aspect formulated from end-users’ perspective. It defines the physical properties of the environment where the end users are located when having particular services. Hence, each test case contains a set of assumptions, constraints and requirements. An use case may cover several concrete test cases; a test case may have several challenges and therefore belong to several use cases

Transceiver architecture

Transceiver architecture defines a number of components, such as power amplifier, antenna, BB part, etc., and their functionalities, the way that they are grouped and the interfaces between them.

Ultra-lean transmission

Transmission which minimizes energy consumption of network nodes

Use case General account of a situation or course of actions that may occur in the future. It is described from end-user perspective and illustrates fundamental challenges. It provides an example on how, when and where end users can utilize mobile communication for having particular services

User Equipment

Unified term for user equipment, user terminal, mobile devices etc.

User Plane The part of the network that mainly carries user traffic (also known as data plane) coupled with minimized control signaling such as multiple access information




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1 Introduction

The WP3 of mmMAGIC is dedicated to investigate and develop functionalities and solutions to achieve the integration of mm-wave communication systems into the overall 5G radio access network (RAN). The range of use cases envisioned for the new generation of communication systems goes far beyond voice and mobile broadband applications, having impact on all layers of the system. Among the different aspects of new system design, WP3 takes care of architectural aspects, focusing on architectural elements and concepts essentially needed to operate mm-wave RATs and to integrate them with other new and legacy RATs.

In former work [MMMAG15-IR31], [NGMN15], [NoK15] several use case families and deployment scenarios have been envisaged. However, during 3GPP standardisation process the use cases belonging to extreme mobile broadband (xMBB) gained highest prioritisation. In this context, the related xMBB use cases defined in [MMMAG15-D11] are “media on demand”, “cloud services”, immersive early 5G experience” and “smart office”. They are characterized by both high connection density and high data rates, and therefore provide the highest challenges to be addressed from the RAN architecture perspective. Further, it is expected that these use cases will be deployed in ultra-dense scenarios, adding specific challenges for network management and backhaul provisioning.

This deliverable focuses on concepts for mm-wave systems as identified along the project work and the integration into the overall 5G system. So the 5G mm-wave system is seen as a part of a wider 5G context consisting of an overall multi-RAT framework into which it will be integrated. This generic view and the extension with the mmMAGIC mm-wave system is explained in Section 2.

In WP3 specific architectural enablers and concepts have been developed and analysed, which are mandatory to address the challenges mentioned above. These mandatory enablers are multi-connectivity, cell clustering, a new mobility state “RRC_INACTIVE”, self-backhauling, network slicing and interference coordination. Further, technology components and RAN functions, which complement the set of features needed for a successful integration of mm-wave RATs into the overall 5G system have been investigated. Section 3 and 4 describe the specific capabilities and requirements for these enablers and RAN functions and point out why these concepts are essential for a successful integration of the mm-wave systems. Architectural analysis and quantitative evaluations show the importance and the impact of the enablers and RAN functions on the performance of different use cases.

The network integration, as described in section 5, shows the interrelation and dependencies of the enablers and functions. As an example for network densification, a power efficient (PE) system model compliant with the vertical multi-RAT architecture, is integrated in a dedicated software engineering platform and different mmMAGIC scenarios are evaluated.

As the final deliverable of WP3, this deliverable summarises the WP3 outcome and shows the impact of the enablers and functionalities on target KPIs and overall system design and performance.




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2 Multi-RAT/RAN management

The management of several RATs in a heterogeneous multi-RAT network involves the use of dedicated KPIs (power, energy and cost efficiency, flexible QoS, multiple access point (AP) selection) to switch from one technology to another one, and their integration in multi-RAT/RAN architectures. The generic concept carries out a vertical multi-layer multi-RAT management depending on technical requirements to activate the appropriate technologies in accordance with involved KPIs in the RAT/RAN selection process. Three layer levels are envisioned in the general architecture.

2.1 Multi-RAT multi-layer management concept The multi-RAT/RAN management leverages a vertical multi-layer management of several interfaces and transmission modes (TMs) related to involved technologies in the RATs management process [SUM16] [SUMP16] taking into account optimization criteria (power and spectral efficiency, radio coverage, etc..). Such multiple interface management (MIM) may cover three independent abstraction layers, where the activated abstraction layer depends on functional blocks that are required to activate the concerned air interface TMs in charge of the transmission between the two communication entities. The abstraction layer 3 residing at the IP/network layer covers the largest envisioned mmMAGIC scenarios compliant with the release 13 of the 3GPP WLAN/LTE-A carrier aggregation protocols [3GPP TS 36.401].

Link adaptation (LA) metrics reflecting proper KPIs for optimization are the input of the multi-RAT/RAN management processing in order to select and activate the target air interface and TMs. These metrics depend on propagation conditions and optimization criteria. The green link budget (GLB) metric [SUM16] is the candidate LA metric for power and energy efficiency optimization in the multi-RAT context where independent interfaces may be assessed to carry out the radio link communication. The selection process between several interfaces and TMs embraces equivalent throughput TMs related to the transported service and target QoS. Each P2P link is independently selected in the multi-user context. The GLB metric allows a link budget comparison between interfaces exhibiting independent power sensitivity levels and different radio frequency spectrum operations (see section 4.1)

Innovative KPIs, as recently introduced in the ETSI Reconfigurable Radio System (ETSI RRS) technical committee, are computed at the lowest layer, typically at the PHY layer based on Received Signal Strength Indicator (RSSI) and link budget elements [SUM16] deduced from involved interfaces in the multi-RAT process [ETSI-RRS_1+16].

The power/energy efficient LA metric adopted in mmMAGIC in order to optimize power and cost efficiency is described in [SUM16] and has been selected for multi-RAT power management issues (section 4.1). In the ICT FP7 MiWEBA project [MiWEBA], the GLB metric and the general procedure to estimate it at the PHY layer were designed. In mmMAGIC, the GLB metric is evaluated on mmMAGIC scenarios in the context of urban densification embracing complementary mm-wave RF bands and aggregated systems. A power management metric is then derived from the GLB in order to explicitly estimate transmit radiated power gains related to multi-RAT network deployments. Results (5.2.3) are given in E-bands and Wi-Fi hot spots, implementing power efficient MIMO techniques. Network densification integrating the GLB metric in radio engineering tools led to the development of a green multi-technology engineering (GMTE) platform and a collaboration between Orange and the University of Bristol within the

project [MMMAG17-D13].

Figure 2-1 illustrates the general multi-RAT architecture using 5G RAT link adaptation metrics to select the most appropriate technology (technology 1, 2 or 3 following a generic approach), to establish communications between the transmitter and the receiver. LA metrics are computed using available PHY parameters as the RSSI and context information provided by physical layer convergence procedure (PLCP) headers and signalling headers of every concerned RAT. Metrics are then forwarded to the appropriate layer to initiate air interface and TM switching.




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Within the work in mmMAGIC, the spectrum management considering a subset of several RF bands (from RF band#1 to RF band #N) has been incorporated into the multi-layer architecture, leading to the multi-band system integration exposed in the section 3.1.5.

Figure 2-1 Generic multi-layer architecture using 5G RAT link adaptation metrics for multi RAT

management

Depending on involved technologies in the multi-RAT scenario, each layer is able to manage interfaces having their own capabilities to exchange context information together.

The MIM abstraction layer 1 exploits PHY and MAC protocols to exchange information and forward the metric decision to appropriate PLCP headers transporting TM information. Switching from one Modulation and Coding Scheme (MCS) to another in a single RAT may be possible in this configuration. A selection of different MIMO algorithms and transmission bandwidth sizes for a single interface is another illustration of MIM abstraction layer 1.

A switching between IEEE802.11 ac TMs and IEEE802.11 ad TMs may be also implemented using the fast session transfer protocol designed in the IEEE802.11 ad standard IEEE802.11ad12].

The multi-band system integration (see section 3.1.5), enabling multi-RF channel hopping between different RF channels combined with TMs management of a single interface, may also efficiently use the MIM abstraction layer 1 in order to conjunctly select the RF band and the TM in compliance with radio coverage, power management and QoS requirements.

The MIM abstraction layer-2 requires an additional Layer enable to manage independent interfaces in the case of a missing common context information exchange. The I-MAC layer [KBN12] which was designed in the ICT-FP7 OMEGA project, illustrates a concrete hardware and software implementation of MIM for indoor communications using this abstraction layer-2.

The MIM abstraction layer-3 refers to the multi-RAN IP layer described in Section 0, which combines gNBs and eNBs aggregation that require multi-RAT management to perform mm-wave and LTE-A carrier aggregation. The control-plane then forwards the metric decision

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evaluated at the PHY layers to the gNB or eNB and the decision is transported at the user plane using X2 interface to achieve data transport between communication entities

Context information exchange result from the E-UTRAN architecture (Figure 2-2) with extensions to other RATs. .According to 3GPP TR 125.912 (release 13) [3GPP TR 125 912], E-UTRAN is described as “The evolved UTRAN consists of eNBs, providing the evolved UTRAN U-plane and C-plane protocol terminations towards the UE. The eNBs are connected with each other by means of the X2 interface. It is assumed that there always exist an X2 interface between the eNBs that need to communicate with each other, e.g., for support of handover of UEs in LTE_ACTIVE. The eNBs are also connected by means of the S1 interface to the EPC (Evolved Packet Core).

Figure 2-2 The E-UTRAN architecture

The abstraction layer-3 performs multi-RAT management at the IP level using RAT Termination (RT) mechanisms derived from the IEEE802.11 u standard [IEEE802.11u 11] to establish LTE/Multi-RAT aggregation and X2/Si interface connections.

First architectures of the MIM abstraction layer 3 have been designed in [SUMP16] where 60 GHz components are combined with the LTE-A for Wi-Fi hot spots (Figure 2-3) using Radio Termination (RT) module to activate different interfaces. On the Figure 2-3, the system uses Radio Termination (RT) in the CP to activate the radio link selection (thanks to the GLB metric feedback) between independent interfaces and implement it. The RT is proposed to connect the macro eNB to Multi-RAT APs and transfer user data traffic to UEs, following Link Adaptation (LA) metric decision and dedicated KPI, typically the GLB metric for power efficiency issues. The RT is the same as the “WT” (WLAN Termination) specified in 3GPP Release 13 for LTE/WLAN aggregation (LWA), [RMC16].

The architecture exposed on Figure 2-3 is an extension of the proposed RAN-level LTE/WiGig interworking detailed in [PYS15] for mm-wave LTE-A combination. Examples and performance are given when considering LOS/NLOS transitions upon a 60 GHz video transmission in [SUMP16].

In mmMAGIC, we have extended it with the combination of LTE-A and E-band transmissions implemented on embedded modes of the IEEE802.11 ad standard in accordance with the multi-band system integration developed in the section 3.1.5. In the section 5.2, results are extended to geographical access point position selection combined with RATs selection and transmit power minimization.

http://www.3gpp.org/DynaReport/25912.htm




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Figure 2-3 Abstraction layer-3 for multi-RAT management, source [SUMP16]

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2.2 The multi-RAN/RAT IP layer architecture The multi-RAN IP layer architecture is turned towards the MIM abstraction layer 3 exploiting control and user plane splitting to manage small cells controlled by macro-cells. The 5G network architecture shown in Figure 2-4, will comprise a large number of RAN entities, user plane (UP) and control plane (CP) elements. The RAN entities will in turn comprise legacy eNBs (evolved NodeBs) combined with enhanced eNBs, for which the term “gNB” has been recently introduced by 3GPP. It is envisioned that the new nodes will be capable of supporting mm-wave frequencies [3GPP TR 38.801]. Similar to those in existing technologies, the gNB (APs) must offer E-UTRAN UP and CP (i.e., through the RRC protocol) terminations towards the user equipment (UE). The existing LTE eNBs (LTE connecting to 5G CN is sometimes termed as “eLTE”), and the new gNBs will be interconnected by means of Xn interfaces. These existing and enhanced RAN elements can be directly connected to the core network by the NG-C and NG-U interfaces. The next generation (NG) logical connection is capable of supporting diverse connectivity amongst different MMEs, serving gateways and 5G-RAN elements.

Figure 2-4 Overall architecture




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3 Enabling concepts for integration of 5G mm-wave systems

This chapter describes the concepts and features envisaged for a seamless integration of mm-wave communication into the 5G systems. We first give an overview of the overall architecture and the key enablers, including multi-connectivity, tight interworking with LTE, cell clustering, new mobility states, self-backhauling, network slicing and interference coordination. The next sections illustrate for each key enabler concept description, performance evaluation and expected impacts.

The novel 5G architecture is designed to meet the requirements illustrated in [MMMAG17-W32] through the following enablers: multi-connectivity with several mm-wave access points (APs) to increase reliability; mm-wave cell clustering to handle mobility amongst the relatively small mm-wave cells; a new mobility state; network slicing concept to address diverse set of requirements; and self-backhauling to provide a low-cost solution to increase coverage. Simultaneous connectivity to mm-wave and low frequency band RATs brings the benefit of larger coverage and availability for control signalling, inter-cell coordination to reduce interference, and various architectural aspects of backhauling to serve a multitude of different AP distributions. The multi-connectivity concept is related to mm-wave cell clustering where the UE is primarily using one active connection to a mm-wave AP, but simultaneous connectivity to the low-band system is highly beneficial for control signalling from the CN. Integrating multi-connectivity with self-backhauling might be beneficial to enable fast data forwarding for buffer synchronization between the APs involved (in case where service flow is split at the PDCP level).

In more details:

Multi-connectivity: gNBs must be capable of supporting connectivity amongst different APs with a variety of technologies. Functions include the management of UP and CP for different radio interfaces, and/or carrier types. It also controls the service flow routing over multiple radio interfaces by means of data flow aggregation.

Tight interworking with LTE: By harmonizing the protocol stacks of LTE and NR, it will be possible to have a tight interworking between LTE and NR which will for instance, enable aggregation of carriers or fast switching between the RATs. Noticed that for tight interworking to happen the UE may or may not use dual-connectivity.

Cell clustering: in mm-wave communication, it is challenging to provide continuous connectivity for the active user in dynamic environments especially due to changing position of the UE and other objects in the scene. In case of such beam-based prone to obstruction links, it is crucial to provide mechanism that can handle switching of serving cells in a quick and transparent manner to CN. mm-wave node clustering is therefore one of the mandatory elements of the network architecture.

New mobility state (RRC_INACTIVE): the next generation mobile radio is expected to consist of a large number of densely deployed and mixed APs, with greater user mobility to support a number of Use Cases (UCs). In [MMMAGIC16-D31] it was proposed to introduce a new RRC state to complement the existing ones and allowing a UE to benefit from several aspects of the two original states.

Self-backhauling: in mm-wave bands the dynamic nature of the radio channel demands dense and redundant APs in order to provide full coverage in a given area. Due to prohibitively expensive cost of dedicated backhaul for very dense deployments, self-backhauling is seen as an essential enabler to provide ubiquitous 5G coverage. Thus, the two main motivators for self-backhauling in 5G are low cost and ease-of-deployment.

Network slicing: to address divergent services’ requirements in a flexible and cost efficient manner, services and operations can be realised on shared infrastructure to guarantee to each slice the adequate resources without impairing other slices.




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3.1 Multi-Connectivity In order to fulfil the 5G vision of seamless connectivity with greatly improved performance in terms of throughput, reliability, and latency, while overcoming the challenges of mm-wave propagation, multi-connectivity will be a fundamental enabling concept. In LTE, the concept of dual connectivity (DC) was introduced in release 12, where a UE can be connected to two eNBs simultaneously. One of the eNBs is designated as master eNB (MeNB) and handles the control plane signalling. The other eNB is designated as secondary eNB (SeNB) and can provide additional radio resources to the UE. Each eNB handles its own scheduler which implies independent carrier frequencies in the two nodes, or tight coordination between the nodes if the same carrier frequency is used in both nodes. In case the transport delay between the eNBs is negligible, it is possible to achieve this tight coordination in a coordinated multi-point (CoMP) transmission. However, in most deployments, the transport between the nodes will be non-ideal requiring a more latency-tolerable scheme. For DC, the user plane (UP) traffic is split at the packet data convergence protocol (PDCP) layer in the MeNB and forwarded to the lower layers (RLC/MAC/PHY) in the MeNB and/or SeNB. The concept of RRC diversity was also introduced [3GPP TR 36.842], where control signals can be forwarded across either the MeNB or the SeNB. The mm-wave system is expected to extend the concept of dual connectivity by: allowing more than two nodes to be connected; enabling a new bearer type where the traffic is terminated in the SeNB and split into the MeNB and/or the SeNB; enabling tight interworking between LTE and NR. Notice that tight-interworking with LTE can always be performed, independently on whether the user is connected to both RATS (LTE and NR) or only to one. The results for the tight interworking evaluations are reported under the multi-connectivity section, given that the result is a combination of both users with dual-connectivity and single connectivity. The concept of RRC diversity will be further considered in regard to the new latency opportunities with new NR numerologies and the more challenging propagation conditions at higher frequencies.

3.1.1 Impact of new functional blocks on generic architecture

The major changes of LTE-NR interworking from LTE DC are (apart from it being a DC between two different RATs) the introduction of new split bearers. Besides the introduction of a new bearer types 3GPP has agreed to have RRC termination also at the SeNB for the case of DC between LTE and NR. This gives the possibility to have the RRC messages handled directly in the SeNB without MeNB knowledge [3GPP TR 38.804]. In RRC diversity, RRC messages are then routed via different paths, e.g. in LTE Rel.15 with an MCGSRB split bearer, thus RRC messages could be sent directly via the MeNB radio or/and copied and routed via the Xn interface between the MeNB and SeNB and then via the SeNB radio. There is also a new AS sublayer added (applicable for connections to NextGen Core) for the user plane which handles QoS flow and DRB mappings.

In user plane LTE-NR interworking the new bearer types are:

Split bearer via MCG as illustrated in Figure 3-1

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Split bearer via SCG as illustrated in Figure 3-3, where the split occurs in the secondary node.

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In control plane LTE-NR interworking the new bearer types are

Split bearer for RRC

Direct RRC bearer from the SN (known as SCG SRB or direct SRB) In Figure 3-4, the bearer types for Control Plane (CP) architecture in LTE-NR tight interworking are shown.

Figure 3-4 Split bearer illustration for control plane in 5G

3.1.2 LTE-NR tight interworking system evaluations

In order to fulfil the stringent 5G requirements, NR is developed to be operational also at higher frequencies (e.g. mm-wave frequencies) up to 100 GHz where significant bandwidth resources are available. Although the path loss at higher frequencies can be mitigated through the use of narrowed directional beams, this may aggravate the poor coverage in case the directions of the beams are uncertain. NR will anyway support standalone operations, where sufficient mm-wave




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coverage can be provided, but tight interworking between LTE and NR will allow gradual deployment of the mm-wave RAT and leverage on the incumbent LTE infrastructure. For instance, users with good coverage to both LTE and NR can aggregate carriers from both RATs to obtain even higher throughputs (dual-connectivity), while edge users requiring large resources to obtain a modicum of performance can beneficially be served by LTE (singe-connectivity). To allow this dynamic RAT selection and utilization, a harmonized set of procedures in LTE and NR (e.g. addition/removal of SCG cell using signal quality measurements) will allow an optimized selection and smooth transition between them.

3.1.2.1 Evaluation of realistic dense urban deployment of LTE and NR

To evaluate the impact of utilizing mm-wave access, a system evaluation was performed in a dense urban deployment consisting of 1442 buildings with heights varying between 16 and 148 m distributed in a 2x2 km2 area, with the taller building located in the centre, as shown in Figure 3-5. The evaluations compare legacy LTE at 2.6 GHz with 40 MHz bandwidth to standalone NR at 15 GHz with 100 MHz bandwidth, as well as non-standalone deployments with co-located LTE and NR. The traffic is served by a macro network with an inner and an outer layer. The inner macro layer contains 7 sites deployed on the building rooftops with 200 m inter-site distance and the outer macro layer contains 28 sites deployed at 30 m height with 400 m inter-site distance. The traffic is generated by deploying 10000 users distributed uniformly randomly in the area with 80% of the traffic generated inside the buildings. Each user downloads a single packet with a size depending on the traffic load.

Figure 3-5 Network layout for LTE-NR tight interworking simulations [ATS+15]

To avoid boundary issues from users located near the edge of the simulation area, the evaluation only considered the performance of users located within the inner macro layer (i.e. the central 1x1 km2). The users outside this area still generate traffic, but are only considered as a source of interference. The end-user performance of the evaluated systems is illustrated in Figure 3-6.

Figure 3-6 Simulation results for LTE at 2.6 GHz standalone, NR at 15 GHz standalone, and non-standalone deployment with LTE at 2.6 GHz and NR at 15 GHz. (a) 5th percentile; (b) median; (c)

95th percentile user throughput

As the Figure 3-6 shows, LTE standalone cannot cope with the traffic load and cannot provide 50 Mbps for the 5th percentile users, even at very low loads. For NR standalone, the throughput




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even for the 5th percentile is increased thanks to the wider bandwidths. However, when employing both LTE and NR, the throughput is increased for all users to more than the sum of the two systems. An explanation for this synergy effect is that in the standalone deployment, all UEs compete for the same resources while for the non-standalone deployment, the UEs can be served by whichever system gives the best performance. For instance, for a UE in poor coverage, i.e. a low signal to noise ratio (SNR), require much more radio resource to transfer the same amount of data as a UE in good coverage as a more robust modulation-coding scheme (MCS) need to be used. Consequently, since the UE in poor coverage require more resources, these resources cannot be used by other UEs whose throughputs are reduced. In addition, as the UEs with poor coverage have reduced throughput, they also take longer time to finish their data transfer meanwhile generating interference for the other UEs. This will reduce the overall performance as the signal to interference plus noise ratio (SINR) reduces. When both LTE and NR are utilized, the UEs with good coverage to both systems get the benefit of receiving the full capacity of both RATs while the UEs with poor NR coverage are only served by LTE. These edge users in turn also benefit from NR as a significant amount of traffic is offloaded from LTE to NR, thus reducing the traffic load as well as the interference.

3.1.2.2 Dynamic evaluations

To evaluate dynamic features of LTE-NR interworking, a system level evaluation, modelling the full protocol stack, is used. Since each data packet is modelled independently, allowing e.g. user mobility and dynamic scheduling, a statistical propagation model is used in lieu of ray tracing in a realistic deployment. The scenario evaluated is the 3GPP dense urban scenario [TS 38.913] with three-sector hexagonal deployments using three sites and wraparound, using standalone LTE Macro at 3.5 GHz or standalone NR Macro at 26 GHz, or co-deployed non-standalone LTE and NR Macro at 3.5 and 26 GHz respectively. The traffic model used is Simple File Transfer Protocol (SFTP) with UDP as transport layer protocol. User lifetime is fixed to 10 seconds during which users will keep downloading files of 1 MB size. The user distribution is 80% indoor, where the users move at 3 km/h and 20% outdoor, where the users move at 30 km/h. According to the 3GPP scenarios, the bandwidth for LTE and NR are 200 MHz and 1 GHz respectively. As this bandwidth is prohibitively large to allow system level evaluations at high loads, the bandwidth is scaled down while maintaining constant spectral efficiency (i.e. 20 and 100 MHz bandwidth for LTE and NR respectively with 10 dB reduced transmit power). The obtained throughput can then assumedly be scaled linearly. Further simulation details can be found in annex 8.3.

Figure 3-7 Simulation results for LTE at 3.5 GHz standalone, NR at 26 GHz standalone, and non-

standalone deployment with LTE at 3.5 GHz and NR at 26 GHz. Average throughput plotted against Active users per cell (cell area 0.0115 km2)




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Figure 3-7 shows the simulation results comparing the average user throughput for the different deployments. The non-standalone dual connectivity deployment gives a noticeable gain in throughput compared to the standalone deployments. This is especially true at higher loads where the gain reaches even higher than the sum of the two standalone systems, this as a result of the synergy effect explained in 3.1.2.1. Similar trends as in 3.1.2.1 can also be seen for the worst users shown in Figure 3-8.

Figure 3-8 10th percentile user throughput plotted against active users per cell (cell area 0.0115

km2)

The 10th percentile users in the NR standalone system performs below the 50 Mbps target at all loads despite the wide bandwidth available. This is a consequence of bad coverage for the high frequencies, e.g. high penetration loss for indoor users, and thus poor utilization of the resources. Users in the LTE standalone case achieve more than 50 Mbps but only at low loads. Again, the DC case fares better, where the 10th percentile users reach above the 50 MB target up to high load points. The coverage issues for high frequencies can also be seen in Figure 3-9, which shows the transmissions and retransmissions in the two networks for different deployments. The LTE network with standalone and non-standalone deployment experiences a similar number of retransmissions, with a slightly lower number of initial transmissions in the dual connectivity case because of offloading to the secondary network. In the NR network, the initial number of transmissions shows similar behaviour as in the LTE network but the retransmissions differ greatly between the standalone and non-standalone case. In the standalone deployment, the number of retransmissions reaches very high values while the non-standalone system succeeds in keeping them low. This is because of the diversity possibility by limiting the Dual Connectivity operation to the users with acceptable NR coverage, thus avoiding wasting NR resources on users not able to efficiently use them and at the same time lower the interference and free more resources on the LTE network for the poor performing NR users.




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Figure 3-9 Comparisons of average number of downlink transmissions and retransmissions for the standalone and non-standalone deployments (a) DC LTE-NR and LTE only; (b) DC LTE-NR

and NR only

3.1.3 RRC diversity

3.1.3.1 Basic description

Multi-connectivity on the CP side can be beneficial in many use cases, for instance in connection robustness. More specifically, having multiple RRC connections or transmission paths leads to less connection failures and, as a result, to more reliable RRC transmissions. Also, service continuity can be assured through different CP MC options, to provide seamless mobility. The RRC signalling in LTE Rel.12/13 can be realized only via the MeNB. In this version of RRC diversity, there is still only one RRC entity, which is located in the MeNB. A graphical comparison of RRC signalling only from MeNB and RRC diversity for Rel.12/13 is presented in Figure 3-10.

Figure 3-10 Architectural alternatives for RRC messaging. Rel-12/13 (left): RRC messaging only via MeNB and RRC Diversity (right): RRC messages can be sent either via MeNB or SeNB. Solid

line – control plane connection, dashed line X2/Xn interface

The key architectural extension in this case of RRC diversity consists of allowing the UE to establish an RRC connection via SeNB i.e., while adding a SeNB, the RRC Connection Reconfiguration ensures that further RRC messaging can be delivered either over MeNB or SeNB.

3.1.3.2 Specific benefits

The RRC diversity can improve delivering measurement reports on time to handle dynamics of SeNB set configuration. Such improvement facilitates taking more effective handover decisions (based on more up to date measurements). In case of mm-wave SeNB the short TTI provides the potential to further reduce the latency due to more scheduling opportunities in given time period when compared to RATs with longer TTI.




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3.1.3.3 Analysis, simulation, performance

The focus of our research is on evaluating the RRC diversity concept based on simulation results. Instead of full set of RRC messages, only periodic RRC measurement reports were considered. This implies evaluating only the uplink RRC diversity case. In the simulations, only the DC Rel.12/13 case was evaluated, i.e. UL RRC messages received at SeNB are forwarded to MeNB (where RRC entity resides) over the Xn interface. Simulations for evaluation of the RRC diversity technique were performed in a HetNet environment composed of a macro LTE MeNBs and a small cells layer based on 5G SeNBs. The simulation assumptions for these studies can be found in Annex 0. The first metric investigated by simulation, is the rate of RRC packets that are received quicker from the SeNB path, as compared to the MeNB path (Figure 3-11). The rate is expressed as a percentage of the RRC packets that were received quicker in SeNB at RLC layer level over all received RRC packets in the whole simulation. Messages that were received only by SeNB and were not received by MeNB are also counted. The results of this analysis are presented in Figure 3-11.

Figure 3-11 Rate of RRC measurement reports received faster on the SeNB link

Chances for faster delivery of measurement reports via SeNB using RRC diversity are strongly

dependent on Xn delay value. The consistent gain of ~25% is observed for Xn delays equal or

lower than 2 ms. The upper bound for the cases when RRC messages are delivered faster via

SeNB is ~74% for the ideal case of Xn delay = 0 ms.

The on-time delivery of measurements in RRC messages can impact the packet drop ratio

observed in the network. However, in the evaluated simulation scenario, RRC diversity does

not bring gain in packet drop ratio, as in single transmission path almost all the messages are

received (the delivery percentage without diversity is 99.9977%, whereas with RRC diversity is

99.9457%).

Even though packet delivery ratio is not differentiated when using or not RRC diversity, the

studied scheme (RRC Diversity) can help delivering RRC messages from UE to MeNB faster.

A reduction of latency for worst case users (97.5th percentile in terms of packet latency) is shown

– in case of having Xn with a delay of 2ms, it is possible to reduce end-to-end (E2E) latency by

up to 300% and with 5ms Xn delay still, the latency can be reduced by around 200% (Figure

73,64%

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3-12). Therefore, RRC diversity can be useful in low-latency-centric UCs, as it can significantly

reduce the E2E latency.

With the introduction of RRC termination in SeNB and Direct SRB, now standardized in 3GPP, some of the RRC messages sent to SeNB could be handled directly in the SeNB and thus not needed to be forwarded to the MeNB, essentially giving the same performance gain as the 0 Xn delay in the performed evaluations.

Figure 3-12 Packet latency for users with bad propagation conditions

3.1.4 Performance and architecture evaluations for ultra-reliable and low latency systems

Due to significant path loss and large blocking probability over mmWave frequency bands, it is generally very challenging to support use cases in a cellular network that require ultra-low latency and high reliability, such as tactile internet, video augmented robotic control and remote-robot manipulation surgery. In this subsection, we evaluate the impact of different multi-connectivity options on the achievable system coverage within 200m ISD in hexagonal grids at 28 GHz. We assume that in case very low outage probability is achieved re-transmission is most likely not required, and low latency requirement can be reached by ultra-light frame design alone.

To begin with, we assume each UE can connect to multiple nodes using only LOS connections. With proper signaling procedures, UE is assumed to have acquired knowledge on the nearest BS(s). Then two types of multi-connectivity nodes are considered. First, UEs connect to the closest multiple BS(s) at the same frequency band (multi-connectivity approach #1). Second, UEs connect to the same BS but over non-overlapping neighboring frequency bands (multi-connectivity approach #2). Here non-overlapping bands refer to the case that guard intervals are inserted among different frequency bands to minimize inter-band interferences. This is generally a carrier aggregation approach but can support data split on PDCP level, which maps the used different frequency bands to be multiple virtual nodes. In case, e.g., one stream at one frequency band is lost, the other stream(s) can still be processed independently. In order to achieve full diversity over multi-connectivity, a root- Low-Density Parity-Check (LDPC) code based joint coding scheme [SSF16] is applied across different nodes. In effect, outage probability drops significantly and reads

𝑃𝑜𝑢𝑡 = ∏ 𝑃𝑙𝐿𝑙=1 (3.1)

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where 𝑃𝑙 refers to the outage probability of connection link l and L is the number of connections. As described in [ALS+14], the outage probability of the single link is a function of UE distance d (in meters) is denoted as

𝑃𝑙(𝑑) = max(0,1 − 𝑒−𝑎𝑜𝑢𝑡𝑑+𝑏𝑜𝑢𝑡) (3.2) Assuming isotropic channel model and transmit power of 30 dBm, example values are given in

[ALS+14] for system operating at 28 GHz, i.e., 𝑎𝑜𝑢𝑡 = 30𝑚 and 𝑏𝑜𝑢𝑡 = 5.2. The resulting outage probability map in single-connectivity mode is shown in Figure 3-13.

Based on the above-mentioned formulas and parameter settings, the achievable coverage by deploying single-connectivity, multi-connectivity approach #1 and multi-connectivity approach #2 is examined within 200 m ISD in a hexagonal grid at 28 GHz as in Figure 3-13 and Figure 3-14. Here single-connectivity is defined as the case each UE connects to one node and one frequency band. Clearly, the network coverage can be improved a lot by deploying multi-connectivity. With given hexagonal grid network structure, the multi-connectivity approach #2 is, shown in Figure 3-14, to be more efficient than the multi-connectivity approach #1. Indeed, communication over mmWave frequency suffers from severe path loss and blockages and the distance between one serving BS and one UE has very pronounced impact on the achievable outage probability. In this context, it is more sensible to connect the serving UE to the nearest BS over different non-overlapping neighbouring frequency bands for forming multi-connectivity links. More advanced or specific network architecture, other than hexagonal grid network, needs to be developed if the UEs choose to connect multiple nearest BSs for multi-connectivity as in approach #1.

Figure 3-13 The outage probability map in single-connectivity mode in mmWave network with

ISD equal to 200 meters

Figure 3-14 The outage probability map in dual-connectivity mode in mmWave network with ISD equal to 200 meters: (a) Multi-connectivity approach #1; (b) Multi-connectivity approach #2




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Figure 3-15 The outage probability in the area between two nodes (located: x = 0 m, y = 0 m and x = 500 m, y = 0 m) in mmWave network with ISD equal to 200 meters: (a) Multi-connectivity

approach #1; (b) Multi-connectivity approach #2

3.1.5 Multi-band system integration

3.1.5.1 System model

The multi-band system integration (MBSI) is a multi-RAT PHY layer system design which addresses joint multi-/single RAT system activation following dedicated KPIs combined with scalable spectrum management. Joint LA and spectrum metrics positively undertake the PHY layer system design and RF front end scalability in order to limit power transmissions whilst guaranteeing target QoS and radio coverage. MBSI selects the appropriate RF bands in accordance with PHY system parameters accuracy, frequency and power regulations. PHY layer parameters intended to cope with the multipath propagation channel may be similar in contiguous and non-contiguous RF bands if we select properly antenna diagrams and scalable RF front-end architectures. For example, the cyclic prefix size shall be similar in both V and E bands, depending on antenna diagrams and radio coverage. In that case, a single interface may operate in separate RF bands. Therefore, the multi-band system integration is conditioned by path-loss signature and frequency/power regulations playing a role on the radio coverage extensions. Furthermore, several RF bands may be simultaneously used in parallel in order to increase throughput and bring diversity in the transmission.

The MBSI, conceived in mmMAGIC, is about carrier aggregation combining several RATs and scalable licensed and unlicensed band allocation turned towards V and E bands for multi-gigabit transmissions. The multi-band system integration is interconnected to the multi-RAN/RAT management architecture (section 2) for integration issues.

Single RAT based MBSI considers a single interface enabling to operate over several RF bands. The proposed system is foreseen as an extension of a dynamic channel allocation [SUM11] applied to non-contiguous RF bands, as well as 3GPP LTE including LWA and LAA (rel. 13) used for the LTE aggregation of unlicensed 5 GHz and licensed 2 GHz spectrum.

In the MiWEBA project, IEEE802.11 ad standard has been evaluated for small cell deployment. The MBSI combines V (57 - 66 GHz) and E (71 -76 & 81 - 86 GHz) bands together to perform multi-gigabit transmissions. transmissions.The objective here is to extend such deployments in V band and E band by considering the IEEE802.11 ad/ay standard Applications to mm-wave

Focusing on V to E band transposition for mm-wave applications, the oxygen absorption and hydrometeor attenuation affecting V-bands transmissions may be reduced in E-bands, improving link budget assessments as illustrated on the Figure 3-16. The relative attenuation of oxygen absorption and rain fall rates are given in V and E bands in [ETSI WP2+15].




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Figure 3-16 Relative attenuation due to Oxygen and hydrometeors, source [ETSI WP2+15]

As illustrated on Figure 3-17, the IEEE802.11 ad/ay system considering a 2160 MHz transmission channel size usually operating in the V-band, may be transposed in E-bands depending on frequency and power regulations and propagation conditions in the interest deployment zones.

Figure 3-17 V and E band frequency plane with a common transmission channel size

Four transmission channels dedicated to V-bands (channels #Ai) may be also designed in the E-band in accordance with European Electronic Communications Committee (ECC) decisions reported in [ETSI WP2+15]. Later on, in 2009, the ECC recognized the high importance of E band, which was foreseen for the expected high density deployment in 3G/4G mobile backhauling. Therefore, specific channel arrangements for channel sizes ranging from 250 to 4750 MHz were proposed, permitting TDD and FDD applications with 10 GHz as well as 2.5 GHz duplex separation.

Ch #B1 Ch #B2 Ch #B3 Ch #B4

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Advantages in using E bands instead of V-bands remain in the oxygen absorption limitations and higher Emitted Isotropic Radiated Power (EIRP) levels that are allowed in the E band to transmit data (CEPT EIRP levels up to 85 dBm in E-band and 55 dBm in V-band). Then, the E band use compensates the additional RF attenuation when moving from V to E-band which is involved by path-loss propagation (20Log(fc), where fc is the RF carrier frequency). The relationships between the transmit power at the input of the transmitter antenna (Pout), transmitter antenna gain and EIRP for both V and E bands as specified in ETSI [ETSI_WP1+15] are illustrated in Figure 3-18. The left figure depicts power regulations of V-bands in all regions and the right figure supplies power regulations of E-band in Europe. It shows that, in Europe, in V-band, the maximum EIRP is set to 55 dBm with an antenna gain set to 50 dB. In E-band, with a similar antenna gain, the EIRP is set to 85 dBm. Antenna gains are then potentially higher than 30 dBi by choosing E-bands.

Figure 3-18 V and E bands power regulations [ETSI_WP1+15]

The multi-band system integration (MBSI) proposed in V and E bands may be applied to other RF bands below 6 GHz with low band integration below 6 GHz. The MBSI extends a single PHY layer operation to several RF channels in order to limit interference, optimize power efficiency and radio coverage in accordance with link budget parameters and proper KPIs and link adaptation metrics (see section 4.1).

Multi-RAT MBSI: an alternative to this RF band extension using a single interface is also to address a multi-RF PHY layer system by enabling multi-RF channel management upon several air interfaces and having flexible PHY/MAC parameters as the sub-carrier spacing, transmission channel and guard interval sizes on the basis of elementary components. As an example, OFDM sub-carrier spacing may be adjusted to RF channel management by aggregating several elementary sub-carrier spacing in the OFDM design in order to cope with phase noise when switching between several RF bands. Flexible guard time interval adjustment for flexible radio coverage requirements may be adjusted. Multi-RF PHY layer combines baseband adaptation into several PHY parameter subsets with RF channel assignment. To design such scalable system, KPIs related to the system concept may include the trade-off between the radio coverage and the throughput, cost and power efficiency metrics, in accordance with power regulations, considering the green link budget metric [SUM16] in the multi-band system decision engine.

Focusing on mm-wave bands IEEE802.11 ad and other UWB standards, such as the ECMA-368 [ECMA 368] initially dedicated to 3.1-10.6 GHz bands may be transposed in these bands in order to introduce frequency diversity and improve radio coverage following power regulations. The extension in V and E bands permits higher transmit power level and outdoor deployments for both standards.

V bands E bands ETSI




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3.2 Cell clustering During recent standardization efforts in 3GPP, it has been agreed that NR will support two different levels of mobility, namely RRC driven and mobility without RRC involvement [3GPP TR 38.804]. For the RRC driven mobility, the handover procedure will be similar to LTE, where the UE reports measurement on serving and neighbour cells when the measurements fulfil certain criteria e.g. neighbour cells is offset better than serving cell. The network can then reconfigure RRC in the UE to perform a handover to a target cell. For the mobility without RRC involvement, the measurements are handled at PHY or MAC layer based e.g. on channel-state information reference signals (CSI-RS) or channel quality indicators (CQI). The UE will then be configured to select a specific beam, which may come from an AP different from the serving AP. 3GPP has recently agreed to standardize a protocol split with a centralized PDCP and distributed RLC/MAC/PHY [3GPP R3-171305], it would be possible to maintain a single PDCP entity in one gNB while switching the RLC/MAC and PHY from one transmission/reception point (TRxP) to another. This protocol split also allows for arranging UE-specific base station clusters [MMMAG16-D31]. The cluster will consist of at least two APs, where one of the APs is designated as cluster head (CH). The CH terminates the control plane and an interface to the CN, and can configure which other APs should be included in the cluster. All cells in the cluster transmit reference signals (e.g. CSI-RS) in beams for the UE to measure on. The UE then reports the quality of the measured beams, either directly to the CH, or via another AP responsible of forwarding the measurement reports to the CH. Based on the measurement reports, the CH can switch between beams from different AP, only relying on PHY layer beam management and beam refinement procedures.

Since the availability and configuration of the base station cluster is transparent to the UE, the UE will not be aware of which AP is the CH, or which neighbouring cells are included in the cluster and which are outside the cluster. Instead, the UE will measure on all the beams it detects of the configured measurement objects. The network can then decide whether multiple APs should be prepared to serve the UE, based on transport capacity between the nodes, as well as processing and storage capacity of the target node. The message flow required to extend the cluster set is presented in Figure 3-19.

Figure 3-19 Addition of cells served by secondary APs to the cluster set

3.3 RRC_Inactive

3.3.1 Consideration of mobility state transition

Another fundamental concept that can affect the architecture for mm-wave RAN integration, is the consideration of mobility state transition in 5G systems. As the next generation mobile radio

X2*-AP: Addition Request (UL GTP Tunnel Endpoint)

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is expected to comprise mixed APs; to support greater user mobility and deployment, it has now become imperative to review LTE’s existing state handling mechanism. To briefly summarize, the RRC_IDLE was optimized to minimize UE power consumption, network resource usage and memory consumption; whilst, the RRC_CONNECTED was introduced for full active data transmission. More recently, to cope with the diverse 5G system requirements and to support new use cases, a new state has been introduced [3GPP TR 38.804]. Although the purpose, basic features and its benefits has already been discussed in the project’s previous publications [WP_MMMAG17], [MMMAG16-D31]; some of its conceptual evaluation in terms of reducing the CP latency is presented here.

3.3.1.1 Features of RRC_INACTIVE

A quick recap on some of the characteristic features of the new state [WP_MMMAG16]: The UP and CP connections between the RAN and CN are kept active;

The UE AS context is stored in at least one (e.g., the last serving) gNB and the UE;

Mobility is based on the UE measurements and cell reselection procedure, with

configuration received from the network;

Paging is initiated by the NR RAN;

RAN based notification area for the UE is known and managed by NR RAN

RRC_CONNECTED

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Figure 3-20 EMM State transition diagram for 5G systems

3.3.2 Fulfilling the CP latency requirements

It can be stated that retaining the RAN and CN connections will undoubtedly help in reducing the overall signalling delay for state transition, but in addition can minimize the number of steps and messages involved in fulfilling the CP latency requirement of 10 ms in 5G systems [3GPP TS 36.401]. Adopting LTE’s latency calculations as a basis [3GPP TR 36.912], the CP latency evaluations can be done for 5G systems. The assumption here is made on the basis that UE context is stored in the localized RAN in the new state, which includes the storage of NAS and AS security context information, with the user mobility and reachability functions taken care. Thus, in such instances the UE connection can be easily established, for example, by the Resume procedure [3GPP TR 36.912], which in-turn, can reduce the overall CP signalling delay/latency. Additionally, assuming that there will be shortening of 5G sub-frames and faster UE and node processing capabilities, there will be further reduction in the overhead time required for state transition. Figure 3-21 shows the signal flow diagram in the new state for next generation radio without considering the context fetch procedure.




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UE gNB MME

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cc

Figure 3-21 Example signal flow diagram for transition from RRC Inactive to RRC Connected

(without context fetch)

By analysing the delays associated to the state transition in Figure 3-21, the CP latency will be 7.125, 9, and 12.75 ms for the TTI lengths 0.25, 0.5, and 1 ms respectively. In addition, the analysis is based on the following assumptions:

The delay in receiving the random access response (RAR) after sending preamble in 3 sub-frames (3TTIs);

PRACH (Physical Random Access Channels) cycle takes place in one sub-frame (1TTI); In addition, it is assumed that the next generation eNB, or more precisely gNB, will have

greater processing capabilities — consequently, requiring an increase in the processing speed of the UE to match the fast processing pace of the gNB.

Further details can be found in Annex 8.1 When considering dense heterogeneous deployments, comprising several mm-wave APs, there will be frequent handovers (HO) to maintain good QoS requirements. In such conditions, it becomes imperative to conceptually analyze the signalling latency incurred when the UE moves/changes gNB. In such cases, the Xn transfer latency and the processing delay for retrieving the UE context must be taken into account, which can vary depending upon its deployment. Figure 3-22 shows the signal flow diagram in the new state with the inclusion of context fetch procedure.




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Figure 3-22 Signal flow diagram for transition from RRC Inactive to RRC Connected (with

context fetch)

By analysing the delays involved, it is clear that there is an overall increase in the transition time, although they are still smaller if compared to the existing telecommunication systems. Using the same assumptions as for the state transition without context fetch, the transition delay will be 28.125, 30, and 33.75 ms for 0.25, 0.5, and 1 ms TTIs lengths respectively, if the context fetch delay is 10 ms, whereas the delays increase to 78.125, 80, and 83.75 ms for 0.25, 0.5, and 1 ms TTIs lengths respectively if the context fetch delay is 50 ms. It can be remarked that the stringent CP latency of 10 ms can be achieved, if the UE resumes its connection with the current serving eNB. However, additional delay of up to 70 ms may be encountered because of the context fetch procedure, depending upon the Xn backhaul deployment.

3.3.2.1 Connection resume for RAN area update in RRC_INACTIVE

The main application for the new RRC state will be to reduce the latency when resuming the connection, especially when the UE needs to transmit or receive data. As mentioned in previous section, the UE will be allowed to move around within a finite area, defined by the RAN, without informing the network of its movement and still capable of resuming the connection. This is similar to the UE behaviour in RRC_IDLE, where a UE can move around within a Tracking Area configured by the CN, without notifying the network. The difference between RRC_IDLE and




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RRC_INACTIVE is that for the latter case, there must be a provision to use the previously set connection and configuration.

During the resume procedure, the UE will have to provide the network with an identifier, a security token, and possibly a resume cause. The identifier is used to locate and retrieve the stored UE context, whereas the security token e.g. a Message Authentication Code – Integrity (MAC-I), or a truncated version, is used to authenticate the UE identity.

It is worth to note that the resume procedure enables the network to treat different resume requests differently. For instance, if the UE moves outside its configured RAN area, it needs to notify the network, to be able to receive an updated RAN area information. However, if the UE does not have any UL or DL data, it would be beneficial to return as soon as possible to RRC_INACTIVE to reduce the UE’s battery consumption.

On the other hand, if the UE has UL data to transmit, it should enter RRC_CONNECTED as soon as possible to be able to transmit the data. However, if the UE is responding to a paging instant, the network may prioritize these UEs over other UEs that only have UL data for transmission.

The UE will be able to send this resume information to any cell, both inside the RAN area; when the UE resumes to transmit and/or receive data, as well as outside the RAN area; when the UE notifies the network about leaving the RAN area. As the target node is not capable of identifying the UE before receiving this message, the message will have to be sent unencrypted. To prevent replay attacks, neither the UE identity, nor the security token should ever be reused, and the UE should receive updated identifiers and security tokens every time it enters RRC_INACTIVE. However, in order for the UE to receive updated information, security will have to be activated. In LTE, to achieve this, the network provides the UE with a list of so called Next Hop parameters (NH) and corresponding Next Hop Chaining Count (NCC), which indicate which NH the UE should use to calculate the security key for the target eNB. Thus, when UE derives a new key, it is provided with a NCC — either the same NCC as was used before, or a single step increment to use a new NH in the derivation.

If no optimizations are made to the Resume procedure, the UE would not receive the NCC until Msg.4 (i.e. RRCConnectionResume), and would not be able to receive any encrypted information until the next round trip time (RTT). If the UE only performs a RAN area update to inform the network that it has left the previous configured RAN area, and the network wants to provide an updated RAN area and a new UE identifier, the UE will have to wait for Msg.6 to receive this information.

However, if the NCC is included in the message which transitions the UE from RRC_CONNECTED to RRC_INACTIVE (e.g. RRCConnectionSuspend), the UE would be able to calculate the new security key before transmitting the RRCConnectionResumeRequest to integrity protect the message, and the network could respond with an encrypted message e.g. a RRCConnectionSuspend message with an updated RAN area and a new UE identity as noticed in Figure 3-23.




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Figure 3-23 Optimized RAN area update

3.3.3 Assessing the best DRX scheme for RRC_INACTIVE

Retaining the RAN and CN connection will greatly reduce the number of frequent state transitions; however, cannot fulfil the requirement of power efficiency. As the RAN connection with the UE is temporarily suspended, there will be a necessity for the inception of some techniques to monitor the RAN paging instants [3GPP R2-167707]. This timely monitoring of the paging messages can be done by deploying Discontinuous reception (DRX) mechanism.

Packet Burst

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A session could be a file download, streamed video,

smartphone background update etc

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Figure 3-24 DRX for next generation systems




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As it is widely known, the connected mode DRX in the existing technology, first enters short DRX cycles followed by long DRX periods. Although the instances and transition periods are short, retaining the UE in RRC_CONNECTED for long duration when there is no data transmission is neither energy nor resource efficient, as there will be a requirement to dedicate resources for the UE. This implies a need for proper UE power saving mechanism for the new state. Additionally, due to the high power consumption by number of RF chains in the mm-wave system, the inception of proper DRX mechanism becomes mandatory.

As the UE is controlled by RAN in RRC_INACTIVE, there may be deployment scenarios involving fast moving users, where it may be more suitable to page the UE from the CN e.g. in case the UE lost connection with the localized RAN or in network failure conditions. As it has been agreed in 3GPP that a UE in RRC_INACTIVE should monitor both CN and RAN paging [3GPP TR 38.804], the configuration of the RRC_INACTIVE DRX can allow shorter periodicities to meet stringent performance requirements for supporting diverse applications as long as the RAN and CN paging occasions overlap as shown in Figure 3-24.

3.4 Self-Backhauling The new level of densification in 5G will require innovative approaches in radio resource, mobility, and/or interference management. A centralized operation of mobile networks, as implemented by C-RAN, allows obtaining a globalized view on mobility and interference management in order to optimize the resource usage [BDO+13]. Aiming at centralization of the mobile network operation, high capacity links among access points of small cells and the centralized base station of macro cell are required, which is usually satisfied by optical fibre connections. Nevertheless, it may be too expensive or impractical to equip every cell with fibre connectivity. As an attractive cost-efficient alternative, wireless backhauling enables direct, low latency connections among APs and BSs and, hence provide them with a possibility for enhanced cooperation to achieve better performance, in addition to providing high data rate throughput to small cells.

A further step of wireless backhauling is self-backhauling, which refers to a set of solutions to provide technology- and topology-dependent coverage extension and capacity expansion utilizing same frequency band for both backhaul and access links, as shown in Figure 3-25. Self-backhauling provides an efficient way to combat infrastructure constraints especially in dense network deployment, where access to fibre may be limited to only some APs. However, self-backhauling also brings challenges to the radio resource management (RRM) between BH and access links, which leads to joint backhaul and access RRM optimize system efficiency.

Figure 3-25 Concept of Self-backhauling

3.4.1 Access-integrated backhaul in fixed wireless access

The potential of using access-integrated backhaul in a fixed wireless access (FWA) use case is evaluated. FWA is considered as a potential use case for early 5G deployment. The system is deployed in the 28 GHz band to provide FWA to residential houses in a suburban scenario where the access points (APs) are deployed on utility poles. It is assumed that only a small fraction of the APs has dedicated backhaul transport (e.g. fiber) and the remainder of the APs are instead backhauled wirelessly by using access-integrated backhaul. The access-integrated

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backhaul shares the radio resources with the FWA and consumes a substantial part of the available resources especially when multi-hop backhaul is considered. The backhaul aggregation points quickly become the bottle necks since they consume a majority of the radio resources. However, when considering the amount of spectrum that is available at 28 GHz then serving up to 40 Mbps in aggregated constant DL+UL traffic to houses seems feasible with careful link planning. This is accomplished by having less than 20% of the APs connected with dedicated backhaul while the rest of the APs are using access-integrated backhaul.

The frequency bands targeted for future 5G deployments are typically much wider than the ones used today. Hundreds of MHz up to possibly thousands of MHz of bandwidth compared to today’s tens of MHz bandwidths are typically targeted by 5G radio access. 5G radio should be able to operate at similar frequencies as current radio systems, however, the very wide bandwidths are typically available at much higher frequencies than the ones used by current systems. The 5G physical layer must therefore be flexible also with respect to which frequency it may operate in, ranging from 1 GHz to 100 GHz. For example, on July 2016, the U.S. Federal Communications Commission (FCC) opened up 3.85 GHz of licensed spectrum for 5G use in the U.S. at the 28 GHz, 37 GHz and 39 GHz bands, and 7 GHz of unlicensed spectrum in the 60 GHz regime [FCC16]. Indeed, with very wide bandwidths it becomes interesting to study how feasible it is to include wireless backhaul transport in the same spectrum as wireless access.

Figure 3-26 illustrates a multi-hop FWA system with access-integrated backhaul functionality. The Access Points (APs) provide FWA to residential houses where each residential house is equipped with a Customer Premise Equipment (CPE). In this simple example, AP1 has a dedicated fiber backhaul connection while AP2, AP3 and AP4 have access-integrated backhaul connections. Thus, AP1 has to serve the CPEs connected directly to it and also serve the backhaul connection towards AP2. The backhaul connection between AP1 and AP2 often becomes the bottleneck in the system since it also needs to carry the aggregated backhaul traffic to/from AP3 and AP4. Such aggregated backhaul connections typically require wide bandwidth and good link quality to support the high data rate demand and high reliability. The amount of aggregated backhaul traffic that an AP can serve will set a limit on the number of multi-hops and overall number of CPEs the system can serve.

Figure 3-26 Illustration of a multi-hop FWA system with access-integrated backhaul. The APs

serve the CPEs located at the residential houses and provide backhaul to neighboring APs. AP1 has a dedicated fiber backhaul.

A FWA deployment scenario was simulated to illustrate how much of the overall radio resources the backhaul connections consume. The deployment scenario is shown in Figure 3-27 where building footprints, AP locations and trees are depicted. The APs have three sectors where each sector operates at 28 GHz and serves a varying number of CPEs and depending on the fiber availability possibly also backhaul connections to neighbouring APs. More details on the simulated deployment scenario and results are given in [HCJP17]. However, to understand the bottleneck in the network, the portion of backhaul and access traffic of the sectors with utilization of 50% or more is shown in Figure 3-28. Comparing backhaul and access traffic, most of the resources are utilized for backhaul traffic. In particular, sectors 10 and 22 are highly over-utilized




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mainly due to large path loss caused by obstructing trees in the backhaul link providing interconnection, which makes them “over-consume” the available resources. This observation stresses the importance of planning the backhaul links properly to avoid this kind of situation. This becomes even more critical if the backhaul links also carry aggregated backhaul traffic to/from multiple APs.

Figure 3-27 Suburban scenario deployment (buildings and trees are shown in yellow and green

respectively, APs are shown as red triangles)

Figure 3-28 Access and backhaul radio resource utilization portions for the sectors with

resource utilization higher than 50% for simulation

A maximum allowed resource utilization limit of 70% is assumed to ensure that the network performance is robust against dynamic load variations. As seen in Figure 3-29, when the aggregated DL+UL CPE traffic demand is 25 Mbps, all the sectors operate below the resource utilization limit and thus they can provide the traffic demand to all the CPEs in a robust way. However, for the 40 Mbps case, about 3% of the sectors are over-utilized meaning that they need more resources than is allowed or in some cases even possible to serve. No surprise, all over-utilized sectors serve lot of resource demanding backhaul traffic whereof the worst ones




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also have poor backhaul Signal-to-Noise Ratio (SNR) due to trees obstructing their propagation paths. This also stresses the importance of proper backhaul link planning.

Figure 3-29 Radio resource utilization for all the sectors for 25 Mbps and 40 Mbps aggregated

CPE traffic demand

To conclude, a 5G-based access-integrated backhaul concept for providing fixed wireless access services was evaluated in a sub-urban deployment scenario. Radio simulations at 28 GHz with up to 40 Mbps achieved in aggregated end-user throughput show the feasibility in case of careful deployment planning.

One advantage of using a multi-hop access-integrated backhaul network is that CAPEX cost is reduced due to fewer fiber-connected APs, which significantly reduces the need to deploy fiber. Another advantage is the use of common hardware that is both used by access and backhaul connections.

In principle, it is the number of APs and corresponding number of CPEs that are connected along a single multi-hop chain that sets the performance. In most cases, it is the backhaul aggregation link that becomes the bottleneck and consumes the radio resources so careful network and backhaul link planning is needed.

3.4.2 Joint optimization of access and backhaul

The joint optimization problem is mathematically decomposed into transmission link scheduling, transmission duration and power allocation governed by a set of constraints as follows.

Scheduling constraint: each link can be scheduled only once in each frame.

Half duplex constraint: BS/AP/UE can only either transmit or receive for a given time slot, instead of simultaneous transmission and reception.

Power constraint: Total transmission power of all simultaneously active links from the same Tx should not exceed the available transmission power of the Tx.

The scheduling and resource allocation algorithm is further proposed to exploit space division multiple access (SDMA) that allows non-conflicting links to be transmitted simultaneously. The proposed solution exploits self-backhauling within a unified BH/access optimization framework. Presently in IEEE 802.11ad [IEEE802.11ad+12] standardization, TDMA scheme has been adopted to allocate each time slot to a specific link, i. e., each time slot is exclusively occupied by one link. Consequently, TDMA limits the time resource allocated to all links and results in relatively low network throughput. Considering directional transmission at mm-wave band, we allow concurrent transmissions to exploit spatial multiplexing gain. Specifically, each time slot can be allocated to multiple flows, which increases network throughput eventually. In the following, we describe the framework assuming non-standalone deployment. However, this can be also applicable to a standalone network with internode coordination. See [LLX+17] for more details of the optimization problem and the proposed algorithm.




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Here, we assume BH and access links share the same air interface, and all network elements (including BS, APs and UEs) are equipped with directional steerable antennas and can direct their beams in specific directions. The BS processes transmission link scheduling and adjusts transmission duration and power on both BH and access links. Figure 3-30 shows an example of considered HetNet.

Figure 3-30 Illustration of a HetNet with mm-Wave wireless BH and access

Monte Carlo simulations are used to evaluate the efficiency of the proposed algorithm in enhancing user throughputs. For the evaluation, we consider a HetNet deployed under a single Manhattan Grid, where square blocks are surrounded by streets that are 200 meters long and 30 meters wide. One BS and four APs are located at the crossroads. 100 UEs are uniformly dropped in the streets. Channel model is consistent with [ALS+14].

Figure 3-31 Comparison of edge/average user throughputs for 100 users at carrier frequency of 28GHz and bandwidth of 1GHz




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Figure 3-32 Comparison of average user throughputs for different number of users at carrier

frequency of 28GHz and bandwidth of 1GHz

Figure 3-31 shows the simulation results of user throughput at carrier frequency of 28 GHz and bandwidth of 1 GHz. Here, the cell edge user throughput is defined as 5-th percentile point of the cumulative distribution function of user throughputs. Compared to the benchmark TDMA scheme, our proposed algorithm provides considerable improvement in both edge user throughput and average user throughput due to exploiting spatial multiplexing that allocates more time resources to each link in the network by allowing multiple links to transmit concurrently.

Figure 3-32 shows the simulation results of average user throughputs for different numbers of users in the network. On one hand, as expected, increasing the number of users reduces average user throughput due to limited bandwidth. However, enabling space dimension still achieves high user throughput in the case of 300 users, and provides significant improvement compared to the benchmark scheme. On the other hand, as user density increases, gain of proposed scheme to TDMA scheme also grows (604 percent, 614 percent and 623 percent by the proposed algorithm against TDMA for 100, 200 and 300 users, respectively). This is mainly because with the increasing number of users, allocable slots for each link in TDMA scheme are limited and become dominant factor in determining user throughputs, consequently user throughputs benefit more from the spatial multiplexing gain.

3.5 Network slicing The concept of Network Slicing has been previously introduced to core network features, where multiple independent instances of network functions can be instantiated on common hardware equipment to allow a flexible and resource-efficient utilization. In the mmMagic project the Network Slicing concept has only been analysed on a conceptual level as more detailed evaluations was deemed too complicated to realize during the project timeframe. As stated by NGMN [NGMN15], Network Slicing is envisioned to be extended as an end-to-end service including both the CN and the RAN where multiple services and business operations can be realized independently on a shared infrastructure (including shared processing, storage, transport, radio spectrum, and hardware platforms). Logical separation of different services and operations will allow for flexible and independent configuration of different slices enabling more cost- and energy-efficient asset utilization while maintaining network performance, stability, reliability, and security. Different slices can be configured and customized for specific use cases without impairing the operation of other slices in the same network. However, it is likely that some network will enable multiple parallel slices supporting the same use case, e.g. mobile broadband (MBB), whereas some network will provide a few slices that each support a multitude of services and use case, e.g. MBB, machine type communication (MTC), or vehicular




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communication (VTX). As such, it is important that the configuration and maintenance of the slices should add minimum overhead so as not to waste scarce radio resources. For instance, UEs may be provided with dedicated configurations to connect to a specific slice, instead of broadcasting the availability and configurations for all different slices in all cells. However, since different network slices are to be operated as independent networks, it is important to ensure slice protection to prevent shortage of shared resources, (e.g. common signalling resources). This could be achieved using slice-specific access class barring where the network configures UEs already associated to a specific slice with e.g. modified back-off timers.

To facilitate an optimized slice selection, a UE can provide the network with a configured slice ID, which is obtained after its initial attach as can be seen in Figure 3-33. Absent of a valid slice ID, the UE should access using default configurations and the network will further configure and redirect the UE to a proper slice.

gNB CN Node1 CN Node2UE

RRC Connection Setup

Selected Slice ID = x

NG1 Setup Request

NG1 Setup Response (List of supported Slice IDs)

NG1 Setup Request

NG1 Setup Response (List of supported Slice IDs)

Identify Slice policiesIdentify CN Node

supporting Slice ID

Initial UE Message (Slice ID x)

Identify Slice policiesIdentify CN Node

supporting Slice ID

Figure 3-33 Slice selection at initial attach when Slice ID is provided by the UE to RAN

3.6 Interference coordination The presence of significant inter-cell interference calls for the design of efficient coordination mechanisms between mm-wave nodes. In [MMMAG16-D31] details were given on a beam labelling technique aimed for identifying beams from neighbour cells, in such a way that victim users can provide interference details to the serving node and inter-cell coordination mechanisms can be devised between the nodes involved.

Additional justification is however required about the real need of such interference coordination mechanisms. Accurate assessment of actual interference in real scenarios can only be accomplished through ray-tracing techniques, and taking into account the detailed geometry of the environment including the presence of objects, foliage, trees, and any potential scatterer. However, by looking at simplified but representative mm-wave scenarios, it is possible to find conditions where such interference can be important and that could therefore justify the presence of coordination.

Let us assume that the user’s device does not have beamforming capabilities so as to consider a worst case scenario. Both serving and interfering nodes operate at the same carrier




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frequencies. The aim is to calculate the Signal to Interference (S/I) ratio within the empty room along the LOS between the device and the serving node, while the interfering node is changed both in position and orientation within the room so as to reproduce some variability of the interfering signal conditions. Annex 8.4 shows the desired and interference signal powers as obtained by the ray tracing tool, and Figure 3-34 illustrates the cumulative distribution functions of S/I are depicted for the three operating frequencies.

Figure 3-34 Cumulative distribution of S/I at 10 GHz, 30 GHz, and 70 GHz

As expected, S/I distributions have some dependency with frequency in the median (S/Imedian) and 10th-percentile (S/I10th) S/I values, as collected in Table 3-1 Median S/I, 10th-percentile S/I, difference between the two, and standard deviation σ as functions of the carrier frequency. More interestingly, the difference S/Imedian – S/I10th and the variance of S/I (σ2) is rather large, and mostly independent of frequency. This means that, even if S/I values actually increase with frequency, the standard deviation remains rather high. Moreover, such S/I variations are likely to be bursty and intermittent, hence quite unpredictable, and cannot be dealt with by means of any link adaptation scheme based on CSI feedback. As a result, performance can significantly change in an abrupt and unpredictable way unless receiver beamforming or some form of resources coordination technique is introduced (or both). An exact dependency of S/I with frequency would require accurate characterization of the geometry and furniture involved, but unless strongly absorptive materials are present, the standard deviation of S/I may not be neglected.




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Table 3-1 Median S/I, 10th-percentile S/I, difference between the two, and standard deviation σ as functions of the carrier frequency

Frequency (GHz)

S/Imedian (dB) S/I10th (dB) S/Imedian – S/I10th (dB)

σ (dB)

10 19 8 11 7.31

30 20 7 13 7.79

70 24 12 12 7.88

The conclusion from this analysis is that interference in scenarios with little isolation between the nodes, and when the receiver has very limited or no beamforming capabilities, can introduce large signal quality variations even at very high frequencies. Such quality variations cannot be overcome by traditional link adaptation techniques because of the longer time scales of the latter, whereas mm-wave interferences can basically change on a per-subframe basis. This issue demands some form of inter-node resource coordination, which at mm-waves can be based on beam coordination. Of course, the actual numbers of S/I strongly depend on the scenario, geometry, isolation between rooms, materials absorption, etc. but the conclusions are expected to remain in very dense scenarios.




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4 RAN and Transport functionalities for network integration

This chapter explores RAN and Transport functionalities, enabling network integration solutions, in order to support edge-less user experience in mm-wave bands. In particular, we study power and energy efficient link adaptation techniques and relevant KPIs for single versus Multi-RAT management. We investigate transport layer performance studies under outage-prone mm-wave RAN. Additionally, low frequency-assisted initial access procedures, as well as mobility-related and PHY improvement procedures are developed to enable mm-wave network integration.

4.1 Power and Energy efficiency KPIs Single RAT scenario

As reminded in [SUM16], spectrum efficiency (SE) link adaptation techniques adopted in 3GPP are focused on throughput maximization bound with received signal strength and Adaptive Modulation Coding (AMC) to select transmission modes (TM) in LTE-A based HetNets [KBH12] [SNN+13]. Figure 4-1 illustrates the SE based MCS selection processing: in LTE-A, the evolved node base station eNB decides upon the applicable MCS for DL transmissions, using CQI feedback reported by the UE to the eNB. The eNB forwards dedicated pilots to the UE in order to estimate the RSSI on the DL. The UE converts the RSSI into an Energy per bit to noise power spectral density ratio, Ec/N0, and determines a CQI index such that it corresponds to the highest MCS allowing the UE to decode the transport block with error rate probability not exceeding 10% [Pelcat,13] [Sal,13].

Figure 4-1 SNR-to-CQI mapping and SE based MCS decision for LTE, source [SUM16]

Nevertheless, power efficiency (PE) oriented TM selection has been analytically introduced for LTE-A in [SNN+13]. A relation between the SE, MCS and power requirements upon DL LTE-A transmissions has been developed as follows:

3

3

0

11

2 10

1 102 (1 )

2 (1 )

cbps

c cbps

N nc c

BLER R N LSE

EIRP gM R R

BLER L BN

(4.1)

6

E-Node BPilot signalsData signals

UE side

CSI measurement : 3GPP TS 36.211RSSI Estimation: 3GPP TS 36214

BER CQI conversion 4 bit CQI index3GPP TS 36.213

MCS choice using CQI index (4 bit-CQI) and BLER LUT

Ec/No (dB)DLPilots

4 bit CQI world

CQIindex Mod

Code ratex1024

-16.25 0000 0 no transmission

-14.75 0001 1 QPSK 78

-13.25 0010 2 QPSK 120

….. …… …

-7.25 0110 6 QPSK 602

-5.75 0111 7 16QAM 378

-4.25 1000 8 16QAM 490

-2.75 1001 9 16QAM 616

…..

0.25 1011 11 64QAM 567

1.75 1100 12 64QAM 666

6.25 1111 15 64QAM 948




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where Rc is the coding rate, Ncbps is the number of encoded bits per modulation constellation

point, i.e., per subcarrier, and L is the number of subcarriers per Resource Block (RB) in the

LTE-A sub-carrier assignment. Also, gn is the average channel gain on RB, N0 is the noise power spectral density, and B is the bandwidth per RB. EIRP stands for the equivalent isotropically radiated power. Equation (4-1) shows that, for a fixed BLER, the SE is linearly increasing with the number of information bits per sub-carrier given by Rc Ncbps.

The modulation order, M, linearly builds up with a transmit EIRP expansion and an equivalent SNR derived from the average channel gain, gn, in an RB.

Multi-RAT scenario

A link adaptation metric, denoted as Green Link Budget (GLB), has been selected to allow a power efficiency comparison of independent interfaces for 5G involving different multipath power sensitivity levels handled by RF carrier path-loss variations and transmission bandwidth sizes [SUM16]. The GLB metric is assessed on mmMAGIC scenarios (see section 5.2) and on the multi-band system integration (see section 3.1.5). operating in parallel and extended RF bands. The metric refers to link budget analysis in order to address the coverage-power requirement trade-off. A normalization of link budget calculations is then performed to evaluate relative gains in the power efficient selection procedure of deployed technologies.

The GLB metric (Figure 4-2) is formed with the aid of two sub-metrics derived from link budget considerations. The α-metric performs an evaluation of the relative degradations involved by multipath propagation impacting, thus, the link level performance and the radiated power requirements in order to achieve an adequate throughput, given an error rate threshold, typically a Bit Error Rate (BER) of 10-5. On the other hand, the β-metric performs dynamic power control by adjusting radiated power levels to the power requirements of the transmission modes recommended by the α-metric. The α-metric is defined as the sum of the Multipath Channel Margin (MCM), which evaluates the relative multipath degradations at the link level stage of a given transmission mode and the Path-Loss Margin (PLM), which refers to the additional path-loss created by obstructions for the considered radio link. More specifically:

( , ) ( , )

M AWGN

MFS FS id

MCM S S

PLM PL d fc PL d fc RSSI RSSI

MCM PLM

(4.2)

where SM is the multipath power sensitivity in a multipath context translating the required power level to transmit data with a typical BER and data rate D, SAWGN is the power sensitivity under AWGN channel related to the same transmission mode and data rate. PLMFS(d,fc) and PLFS(d,fc) represent the multipath and free space path-loss models respectively. The PLM may be also deduced by subtracting the RSSI expressed in dBm from a perfect RSSI indicator deduced from a free space path-loss model.




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Figure 4-2 Definition of the α-metric

The β-metric is defined as the deviation between the received signal power and the required power for the TM initially selected by the α-metric. Hence, power management is performed by exploiting the α-metric to adjust and limit the EIRP at the small cell or the macro-cell. The EIRP is adjusted to obtain β-metric values between 3 dB and 15 dB depending on obstruction levels.

( , ) ( , )

( , )

MFS M R MFS M

R AWGN FS

ARP d fc S EIRP G PL d fc S

EIRP G S PL d fc

(4.3)

The available received power at the UE, denoted as ARPMFS (d,fc) is assimilated to the (RSSI) expressed in dBm upon multipath and GR denotes the gain of the receiving antenna. It is deduced from either direct power measurements or path-loss models.

4.2 Transport layer performance studies under outage-prone mm-wave RAN

4.2.1 Optimising transport via FEC

Mm-wave radio propagation behaviour resembles optical signals with low diffractions and increasingly relies upon LOS or strong reflections from surrounding environment achievable via narrow beamforming rather than diffuse components. As a result, mm-wave signals are more outage-prone compared to low-frequency carriers; blockage can be induced by trees, street furniture, transport traffic and even human body. Signal blockage (in either control or data channel) may lead to an abrupt reduction in link quality or to radio link failures (RLFs) with drastic impacts on transport layer control protocols (e.g., TCP) resulting in degraded quality of experience (QoE) for end-users.

In the context of mm-wave RAN, signal outages or RLFs are not only triggered in cell boundaries in case of high mobility, but also at any location within the coverage area of a mm-wave AP as soon as the strong LOS or reflection channel component is blocked by dynamics of environment (even if the UE is stationary).

One way to remedy the QoE from user perspective is to apply efficient forward error correction (FEC) schemes, known as fountain codes to counterbalance the outage impacts. Fountain codes have been designed for channels with time varying strong path loss characterized by occurrence of erasures. Luby transform codes (LT codes) are the first class of universal erasure codes out of them [MLU02]. The source for fountain codes will encode a file into streams of

1E-6

1E-5

1E-4

1E-3

1E-2

1E-1

1E+0

0 5 10

BE

R

SNR

MCM

AWGN

Multipath

Channel

70

75

80

85

20 21 22 23 24 25 26 27 28 29 30

Pro

pag

atio

n L

oss

(dB

)distance d (m)

PLM

Free space path-loss

Multipath path-loss

α = MCM + PLM




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packets, each containing random parts of the original file. The fountain source keeps sending these encoded packets to the destination, without knowing which packets will be received. At the receiver’s side, when the number of packets received is slightly higher than the original file size, the source file can be recovered. Combining such FEC schemes at application level facilitates utilising simpler transport protocols (e.g. UDP) without congestion management or error check / control at transport layer. This, in-turn, can additionally improve user QoE by avoiding unintended cross-layer interactions when facing abrupt link quality changes (particularly, in mm-wave bands) as outlined.

To evaluate the proposed solution as above, we incorporate an LT code generator and decoder on top of the mm-wave ns-3 framework as outlined in [MDZ+15] and investigate the application layer performance of LT over UDP (LT) versus TCP. In this set of simulations, the remote host sends sequentially files of fixed length to user via an access network operating on mm-wave. As the UE moves at pedestrian speed (1.5 m/s), the mm-wave signal is blocked by obstacles placed between the UE and the eNB. Such blockages result in signal attenuation of around 30 dB. Three blocks of size 1 m, 2 m and 1 m are located between the eNB and the UE’s path during 25 s of simulation run to induce blockages. The rest of parameters are as below:

Application data rate: 300 Mbps

TCP congestion control algorithm: New Reno

TCP send buffer size: 1.5 MB

RLC mode: AM

RLC transmission buffer size: 10 MB

Centre frequency: 28 GHz

Bandwidth: 333 MHz

SGW link to remote server: 100 Gbps

SGW link to remote server delay: 10 ms

Throughput curves depicted in Figure 4-3 show that each occurrence of link blockage introduces throughput drop of more than 200 Mbps for both LT and TCP. LT code, as operates on top of UDP, reaches its steady state throughput, i.e. 300 Mbps, shortly after the simulation starts whereas TCP only manages to reach its maximum throughput of 280 Mbps after 1 second. Moreover, LT scheme achieves an approximately 20 Mbps throughput gain comparing to TCP most of the time. We also note that after each blockage, LT throughput exceeds the steady state throughput for a small duration. This is due to accumulated packets in the RLC buffer during blockages. The throughput spike will last until the end of current file transmission. Such spikes are not seen in TCP due to the congestion control scheme.

Figure 4-3 Application throughput of LT code and TCP for a file size of 52 MB




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Further simulation analysis in equivalent settings as above suggests that LT codes can achieve complete and reliable file transmissions over UDP with lower levels of overhead (i.e., better throughput) in mm-wave bands (as shown in Figure 4-4b). The red line depicts completion of each file (52 MB) in sequence. Furthermore, analysis also shows that in outage regime (of mm-wave bands) application on top of TCP barely receives all the packets transmitted out of each file as a large number of them are lost during the outages (as shown in Figure 4-4a). On the contrary, LT (over UDP) provides complete file deliveries thanks to FEC mechanism, resulting in more consistent QoE.

Figure 4-4 Sequence number per file received over time for TCP (a) vs. LT (b)

4.2.2 Optimization of RLC buffer size and timers

The unique dynamics of mm-wave communications, i.e., their capability in achieving high data rates with high variability in channel quality, have made existing research efforts to focus mainly on the physical (PHY) and medium access control (MAC) layers. While the majority of the works in the literature have investigated propagation issues, beamforming procedures, and MAC layer design aspects, as outlined earlier, the impact of transport protocols requires investigation efforts. Transport protocols such as TCP, regulate the amount and at which rate packets source nodes inject packets in the network. The objectives of transport protocols are to exploit link availability as much as possible, while preventing congestion in the network at the same time. Congestion mechanisms rely either on detection of losses or on estimation of queuing delays. When packets are not acknowledged, the sender performs automatic retransmission after a retransmission timeout (RTO) timer has expired. Large buffers help preventing packet losses at the cost of an increase of delay. 3GPP defines the Radio Link Control (RLC) protocol to perform error recovery in the RAN and it aims to reduce performance degradation of TCP suffers due to packet losses over wireless links. Given the high variability of mm-wave links, proper setting of the RTO and RLC buffer dimension is crucial to optimise desired objectives, such as throughput.

In the simulated scenario, a UE moves parallel to the eNB by first standing still for 2 s, then moving at pedestrian speed of 1.5 m/s for 21 s and finally standing still for 2 s again. Two 10x15x30 m buildings interrupt the mm-wave LOS connectivity for periods of 3.8 s each. The setup permits to verify how the TCP protocols react to transitions between multiple LOS and NLOS periods. The UE establishes a 1 Gbps data rate FTP connection with the eNB for a 25 s period. Simulations are carried out with the ns3 mm-wave module [MDZ+15].

Figure 4-5 illustrates the throughput and RTT performance for several congestion control protocols. The gray and blue backgrounds denote respectively the NLOS period and the period when the user stands still. In the first second of the simulation, all the congestion control protocols make the achieved throughput dropping due to packet loss. The protocols enter in slow start mechanism and quickly increase the Congestion Window (CW) size. However, the RLC buffer, set by default at 1 MB, is not sufficiently large to hold all the incoming packets. Once the sender receives a triple duplicated ACK signal, all protocols enter the Fast Recovery (FR) phase and send only one packet per RTT. Given the large CW size, the number of unacknowledged packets makes impossible for the FR phase to retransmit all the segments




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before the RTO timer of 1 s expires. Then, the CW resets to its initial value and all the TCP protocols restart from the slow start phase. Htcp is the only protocol that exploits the available link capacity, and achieves the highest throughput only after the second NLOS period.

Figure 4-5 Performance comparison of TCP protocols

After having performed an optimization analysis, 7 MB is the minimum buffer size able to store a sufficient number of packets to prevent TCP performing fast retransmission and incur a timeout. Simulation analysis suggested reducing the RTO from 1 s to 200 ms, as shorter RTOs are beneficial to maintain a high data rate. Figure 4-6 (a) shows for demonstration the improvement in throughput performance of TCP Scalable. In LOS phase, the achieved throughput corresponds to the available data rate of 1 Gbps and the recovery from a NLOS to a LOS phase becomes quicker. It is worth noting that, in NLOS, throughput degradation is smoother after having optimized RTO timer and RLC buffer size. However, the increase of RLC buffer size affects negatively the RTT, especially in the NLOS periods (see Figure 4-6 (b)).

Figure 4-6 TCP performance after RLC buffer size optimization

4.3 Design of reference signals to support active and idle mode mobility

In LTE, the mobility related measurements are based on periodic reference signals, broadcast omni-directionally by all cells. A UE shall detect, measure, and compare these reference signals from serving and neighbouring cells. If a UE is in RRC_IDLE state, it will select the best cell to camp on and if the UE is in RRC_CONNECTED state, then the UE will send a measurement report to the network if the signals surpass certain network-configured thresholds, and then the network will select the target cell for handover.

As mm-wave NR will rely on beamforming, in order to overcome the propagation limitations inherent at mm-waves, the active (RRC CONNECTED) mode mobility procedures must be re-evaluated. Figure 4-7 shows a typical scenario for SINR variations as a UE moves around at 10 km/h in an urban environment (same environment as shown in Figure 3-5). Sudden drops of




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SINR as the UE turns around a corner results in RLFs, if the UE cannot switch to another beam quickly enough.

Figure 4-7 Average SINR variations over UE route at 15 GHz

In 3GPP, it has been agreed that a UE in RRC_CONNECTED mode will be able to use the channel state information reference signals (CSI-RS) which were introduced in LTE to support MIMO, in addition to the idle mode synchronization signals (SS) to perform the radio resource management (RRM). However, the properties of such a CSI-RS are under discussion in 3GPP. One of the key requirements of the CSI-RS to be used for RRM measurements for L3 mobility reasons, is to be able to perform beam level qualitative measurements. When the gNB changes its Tx beam, it may be that the signal will travel through a different path to the UE. If the signal reaches the UE via a reflection, the propagation delay will be different, and in some cases very different. One example taken from a measurement campaign is shown in Figure 4-8.

Figure 4-8 Results from a field test with beam switching. Approximately at t = 35 s, the gNB changes its Tx beam. When this happens, the propagation delay changes abruptly with 0.7 µs.

This is larger than the cyclic prefix at 120 kHz sub-carrier spacing, which would make it difficult for the UE to synchronize to the newly received beam.

The result in Figure 4-8 indicates that a large abrupt change in propagation delay is indeed possible even for an intra-TRP beam switch. It is very likely that the signal will arrive at the UE outside the cyclic prefix of the CSI-RS symbol, even for a numerology of 120 kHz. We, thus, can say that the propagation delays of rays corresponding to different intra-gNB beams may differ by an amount exceeding the cyclic prefix for 120 kHz and 240 kHz sub-carrier spacing.




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Another way of saying this is that CSI-RS transmitted through different intra-gNB beams are not quasi co-located (QCL) with respect to timing. This may make it very difficult for the UE to receive the CSI-RS in the new beam due to the lack of synchronization. To overcome this problem, there is a need to consider other ways to provide a synchronization source for the UE and ideally such a synchronization signal needs to be embedded within the CSI-RS symbols.

As higher carrier frequencies in many instances require significantly narrower beamforming compared to lower frequencies (e.g., below 6 GHz) to overcome the increased path losses, there will be a need for a great number of concurrent beams in an active system. As the CSI-RS will be finite, an always-on reference signal would require a very complex beam planning. However, the CSI-RSs are on-demand and only transmitted when the network perceives that a UE needs them, thus greatly reducing the complexity.

In addition, in certain scenarios, it can be beneficial to employ uplink measurement based mobility, where the UE transmits an uplink synch signal (USS) as a wide beam (or a narrower beam scanning over a wide range) and the candidate nodes measure the UL signals. The transmission of this UL signal would be triggered by e.g., a deterioration of the serving beam, where the serving node would configure the UE with the UL signal and candidate nodes to measure the USS. In order to be able to use the UL measurements to decide on DL beams, directional reciprocity must be assumed. The validity of the directional reciprocity needs to be evaluated on a case-by-case basis for both time-division duplex (TDD) and frequency-division duplex (FDD) schemes, as it will depend on e.g., deployment and frequency range. As the assumptions may not hold for all scenarios, UL measurement based mobility must be supplemented by DL based measurement in those cases.

The active mode mobility in NR requires frequent transmissions of reference signals in narrow beams to ensure prompt switching in case of poor coverage. However, if these reference signals were provided in every beam with the strictest periodicity required, the overhead and added interference would be prohibitive, not to mention the wasted energy in transmitting superfluous signals not always used by the UE. Thus, unlike LTE, there is a need to distinguish between idle mode and active mode mobility. The requirement for the idle mode mobility is to provide means for accessing the network, which is much more latency tolerant than the active mode mobility. The strictest mobility requirements of up to 500 km/h for the use case ‘moving hotspot’ [MMMAG15-IR31] will require reference signals to be transmitted very often (e.g., every 5-10 ms) to allow for seamless or lossless handovers. However, for stationary UEs, this very frequent transmission of reference signals would be wasteful, both in terms of energy efficiency as well as in radio resources. Likewise, for idle UEs, the latency requirements are less critical and the UEs can rely on sparser reference signals. Therefore, the idle mode signals should be sparse in time (e.g., every 100 ms), while the CSI-RS should be configurable to be transmitted with very short periodicities (e.g., down to every 5 ms) when needed and be possible to turn off when not needed. In addition, the idle mode reference signals can be more narrowband, only covering the minimum bandwidth (e.g., 6 PRBs), whereas the active mode reference signals will need frequency diversity and may span a wider bandwidth.

Finally, there may be significant hardware limitations to receive wide-beam idle mode signals simultaneously as beamformed active mode signals. The antenna gain of the beamforming may surpass 20 dB, which will lower out omnidirectional idle mode signals in the receiving UE. Therefore, instead of reusing the idle mode reference signals for active mode mobility, it is better to rely on a new set of active mode signals, which are decoupled from the idle mode reference signals.

To evaluate the benefit of using configurable connected mode reference signals and static, sparse idle mode signals, the energy performance of the system described in Section 0 is evaluated. For LTE, a significant contribution to the energy consumption comes from the always-on signals. These signals are broadcast in all cells, regardless of the existence of any active users, or even any users camping within those cells. Figure 4-9 shows the traffic pattern




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for a typical European country [AGD+11], which clearly shows that during peak hours, the traffic load is several times larger than during off-hours.

Figure 4-9 Daily traffic profile for typical European country [AGD+11]

To evaluate the energy consumption of the different systems, the well-known EARTH power models is used for LTE [AGD+11]. The power consumption is modelled as:

𝑃𝑒𝑁𝐵 = 𝑁𝑇𝑅𝑋 × {𝛥𝑝𝑃𝑡𝑥 + 𝑃0 0 < 𝑃𝑡𝑥 < 𝑃𝑚𝑎𝑥

𝑃0 𝑃𝑡𝑥 = 0 (4.4)

where NTRX = 6 and Pmax denote the number of transceivers and maximum transmit power per transceiver respectively. Δp = 4.7 is the slope of the load-dependent power consumption for the output power Ptx and P0 = 130 W is the baseline power consumption associated to transmission of always-on broadcasts of system information requiring active site cooling and signal processing.

For NR, a simplified power consumption model is used defined as:

𝑃𝑔𝑁𝐵 = 𝑁𝑆 × {𝑃𝑡𝑥𝑠

𝜀+𝑁𝑃𝐶 + 𝑃0 0 < 𝑃𝑡𝑥 < 𝑃𝑚𝑎𝑥

𝛿𝑃0 𝑃𝑡𝑥 = 0 (4.5)

where Ns = 3, N = 200, and ε = 0.25 denote the number of sectors in a site, number of RF chains, and power amplifier (PA) efficiency respectively. Pc = 1 W represents the additional digital and RF processing needed for each antenna branch and P0 = 260 W is the baseline power consumption for each sector. δ = 0.26 is the ratio of power consumption related to always-on transmission of system information and Ptx is the transmitted power per sector which depends on the current load of the system. The daily average power consumption per area A is calculated using [TFA+15]:

𝑃𝑎𝑟𝑒𝑎 =1

24

∑ ∑ 𝑃𝑎𝑐𝑡𝑖𝑣𝑒𝜂𝑖𝑡+𝑃𝑠𝑙𝑒𝑒𝑝(1−𝜂𝑖

𝑡)𝑁𝐵𝑆𝑖=1

24𝑡=1

𝐴 (4.6)

where NBS is the total number of base stations in the network, Pactive and Psleep are the power consumption of each base station when it is transmitting and when it is in sleep mode

respectively. 𝜂𝑖𝑡 represents the resource utilization of base station 𝑖 during hour 𝑡. The daily

average power consumption is calculated by identifying the resource utilization of each base station in the network for a given peak data traffic demand and relating this to the daily traffic fluctuation pattern shown in Figure 4-9.

The calculated power consumption is illustrated in Figure 4-10 for standalone LTE at 2.6 GHz, standalone NR at 15 GHz and LTE and NR co-deployed at 2.6 and 15 GHz, respectively. By replacing LTE with NR, the static power consumption is reduced by approximately 65%, since the system information is transmitted more sparsely. When NR and LTE are co-deployed, the static power consumptions are added together.




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Figure 4-10 Daily average area power consumption at a) 95 and b) 1200 Mbps/km2. 5th percentile

downlink user throughput at c) 95 and d) 1200 Mbps/km2.

As can be noted, at low loads (95 Mbps/km2), the majority of the power consumption comes from the static part, corresponding to a significant overhead. As the traffic load increases, a larger part of the energy consumption stems from the dynamic part. It is clear that standalone LTE cannot provide sufficient capacity, even though the power consumption is rather high, both at low and high loads. For NR standalone, the power consumption is greatly reduced compared to LTE standalone for both low and high loads, with an increase in 5th percentile downlink user throughput. When utilizing both LTE and NR, the power consumption at high loads is increased by approximately 80% compared to standalone NR, although it is 20% lower than standalone LTE, however the 5th percentile downlink user throughput for the non-standalone scenario is also approximately 3 times as large. On the other hand, LTE standalone cannot provide any throughput for the 5th percentile user at high loads.

4.4 Low frequency assisted initial access The study on initial access discussed in WP4 of the project focuses mainly on standalone mm-wave systems, whereas this concept is applicable to non-standalone deployments, especially in the scenario, where, low-frequency and mm-wave RATs are co-deployed. In case of co-deployment of low and high-frequency RATs, tight interworking between the two RATs offers more optimization opportunities for system operation. Among others, low frequency-assisted initial access is an illustrative example of tight inter-RAT inter-working.

Typically, the initial access process comprises the three tasks of downlink timing and frequency synchronization, system information acquisition and uplink timing synchronization. It should be noted that some of these tasks are also required during transitions from the RRC_IDLE to the RRC_CONNECTED state; therefore, the proposed concept can be also applied for such RRC state transition procedure, when applicable. During initial access, a UE has to establish a RRC connection with the corresponding mm-wave AP. The performance of this procedure directly impacts the user experience. Therefore, on PHY layer, a beam alignment must be achieved within short time. Exploitation of the limited a-priori information on the preferred transmission direction at both ends of the link will support this. In a non-standalone deployment, i.e., a heterogeneous network, as the one illustrated in Figure 4-11, where mm-wave small cells are located within the coverage area of a macro cell operating at low frequency, low frequency RAT assistance can improve initial access performance significantly. Especially UE power consumption and latency can be reduced.




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Figure 4-11 Low frequency-assisted initial access to a heterogeneous network

In the following, the three mentioned tasks of low frequency RAT assistance are highlighted.

4.4.1 Downlink synchronization

For downlink synchronization the UE exploits synchronization signals transmitted by the AP. These are, in particular, time-frequency resources with a certain periodicity, which allow acquisition of symbol, slot and sub-frame timing. After achieving that, the UE is able to obtain the cell ID. If the UE is located in a low frequency RAT coverage area, the low frequency RAT can transmit information about frequency and cell IDs of mm-wave small cells within its coverage area, by e.g., dedicated signalling to the UE. With this signalling, the UE does not need to perform an exhaustive search over the entire small cell ID space, but it only tries to detect the signalled cell IDs. As a consequence, the UE power consumption for downlink synchronization is significantly reduced.

4.4.2 System information transmission

Another task of the initial access procedure is to acquire the system information, which provides all the essential information for accessing the network to the UE. The coverage of the system information determines the coverage of the cell. Some of the system information components, e.g., the system frame number, are changing fast on the basis of one or several mm-wave RAT frames. Other system information components vary relatively slowly, so information about system bandwidth, random access resources, paging resources and scheduling of other system information components is typically semi-static. For this reason, it can be energy efficient to convey some of the slowly varying system information by exploiting the existing low frequency RAT. The fast-changing system information components, however, need to be transmitted by the mm-wave RAT.

4.4.3 Uplink synchronization

It is important that efficient UL data transmission in the mm-wave RAT is supported as well, especially for “UL data traffic dominant” use cases, e.g., uploading content, such as high-resolution videos to social media during sports events, concerts etc. UL synchronization needs to be achieved prior to any UL packet transmission to ensure that all the co-scheduled UEs’ UL signals are time-aligned at the eNB. A RACH procedure, similar to that standardized in LTE can be used. Based on the RACH preamble transmitted by the UE, the eNB can determine the timing advance value for the UE. The radio resources for the preamble transmission are typically part of the system information and such system information can be signalled by the low frequency RAT. This can be viewed as a basic assistance to the UL synchronization. To ensure




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a certain UL preamble coverage, if several preamble formats are supported by the system, the low frequency RAT can signal a particular preamble format to the UE in order to realize the network assisted preamble format selection. In case of contention free RACH, the low frequency RAT can signal the exact preamble sequence to be used by the UE.

During the LTE-like RACH procedure, the RACH response signal can be also transmitted by the low frequency RAT, e.g., LTE. In addition to the above mentioned options for UL synchronization assistance, the low frequency RAT may also offer assistance to the possible beam alignment operations during the initial UL synchronization procedure.

4.5 Mobility improvements

4.5.1 User movement prediction

Good propagation conditions and beam steering are necessary to achieve high data rates. In order to achieve this, accurate position estimation and position tracking is needed, especially for dense urban and high mobility scenarios. This poses a number of challenges related to the capability of accurately estimating the position and following the movement of the users, in order to maintain a stable mm-wave connection. To illustrate, consider a scenario, where, a number of connected cars are moving at inner city speed (around 30-50 km/h), with mm-wave APs located at elevated sites (e.g., on top of buildings or lampposts). In such a scenario, frequent variations of velocity and acceleration, as well as rapid changes in movement direction make the current beam steering direction become outdated very rapidly, which thus requires frequent re-training. In addition, the beam-training overhead per user is independent of the one related to other users and depends only on the user’s mobility. As the number of users increases, so does the beam training overhead. In high-density mobile scenarios, this overhead may become prohibitively large, unless more intelligent beam-training strategies are used. From this perspective, mobility and user density are equivalent issues to be tackled by beam training and tracking algorithms, and special care is required when highly mobile users associate to APs that already serve a large number of mm-wave terminals. In particular, these scenarios yield three related issues:

First, beam training procedures upon AP association can be too slow and result in suboptimal beam pattern choices, which in turn would lead to unstable channel and data rates;

Second, the changes of the optimal beam pattern induced by the movement of the users must be tracked in order to consistently maintain a sufficiently high link rate;

Third, links can be easily broken due to the users moving behind an obstacle or some blocking material, such as a building, vegetation, vehicles, or other users.

In these cases, agile, possibly proactive AP re-association mechanisms should be provided, in order to avoid that a user loses connectivity over long time periods, and a complete beam training procedure needs to be re-initiated from scratch. Embedding history information about the users’ movement patterns into the beam training and tracking process at an mm-wave AP can considerably improve the performance of mm-wave links and relieve part of the time burden caused by beam training procedures [PDW17]. The prediction of the movement of the users can be fully estimated at the AP side, without requiring any explicit feedback of position information from the users to the AP. This yields the two-fold advantage that it incurs no overhead, and that no interface is required between mm-wave communication systems and other positioning subsystems embedded in user terminals or vehicles, such as Global Positioning System (GPS) receivers. In fact, no precise location information is required from external sources: the same can be reliably estimated by APs, using only some information from current beam pattern choices that would help drive future beam tracking procedures.

4.5.2 User localization and environment mapping

Mm-wave technology will play an essential role in indoor localization and mobility because of its unique characteristics. Propagation occurs in quasi-optical patterns, whereby reflections off the




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boundaries of indoor surfaces and obstacles are subject to limited scattering and the LOS component tends to be predominant over NLOS components, even in the presence of obstacles. Mm-wave signals are characterized by short wavelength and large bandwidth, thus even directional transmission may generate multiple reflected paths reaching the moving receiver with different delays and angles of arrival (AoAs). The information extracted by the phased antenna arrays typically used for mm-wave devices fits well with the purpose of localization. In a generic environment where different mmWave APs are present, the signals transmitted by each AP typically reach a node via both LoS and NLoS paths. The antenna array of the node can be used to estimate the AoA of each multipath arrival from each AP, thereby providing a so-called AoA spectrum for each AP that illuminates the node. The AoA spectrum information can be directly passed on by a node’s receiving hardware, or can be derived by processing beam tracking information (i.e., the sector ID of the phased antenna array). The latter can be forwarded by MAC protocols such as 802.11ad, which are aware of the sector ID. The algorithm estimates the location a mobile user in an indoor space working without any a priori knowledge about the surrounding and the location and number of access points available. The angle-of-difference-of-arrival (ADoA) determines an estimation of user location exploiting the information about AoA components, i.e., LoS and NLoS. The latter map physical APs to virtual APs (VAs), defined as the reflection of a physical AP through a reflective surface. In more detail, the ADoA algorithm aims at finding a set of anchors and user locations over time so that the pairwise differences between the AoAs of the anchor signals and the ones detected by the users are compatible. After having detected an initial estimate of the anchors’ locations through a grid search procedure, the algorithm refines the estimation with an iterative optimization process minimizing the minimum mean square error [PCW17]. The solution obtained is invariant to rotation, translation and scaling as the orientation, origin of the reference system and absolute distance estimation are all unknown. Once the user location and the anchors have been estimated with sufficient accuracy, it is possible to reconstruct the shape of environment determining the location of reflective surfaces and walls. The intuition is as follows: the geometric relationship between physical APs and VAs permits to estimate the location of the point on the wall where the signal of the physical AP reflects. The accuracy of the estimation is enhanced taking into account different user locations to see the reflection point on the wall from different angles. Figure 4-12 (a) illustrates the estimated user and APs location after having performed measurements in a room with reflective walls. Figure 4-12 (b) compares the estimated localization error in form of CDF for the proposed solution (mmMAGIC in the legend) with a baseline algorithm (ADoA) and a scheme which triangulates the location through association of the two strongest AoAs and performs subsequent validation with further triangulations (TV).

Figure 4-12 (a) Path reconstruction in a scenario with 4 APs. (b) Localization error for different schemes




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A fast, accurate and dependable location service is essential to improve robustness and effectiveness of mm-wave communication systems against blockages due to beam misalignment. In addition, it is a fundamental building block for a broad spectrum of applications and advanced services. In transportation hubs, such as airports or railway stations, indoor positioning is essential to inform travellers upon unexpected change of gate or platform. Travellers can be directed to the destination with information on the navigation path and the actual, not estimated, time to reach the gate or platform. In commercial areas, activities like shops and restaurants can push to clients customized advertisement and potential deals based on previous stays or already covered path. At the same time, operators of transportation hubs, convention centres and public buildings such as malls, schools and hospitals can monitor flow of visitors using indoor location analytics and obtain insights on most visited areas. In warehouses and industry areas, location services enable precise tracking of assets and goods for example providing automatic notification when goods enter or leave a predefined area. Healthcare is a special application domain for indoor localization and mobility. Potential use cases include, navigation of patients with mobility impairments, monitoring location of medical staff to ensure adequate assistance to patients and that qualified personnel check status of their recovery, and to verify accessibility of restricted areas such as supply rooms. Application of indoor localization technology is valuable for augmented reality and assisted living. Accurate localization and tracking can also serve as a proxy for physical communication functions, such as beamforming, handovers and context switching.

4.6 Physical layer improvements

4.6.1 Multi antenna techniques to increase area capacity

The mm-wave frequencies bring challenges in terms of coverage, due to the increased path loss and reduced diffraction. Meanwhile, the increased carrier frequency also allows for smaller antennas which will enable the use of a large number of antennas using the same form factor. To evaluate the benefits of the multi-antenna techniques, the performance of a standalone mm-wave system at 30 GHz with 200 MHz bandwidth was simulated in the 3GPP dense urban macro deployment with 200 m inter-site distance with cross polarize 1x32 vertical antennas using MIMO. The traffic model, is based on the media on-demand use case [MMMAGICD3.1] with residential users streaming 4k video (~15 Mbps) during prime time. As such, the user mobility will be negligible and the required bit rate will be moderate, compared to the baseline use case ‘50+Mbps everywhere’. The population density is assumed to be 20 000 persons/km2 with a 40% market share, corresponding to 4000 subscribers/km2 where each user is assumed to be indoor. As each subscriber requires a bit rate of 15 Mbps, the area traffic demand will be 60 Gbps/km2. Due to the hexagonal deployment, each cell area will be approximately 0.0115 km2, which mean that the traffic load will be approximately 690 Mbps/cell.

The simulation results are as captured in in Figure 4-13, showing the downlink user throughput for the average and 5th percentile user as a function of the traffic load per cell. The vertical dashed line indicates the target traffic load to support, which shows that the average throughput is in excess of 750 Mbps, while even the 5th percentile user has more than 250 Mbps in downlink throughput. As is clearly evident, the deployment can easily fulfil the KPIs of the Media on demand use case.




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Figure 4-13 Downlink user throughput for average and 5th percentile user using MIMO at 30 GHz




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5 Network integration

5.1 Integration concept To design the architecture of a 5G mm-wave system the architectural enablers and functions described in the previous sections are mandatory components. To operate mm-wave systems in the various deployments and use cases it is necessary to understand the relation between these enablers and functions.

Integration of the system can be seen as a twofold task.

First, operating mm-wave systems in presence of the challenges provided by the radio channel recommends usage of multiple mm-wave APs to ensure sufficient coverage and reliability. The enablers and functions are supporting such a system integration, which is far more than the conventional multi-cell approach. Also combination with APs operating in a lower frequency band fit into this level of integration.

Second, the interfaces of these enablers and the overall architecture even allow integration into an overall 5G system within a wider context. E.g. mm-wave systems comprising legacy LTE or 5G systems operating in low frequency bands can be further integrated with other wireless systems, even with other existing standards like WiFi. Such integrated systems can provide seamless coverage and allow selection of the best possible link to the UE. Power efficiency in such an environment can be controlled not only through optimization of the individual elements, but will rely on selection of the most efficient RAT, which is available to connect a UE to a desired service. This ensures optimum usage of all available resources integrated in the 5G system.

For both levels of integration the interrelation between the enablers and functions is important. A core point of the integration is the capability of multi-connectivity to connect a UE via multiple paths to the serving APs.

The key for a flexible integration is the multi-connectivity concept. It enables a split of data streams between different connections to the UE, where either all of these connections could be active, or only one connection is active at a time. If only one connection is active, in parallel alternative connections are prepared to take over transmission seamlessly in case of sudden link failure. Therefore, this concept is closely related to mm-wave cell clustering and to low band assisted access. Both will help to retain an alternative connection for data transfer and increased availability for related control signalling.

Cell clustering, on its part, is dependent on the available backhaul capacity. In case of ideal backhaul capabilities, i.e. with all APs connected with a fiber, even highly centralized architectures are feasible, where all control functions can be concentrated in the cloud. For extremely dense mm-wave networks this will not be feasible. In this case wireless self-backhauling is the envisaged solution. However, this approach can lead to non-ideal backhaul with varying capacity limitations. The integration of cell clustering and self-backhaul is mainly about making the right decision on the split between the cloud and radio part of the network, and about deploying the appropriate type of TRPs handling the data connections either up to MAC (with ideal backhaul) or up to PDCP (in case of non-ideal backhaul).

Self-backhauling is an enabler for multi-connectivity since connection between base stations over Xn interface is needed. Multi-connectivity needs to be integrated with self-backhauling which will enable fast data forwarding for buffer synchronization between the APs involved (in case where service flow is split on the PDCP level).

The new mobility state RRC_INACTIVE requires integration with several other enablers. The RRC_INACTIVE state requires measurements and other information from UE for its proper configuration. In case of inter-RAT multi-connectivity, available measurements might vary and integration requires taking proper measurements and processing them, in order to have




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information that can be used to change mobility states and configure RRC_INACTIVE state to achieve gains from using this new mobility state.

When considering operation of mm-wave clusters under presence of RRC_INACTIVE state, the impact on cluster reconfiguration, cluster updates and buffer synchronization needs to be considered. Also self-backhauling needs to be aware of RRC_INACTIVE and APs need to keep S1 connection to the core under mobility of the UE in the new state.

Network slicing, as an important aspect of 5G networks, has also impact on the enablers and RAN functions. A UE will be configured to be served on a given slice, thus the UE will be able to continue performing multi-connectivity or connecting to the different mm-wave cell clustering using the configured slice. At the network side, coordination will be needed between the master and the secondary nodes in order to serve the UE on the same slice.

Network slicing requires self-backhauling to be transparent to the UE in a heterogeneous network. This could lead to complex RRM on network level, especially when the spectrum will be shared between multiple slices but also between self-backhauling and access.

Further, it is expected that the slice availability will be consistent at least within the tracking areas, which a UE can be configured with. A UE in inactive mode can freely reselect to other cells within the configured tracking area index list (TAI-list) and resume its connection in its previous slice. If a UE in RRC_INACTIVE selects a cell outside the configured TAI-list, it will resume its connection and perform a tracking area update, whereupon the CN redirects it to the proper slice. When the UE resumes the connection, it may need to consider slice-specific access control policies broadcasted by the network.

In the following section an integration platform is described, which integrates the 5G mm-wave system with other different RATs and combines various architectural solutions via introduction of additional abstraction layers.

5.2 Multi-RAT Network densification for PE deployment issues

5.2.1 System model

For multi-RAT deployment issues, a software platform, denoted green multi-technology engineering (GMTE) [UMS15], has been developed to optimize network densification covering Wi-Fi technologies for hot spots combined with mobile cellular networks for 5G deployment issues.

In mmMAGIC, we have extended it with the combination of LTE-A and E-band transmissions implemented on embedded modes of the IEEE802.11 ad standard in accordance with the multi-band system integration developed in the section 3.1.5 and the transposition of multi-carrier UWB technologies [ECMA368] in mm-wave bands in order to achieve small cell radio coverage compliant with power regulations. Furthermore, mmMAGIC scenarios considering the usage of WCR’19 spectrum has been examined as detailed in the section 5.2.2. Morever, geographical access point position selection is combined with RATs selection and transmit power minimization. For that purpose, a transmit power metric denoted EIRPopt has been derived from the GLB metric (see section 4.1) as detailed below.

A multi-RAT link adaptation metric selecting the most power efficient transmission technique (the GLB metric exposed in the section 4.1) is computed and mapped in a radio-engineering platform which is denoted GMTE. When devices and base stations support several technologies, the platform shows the metric decision by using a color codification associated to the selected interface, MCS and available TMs. The most relevant key idea has been to integrate the GLB metric in radio engineering tools in order to extend engineering functionalities with PE multi-RAT network densification recommendations.

The GMTE platform integrates and maps link adaptation metrics in order to optimize network densification in a multi-RAT context. Optimization is turned towards power efficiency whilst guaranteeing QoS [UMS15]. The link adaptation metric may be the RSSI, representative of the




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received power in a geographical zone, or other link adaptation metrics, typically the GLB metric detailed in the deliverable D3.1 and [SUM16][UMS15] or the required EIRP level of a deployed technology with respect with the desired radio coverage. The Figure 5-1 qualitatively illustrates the -metric variations in a geographical zone considering several small cell enabling IEEE802.11 ac [IEEE802.11] and ECMA-368 [ECMA-368] standard transmissions. The represented -sub-metric of the GLB metric translates normalized degradations at the link level and propagation side associated to the deployed technology for a given throughput in the interest geographical zone (for further details, see [UMS15] [MMMAG16-D31], [MMMAG17-D13]). On Figure 5-1, the -metric values are captioned for typical distances between the access points and the UE, n2 and n3 TM refer to MIMO spatial time block coding (STBC) and spatial division multiplex (SDM) TMs of the IEEE802.11 ac detailed in Table 5-3 on page 55.

Figure 5-1 GMTE platform with visualisation of the -metric variations in a geographical zone (GLB sub-metric)

Two new functionalities in the GMTE tool have been developed in mmMAGIC with :

A metric derivation translating the required radiated power level denoted EIRPopt of the technology selection carried out by the GLB metric.

A geographical access point position selection which is combined with the RATs selection and transmit power minimization.

The EIRPopt metric gives the power requirement as a function of the distance and propagation

conditions for each considered TM selected by the GLB -sub metric decision (see section 4.1). EIRPopt is derived from the GLB metric computation considering selected TM denoted by the j

index. For those modes, the -metric (eq. (4.3)) is envisioned (in EIRPopt expression), as a power margin to be integrated in the EIRP level requirements for the TM n°j deployed in propagation conditions described by the k index. k depends on LOS/NLOS criterion, path-loss

models and power management (antenna gains). EIRPopt is derived from the -metric as follow:

, , , , , , , , ( , )opt k j k j M j R k j MFS k cEIRP S G PL d f (5.1)

In (5-1), k,j is then fixed to an arbitrary value conditioned by propagation conditions (3-4 dB in

LOS and 6-10 dB in OLOS). EIRPopt may also be expressed as a function of both -metric

and -metric variations as follow, involving then k,j calculations, AWGN sentivity levels and the free space path-loss model:

=6 dB

d1

=17 dB

d1

d1

=14 dB

=14 dB

=24 dB

d1

n2: STBC(1,2,4)

n2: STBC(1,2,4)

n3: SDM(2,2,4)




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, , , , , , , , ,

, , , , ,

( , )

( , ) ( , )

opt k j k j k j AWGN j R k j FS k c

k j M j AWGN j MFS k c FS k c

EIRP S G PL d f

with

S S PL d f PL d f

(5.2)

The second functionality is connected to the access point selection to power efficiently cover a geographical zone combined with air interface selection performed by the GLB metric. Analysis are turned towards small cell deployments with multi-gigabit throughput handled by IEEE802.11 ad technology translated in E-bands using experimental propagation measurements performed by the University of Bristol [[MMMAG17-D13]. An illustration is done on the figure below and developed in the section 5.2.3.

Figure 5-2 A visualization of the GLB metric in the GMTE platform using University of Bristol measurements carried out at 82 GHz

Figure 5-2 illustrates the GLB metric performance in the GMTE platform. The most appropriate access point and the most power efficient technology to be deployed along a mobile path in the Bristol is represented. Three active access points are positioned and MISO techniques are implemented: the time reversal, the Alamouti code and equal gain transmission techniques (TT) [SCUM15]. The TT selection and the coverage of each AP are translated in different colors, highlighting the access point selection combined with the technology selection. The GLB metric has been also implemented in the UoB’s radio engineering tool as illustrated on the following video (https://www.youtube.com/watch?v=tsujaUO9wo4&feature=youtu.be).

https://www.youtube.com/watch?v=tsujaUO9wo4&feature=youtu.be




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5.2.2 Multi-RAT and multiple access point scenarios

Mm-wave scenarios envisioned in the mmMAGIC project consider new unlicensed spectrum bands as candidates for IMT2020 as summarized in the Nokia white paper [Nok15] and Figure 5-3.

Figure 5-3 IMT2020 spectrum bands for 5G scenarios, [Nok15]

Orange proposes to extend Wireless Local Area Networks (WLAN) and Ultra-Wide Band (UWB) systems in IMT2020 bands and combine them together in a multi-RAT and multi-band system integration model (see section 3.1.5) and multi-RAN/RAT process (see Section 2).

Envisioned UWB systems are the ECMA-368 with extensions turned towards multi-band aggregation in WRC’19 bands. IEEE 802.11 ac and ad would be extended to other IMT2020 bands according to multipath signature and their adequacy to system parameters and RF front end architectures.

Some examples are exposed below with associated multi-gigabit transmissions for high QoS and video services

IEEE802.11 ad/ay transposed in E and V bands

ECMA 368 transposed in 5 GHz, V and E bands

IEEE 802.11 ac in 5 GHz band and transposed in 24.5-27 GHz, 28 GHz and 37-40, 47.2-50 GHz.

MCS and throughput targets are given for the IEEE802.11 ad/ay and the ECMA-368 standard on the tables below

Table 5-1 IEEE802.11 ad system parameters

IEEE802.11 ad/IEEE802.11 ay

MCS number 15 17 18 20 20’

MCS QPSK1/2 QPSK3/4 16QAM1/2 16-QAM

3/4

64-QAM

1/2

Channel (MHz) 2640

Data rate(Mbps) 1386 2079 2772 4158




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Table 5-2 ECMA-368 and extensions: system parameters

ECMA-368 and extension IEEE802.15.3c AV

SCM SCM368

-5-2B SCM368

-6-2B SCM368

-3-3B SCMAV-1

MCS 16QAM ¾ 64 QAM ½ 16QAM ½ QPSK 2/3

Channel (MHz) 2x528 2x528 3x528 528

Data rate Mbps 1920 1920 1920 1904

5.2.3 Performance

Performance are given by considering the numerical values of the GLB metric, i.e. the {,} sub-metrics as well as the new metric introduced in mmMAGIC, the EIRPopt metric, upon dedicated scenarios.

5.2.3.1 Small cell with Multiple Wi-Fi and 60 GHz UWB deployments

We consider a service close to 80 Mbps which can be delivered either in IEEE802.11 ac or ECMA-368 transposed at 60 GHz. The 60 GHz transposition enlarges radio coverage due to higher transmit power levels authorized by ECC and FCC power regulations. Table 5-2 summarizes some link budget parameters used to compute the a-sub-metric (SM and MCM, section 4.1). NF is the noise figure and Lo describe various RF connectivity parts loss.

Table 5-3 IEEE 802.11.ac and 60 GHz ECMA-368 link budget parameters

Data rate target: 80 Mbps

IEEE802. ac NLOS ECMA-368 At 60 GHz LOS

MCS11n TM 4 4 26 10

TM 16QAM ¾ - Nss=1

16QAM ¾ - Nss=1

QPSK ¾ Nss=4

QPSK ¾ Nss=2

QPSK ½

40 MHz 40 MHz 20 MHz 40 MHz 528 MHz

SISO MIMO STBC (1,2,4)

MIMO SDM (4,4,4)

MIMO SDM (2,2,4)

QPSK ½ TDS+FDS

Data rate (Mbps)

81 81 78 81 80

Bw (MHz) 35.63 35.63 17.5 35.63 507.37

SNR (dB) 21.5 16 27.5 20 1.5

NF + L0 (dB) 10+2.5 10+2.5 10+2.5 10+2.5 8 +2.5

SAWGN (dBm) -71.9 -71.9 -82.05 -78.8 -82.37

SM (dBm) -57.94 -64.94 -56.44 -68.44 -77.89

MCM (dB) 13.96 3.96 25.6 10.36 4.48

Figure 5-4 provides variations of the -metric for 4 IEEE802.11 ac TM and the ECMA-368 transposed at 60 GHz for Wi-Fi and mm-wave Hot Spot extensions. ECMA-368 transposed at Results in Figure 5-4 show that MISO STBC AM using Alamouti Codes applied on 1 spatial stream is the most outstanding mode compared to SISO and S spatial division multiplex (SDM) TM with gains up to 2 and 6 dB respectively. For a distance higher to 150 m, the ECMA-368 system operating at 60 GHz exhibits better

performance with gains increasing with the distance quantized with the -metric numerical values. This result translates that multipath diversity gains when considering high transmission bandwidths (BECMA-368=528 MHz). This scenario proves that air interface switching (AIS) significantly improves power efficiency of multi-RAT network between IEEE802.11ac TM at 5 GHz and ECMA-368 TM operating at 60 GHz for a target throughput close to 80 Mbps.




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Figure 5-4 -metric variations upon the 5GHz IEEE802.11 ac/60 GHz UWB multi-RAT scenario

5.2.3.2 Multi-band system integration application

The multiband system integration scenario applied in the E-band at 82 GHz, illustrates in the same time the access point selection and the limitations in terms of radio coverage, the benefits of spatial focusing techniques introduced by the TR and EGT techniques and the proper decision done by the GLB metric which may differ the RSSI criterion regarding the AP selection. The figure below is restricted to a single IEEE802.11 ad MCS 18 which supports different MISO (2,1) configurations: the TR, the EGT and STBC Alamouti code.




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Figure 5-5 Multi-band system integration application

Depending on propagation situations, TR, EGT or STBC may be selected with their proper radio coverage as shown on Figure 5-5 . The AP1 is in OLOS along the L1 path. AP2 and AP3 are in LOS. The coverage limitations of each technology highlights limitations of the TR when the radio link is partially obstructed.

Quantitative gains upon other IEEE802.11 ay MCS are discussed in [MMMAG17-D13] and some of them are translated on the figure below.

Figure 5-6. metric variations on embedded IEEE802.11 ad transmission modes at 82 GHz

AP1

AP2

AP3

Alamouti TM by AP1

TR TM by AP2

EGT TM by AP3




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Figure 5-6 exhibits -metric variations in connection with similar simulation configuration

employed for the -metric. Figure 5-6 llustrates that the EGT technique is the most power efficient TM and the gain is clearly illustrated especially under OLOS conditions with gains up to 5 dB by employing STBC and TR upon MCS 18. The comparison between EGT and SISO exhibits gains up to 10 dB. TR gains facing to STBC appear upon LOS with gains close to 2.5 dB.

Considering MCS 17, the EGT gains provided for OLOS and LOS conditions are up to 7.5 and 5 dB respectively. The comparison between MCS17 and MCS18 shows that TR is more efficient than STBC when we consider QPSK constellation points. For deployment issues, we recommend EIRP levels close to 30 dBm in LOS and 35 dBm in OLOS.




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6 Conclusions

This is the final deliverable from WP3, which comprises elaboration upon and evaluation of the key concepts presented in the previous deliverables. A wide range of methodologies were selected to evaluate the presented concepts – comprising analytical evaluation, mathematical modelling, theoretical analysis and simulation/visualization studies. A number of technical concepts have been developed during this project and in this deliverable. The main concepts and solutions, their associated use cases, their impact on the target KPIs and their benefits and impact on the overall system design are concisely presented in Table 6-1:

Concept Description Use case Target KPI

Impact on the

overall system

design

Flexible multi-

RAN

management

Architectures associated with

the cross-layer optimization

in a multi-RAT context

enabling multiple KPIs

awareness and embedded

C/U plane architectures

(section 2.1)

Diverse use cases

supporting different

air interface

Power and

spectrum

efficiency,

latency, QoS

and

link budget

GMTE platform

Optimize End-to

End power

management,

cost efficiency,

latency and

carrier

aggregation

multi-RAT

network

deployment

optimization

LTE-NR tight

interworking

Addition of high frequency

mm-wave RAT connectivity

to low frequency RAT access

for use in dual-connectivity

aggregation and diversity of

data transmissions (section

3.1.2)

150+ Mbps

everywhere

Data rate,

Mobility,

Reliability

Allows add-on of

mm-wave system

to existing LTE

deployments,

Non-standalone

deployment

Multi-band

system

integration

Radio Resource

Management in single and

multi-RAT management

architectures (section 3.1.5)

Diverse use cases

supporting different

air interface to

transport the

information and

scalability in RRM

Power and

spectrum

efficiency, QoS

and

link budget;

scalability in

carrier

aggregation

Improved system

design with the

conjunction of

multi-RAT

management and

radio spectrum

allocation

RRC_Inactive

A third state is introduced

into the existing two-state

transition system, where the

RAN and CN connections are

retained, with the UE context

stored in the localized RAN

(section 3.3)

Supports diverse

use cases and

requirements

Reduces C-

Plane latency

and UE power

consumption

Will influence the

overall EMM state

transition

mechanism

1 For some concepts, 50+Mbps everywhere was used as the use case to evaluate. However, this concept can be

used to enable several other 5G use cases.




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Integrated

access backhaul

Allocation of radio resources

to access and backhaul for

both DL and UL

transmissions, where the

mm-wave links share the

same radio resources and

air-interface with TDD, so as

to optimize the network

throughput of mm-wave

HetNet (section 3.4)

50+Mbps

everywhere

Improves user

throughput in

DL and UL

Essential for

spectrally efficient

backhauling

designs

Network slicing

Possibility for independent

network functions to use

common hardware to allow

flexible and resource efficient

utilization (section 3.5)

Support diverse

use cases and

requirements

Data rate,

Mobility,

Reliability,

Availability,

Connection and

traffic density,

Latency

Required for

efficient

infrastructure

usage

Cross layer

optimization

Optimization of transport

protocol via upper layer FEC

(section 4.2.1)

Immersive early 5G

experience

User

Throughput,

QoE

Seamless

interfacing

between layers to

increase

throughput and

QoE

Optimization of transport

protocol parameters (e.g.,

RLC buffer, RTO timer)

(section 4.2.2)

50+Mbps

everywhere

User

Throughput,

Latency

Reference

signals for active

mode mobility

Design of different types of

reference signals, e.g. static

or dynamic, to be used in

case of idle or active mode

UEs (section 4.3)

Support diverse

use cases and

requirements

Mobility,

Availability,

Connection and

traffic density

Required for

active mode

mobility feature to

establish

seamless

connection with

minimized

signalling

overhead

Low frequency

assisted initial

access

How low-frequency (wide

coverage) RAT connectivity

can assist the initial access

procedure at high-frequency

mm-wave RAT (section 4.4)

“UL data traffic

dominant” use

cases, e.g.,

uploading high-res

videos during

events

UE power

consumption;

end-to-end

latency during

initial access

Enhancement of

the mm-wave

initial access

procedure,

focusing on non-

standalone

deployments

Table 6-1 Architectural enablers and concepts overview

It can be noticed from the table that the evaluated concepts are going to play a major role in supporting the inter-working of mm-wave RATs with existing and future telecommunication systems. With the increasing variety of requirements, there is a rise on the applicability and impact of the presented concepts on the overall system integration. For example, there is optimization of end-to-end power management and cost efficiency with the deployment of flexible multi-RAN management technique, as proposed in this deliverable. While a reliable 50+ Mbps data rate can be offered with non-standalone deployed LTE-NR interworking technique, the multi-band system integration, where applicable, offers improved carrier aggregation design and enhanced spectral efficiency. The GMTE platform allows a multi-RAT deployment optimization focused on power management. Furthermore, to support the increasing demand




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to reduce C-Plane latency requirements, in combination with reduced UE power consumption, the concept of RRC_Inactive is going to be tremendously beneficial. This is not only favourable to the UE, but also offers the ability and flexibility for the network to support diverse use cases and applications. Another way to fulfil the 50+ Mbps use case is the integrated allocation of radio resources for mm-wave access and backhaul link transmissions, as proposed in this report. Additionally, the concept of network slicing, where there is the possibility to use independent network features with common hardware functionalities is going to drastically improve the KPIs of data rate, reliability, availability and connection density. Finally, with the cross-layer management and low frequency assisted initial access schemes, the user throughput and QoS can be exceptionally enhanced. Overall, it can be concluded that the presented concepts can well realize the integration of mm-wave systems for the targeted use cases having a variety of requirements for upcoming 5G applications. Based on these findings, arranged workshops and exchange of information with other 5G PPP projects supported a technical alignment on the architectural concepts, which created momentum for impacting current and upcoming releases of 3GPP New Radio standards.




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7 References

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[3GPP TR 38.804] 3GPP TR 38.804 Study on New Radio Access Technology - Radio Interface Protocol Aspects (Release 14), 2016.[3GPP R2-164755] Nokia, Alcatel-Lucent Shanghai Bell, “3GPP TSG-RAN WG2 Meeting#95, R2-164755, Implications of high frequency bands on mobility”, Tech Rep, August 2016.

[3GPP R2-167707] Nokia, Alcatel-Lucent Shanghai Bell, “3GPP TSG-RAN WG2 Meeting#96, R2-167707, “DRX Principles for NR”, November 2016.

[3GPP R3-171305] AT&T, “3GPP TSG-RAN WG3 Meeting#95bis R3-170305, LS on status of higher-layer functional split between central and distributed unit,” April 2017.

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[AGA+16] D. Aziz, J. Gebert, A. Ambrosy, H. Bakker, H. Halbauer, "Architecture Approaches for 5G Millimetre Wave Access Assisted by 5G Low-Band using Multi-Connectivity, IEEE Globecom 2016 Workshop on 5G RAN Design, Dec 8, Washington, DC, USA.

[ALE+16] A. Awada, A. Lobinger, A. Enqvist, A. Talukdar, and I. Viering, "A simplified deterministic channel model for user mobility investigations in 5G network”, submitted to IEEE international conference on communications (ICC), 21-25 May, 2017 Paris, France.

[ALS+14] M. R. Akdeniz, Y. Liu, M. K. Samimi, S. Sun, S. Rangan, T. S. Rappaport, and E. Erkip, “Millimeter wave channel modeling and cellular capacity evaluation,” Selected Areas in Communications, IEEE Journal on, vol. 32, no. 6, pp. 1164–1179, 2014.

[ATS+15] F. Athley, S. Tombaz, E. Semaan, C. Tidestav, A. Furuskär, ”Providing Extreme Mobile Broadband Using Higher Frequency Bands, Beamforming, and Carrier Aggregation”, in Proc. of IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), September 2015.

[AV16] M. Andrews, H. Viswanathan (Nokia Bell Labs) "Capacity Estimation for Self-Backhaul in mmWave Networks”, in 2016 submitted to Infocom




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[Bre03] D. G. Brennan: “Linear diversity combining techniques,” Proceedings of the IEEE, vol. 91, no. 2, pp. 331–356, February 2003.

[BDO+13] C. J. Bernardos, A. De Domenico, J. Ortin, P. Rost and D. Wübben, "Challenges of Designing Jointly the Backhaul and Radio Access Network in a Cloud-Based Mobile Network," 2013 Future Network & Mobile Summit, Lisboa, 2013, pp. 1-10.

[BFB+10] J. J. Boutros, A. G. i Fabregas, E. Biglieri, and G. Zemor, “Low density parity-check codes for nonergodic block-fading channels,” IEEE Transactions on Information Theory, vol. 56, no. 9, pp. 4286–4300, September 2010.

[DWP16] J. Dion, O. Weppe, and S. Paquelet, “A generic and reconfigurable FEC Transceiver for Multi-RAT Platform”, ETSI workshop on future radio technologies-air interfaces, http://www.etsi.org/news-events/events/1005-workshop-on-future-radio-technologies-air-interfaces, January 2016.

[ECMA368] Standard ECMA-368, "High Rate Ultra Wideband PHY and MAC Standard", 3rd Edition -December 2008.

[ETSI-RRS_1+16] I. Siaud, A. M. Ulmer-Moll, K. Safjan, “On joint mmMAGIC and ETSI RRS Architectures for multi-RAT”, ETSI RRS, doc n° 035017r2, September 2016.

[ETSI_WP1+15] M. G. L. Frecassetti, ETSI White paper n°9, “E-Band and V-Band - Survey on status of worldwide regulation”, June 2015

[FCC16] Federal Communications Commission (FCC), “FCC Takes Steps To Facilitate Mobile Broadband And Next Generation Wireless Technologies In Spectrum Above 24 GHz”, July, 2016.

[HCJP17] M. Hashemi, M. Coldrey, M. Johansson, S. Petersson, “Access-integrated backhaul in fixed wireless access systems”, submitted to IEEE VTC-Fall 2017, Toronto, Canada.

[Hoy11] C. Hoymann, ‘LTE-Advanced:Self-backhauling for cost reduction’, 2011, http://www.3g4g.co.uk/LteA/LteA_Pres_0811_Ericsson

[IEEE80211u 11] IEEE Std 802.11u, “Part 11: Wireless LAN medium access control and physical layer specifications, Amendment 9: Interworking with external networks,” Feb. 2011.

[IEEE802.11ad 12] IEEE Std 802.11ad, “Part 11: Wireless LAN Medium Access Control and Physical Layer Specifications, Amendment 3: Enhancements for Very High Throughput in the 60GHz Band,” Dec. 2012.

[JAM08] J. Foutz, A. Spanias and M. K. Benavar, “Narrowband direction of arrival estimation for antenna arrays”, Morgan & Claypool, 2008.

[KBH12] M. T. Kawser, N. I. Bin Hamid, and all, “Downlink SNR to CQI Mapping for Different Multiple Antenna Techniques in LTE”, Int. Journal of Information and Electronics Engineering, Vol. 2, No. 5, September 2012.

[KBN12] R. Kraemer, M. Brzozowski, S. Nowak, “Reliable Architecture for Heterogeneous home networks: the OMEGA I-MAC Approach”, Elec. Energ. Vol. 25, No 1, April 2012, pp. 43 – 58.

[KMF+16] C. Kilinc, J. F. Monserrat, M. C. Filippou, N. Kuruvatti, A. Zaidi, I. DaSilva, and M. Mezzavilla, “New Radio 5G User Plane Design Alternatives,” to appear in proc. of the IEEE Global Communications

http://www.etsi.org/news-events/events/1005-workshop-on-future-radio-technologies-air-interfaces




http://www.3g4g.co.uk/LteA/LteA_Pres_0811_Ericsson




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Conference Workshops (Globecom Workshops), Washington D.C., USA, Dec. 2016.

[LLX+16] Y. Li, J. Luo, W. Xu, N. Vucic, E. Pateromichelakis, and G. Caire, “A joint scheduling and resource allocation scheme for millimetre wave heterogeneous networks”, in proc. of 2017 IEEE Wireless Communications and Networking Conference (IEEE WCNC 2017), San Francisco, CA, USA, Mar. 2017.

[MIC16] M. Mueck, V. Ivanov, S. Choi et al. “ETSI Spectrum Sharing and Software Reconfiguration Standards”, International workshop on smart wireless communications, Smartcom’2016, Finland, May 2016

[MiWEBA] http://www.miweba.eu/#Start

[MMMAG15-IR31] mmMAGIC IR 3.1 “Requirements, scenarios and use cases", September 2015.

[MMMAG15-IR32] mmMAGIC IR3.2 “Applicability analysis and architectural enablers for mm-wave RAN integration concepts”, December 2016.

[MMMAG16-D31] mmMAGIC D3.1 “Initial concepts on 5G architecture and integration", March 2016.

[MMMAG17-W32] mmMAGIC W3.2 “Architectural enablers and concepts for mm-wave RAN integration", March 2017.

[MMMAG17-D13] mmMAGIC D1.3 “Visualization of the Candidate Radio Interface Concept”, March 2017.

[MDZ+15] Marco Mezzavilla, Sourjya Dutta, Menglei Zhang, Mustafa Riza Akdeniz, and Sundeep Rangan. 2015. 5G MmWave Module for the ns-3 Network Simulator. In Proceedings of the 18th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM '15). ACM, New York, NY, USA, 283-290. DOI: http://dx.doi.org/10.1145/2811587.2811619

[MMZ15] Z. Marzi, U. Madhow, and H. Zheng, "Interference Analysis for mm-Wave Picocells," 2015 IEEE Global Communications Conference (GLOBECOM), San Diego, CA, 2015, pp. 1-6.

[MKT+14] M. Cudak, T. Kovarik, T. A. Thomas, A. Ghosh, Y. Kishiyama, and T. Nakamura, “Experimental mm-wave 5G cellular system,” in IEEE Globecom Workshop, 2014, pp, 377-381.

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[PCW17] Joan Palacios, Paolo Casari, and Joerg Widmer. “Jade: Zero-knowledge device localization and environment mapping for millimeter wave systems.” IEEE Conference on Computer Communications (IEEE Infocom), Atlanta, GA, USA, May 2017.[PDG+16] J. Palacios et al., “Speeding up mmWave beam training through low-complexity hybrid transceivers,” in Proc. IEEE PIMRC, Valencia, Spain, Sep. 2016

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[PDW17] Joan Palacios, Danilo De Donno, and Joerg Widmer. “Tracking mm-wave channel dynamics: Fast beam training strategies under mobility.” In IEEE Conference on Computer Communications (IEEE Infocom), Atlanta, GA, USA, May 2017.

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[PYS15] H. Peng, T. Yamamoto, and Y. Suegara, “LTE/WiGig RAN-level Interworking Architecture for 5G Millimeter-wave Heterogeneous Networks,” IEICE Trans. Commun., vol. E98-B, no. 10, pp. 1957-1968, Oct. 2015.

[RMS+15] T. S. Rappaport, G. R. MacCartney, M. K. Samimi, and S. Sun, “Wideband millimetre-wave propagation measurement and channel modles for future wireless communication system design,” IEEE Transactions on Communications, Vol 63, no 9, pp. 3029-3056, September 2015.

[RMC16] R. Rajavelsamy , M. Choudhary, & D.Das “A Review on Evolution of 3GPP Systems Interworking with WLAN”, Journal of ICT, Vol. 3, September 2016, pp. 133–156.

[RNS+15] I. Rodriguez, H. C. Nguyen, T. B. Sorensen, J. Elling, J. A. Holm, P. Mogensen, and B. Vejlgaard, “Analysis of 38 GHz mmwave propagation characteristics of urban scenarios”, in Proc. Of European Wireless Conference, May 2015, pp, 1-8.

[SCUM15] [16] I. Siaud, N. Cassiau, A.M. Ulmer-Moll, M.A Bouzigues, “Adaptive and Spatial Processing for Millimeter Wave Backhaul Architectures”, 15th IEEE International Conference on Ubiquitous Wireless Broadband, ICUWB’15, Montreal, October 2015

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[SSF16] M. Soszka, M. Simsek, and G. Fettweis, “On Link Combining Methods for Highly Reliable Future Wireless Communication”, ISWCS 2016.

[SUM11] I. Siaud, A.M. Ulmer-Moll, 'Interleaving based Resource Allocation Algorithms for Multi Gigabit Wireless OFDM Systems', IWCLD 2011 workshop, Rennes-France

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[SUMP16] I. Siaud, A. M. Ulmer-Moll, H. Peng, S. Nanba and K. Moriwaki, “C/U-plane splitting architectures and Inter-RAT management for Radio Reconfigurable Systems”, ETSI workshop on future radio technologies-air interfaces, http://www.etsi.org/news-events/events/1005-workshop-on-future-radio-technologies-air-interfaces, January 2016.

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[TVS+16] M. Tesanovic, V. Venkatasubramanian, M. Schellmann, J. Bazzi, M. C. Filippou, D. Calabuig, O. Aydin, and C. Kilinc, “Design framework and suitability assessment proposal for 5G air interface candidates” in proc. of the 2016 IEEE Conference on Standards for Communications and Networking (CSCN’16), Berlin, Germany, Nov. 2016.

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https://bscw.5g-mmmagic.eu/pub/bscw.cgi/d187833/mmMAGIC_Architectural_enablers_mmWave_integration.pdf






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8 Annex

8.1 Annex I Section 3.4 RRC_Inactive This is an annex for the Section 3.4.2

Table 8-1 Transition time from “new state” to fully connected state (without context fetch) Compone

nt (NR) Description TTI 0.25ms TTI 0.5ms TTI 1ms Note

1 Average delay due to RACH scheduling period

0.125 0.25 0.5

2 RACH Preamble 0.25 0.5 1

3-4 Preamble detection and transmission of RA

response (Time between the end RACH transmission and UE’s reception of scheduling grant and timing adjustment)

0.75 1.5 3

5 UE Processing Delay (decoding of scheduling

grant, timing alignment and C-RNTI assignment + L1

encoding of RRC Connection Resume

Request)

1.25 1.25 1.25 Considering the greater and faster

processing capabilities of gNB

and UE, 75% reduction in the

processing delay can be assumed in

next generation radio

6 Transmission of RRC Connection Resume

Request

0.25 0.5 1

7 Processing delay in gNB (L2 and RRC)

1 1 1 Considering the greater and faster





8 Transmission of RRC Connection Resume (and

UL grant)

0.25 0.5 1

9 Processing delay in the UE (L2 and RRC)

3 3 3 Considering the greater and faster






Complete

0.25 0.5 1

Total delay [ms] 7.125 9 12.75




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Table 8-2 Categorization of non-ideal backhaul [PDG+16] Backhaul Technology Latency (One way) Throughput Priority (1 is the highest)

Fiber Access 1 10-30ms 10M-10Gbps 1

Fiber Access 2 5-10ms 100-1000Mbps 2

DSL Access 15-60ms 10-100 Mbps 1

Cable 25-35ms 10-100 Mbps 2

Wireless Backhaul 5-35ms 10Mbps – 100Mbps typical, maybe up to Gbps range

1

Table 8-3 Categorization of good to ideal backhaul [PDG+16]

Backhaul Technology Latency (One way) Throughput Priority (1 is the highest)

Fiber 2-5ms 50M-10Gbps 1

Table 8-4 Transition time from “new state” to Connected (with context fetch) Component

(NR) Description TTI 0.25 ms TTI

0.5 ms

TTI 1 ms Note

1 Average delay due to RACH scheduling period

0.125 0.25 0.5

2 RACH Preamble 0.25 0.5 1

3-4 Preamble detection and transmission of RA

response (time between the end RACH

transmission and UE’s reception of scheduling

grant and timing adjustment)

0.75 1.5 3

5 UE processing delay (decoding of scheduling

grant, timing alignment and C-RNTI assignment + L1

encoding of RRC Connection Resume

Request)

1.25 1.25 1.25 Considering the greater and faster processing

capabilities of gNB and UE, 75% reduction in the processing delay can be

assumed in next generation radio


Request

0.25 0.5 1

7 Processing delay in gNB (Uu -> X2-C)

1 1 1 Considering the greater and faster processing



8 X2 Context Retrieval Transfer delay

5/30 5 / 30 5/30 The transferring delay over X2 depends on the

backhaul deployment, the average latency from

table 3 and table 4 are used.

9 Processing delay in gNB (including the UE context

retrieval)

10 10 10

10 X2 Context Retrieval Transfer delay

5/ 30 5/30 5/30 The transferring delay over X2 depends on the

backhaul deployment, the average latency from

table 3 and table 4 are used.




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11 Processing delay in gNB (X2-C->Uu)




12 Transmission of RRC Connection Resume (and

UL grant)

0.25 0.5 1

13 Processing delay in the UE (L2 and RRC)





Complete

0.25 0.5 1

Total delay (ms) 28.125/78.125 30/80 33.75/83.75

8.2 Annex II: Control plane multi-connectivity – RRC diversity – simulation assumptions

Table 8-5 Simulation assumptions for RRC diversity simulations

Duration of the simulation [s] 10

Interval of sending RRC measurement reports [ms]

9,86

Number of UE drops 10

Number of UEs in each and every drop 84

MeNB technology LTE

SeNB technology 5G

MeNB center frequency [MHz] 2000

SeNB center frequency [GHz] 28

RSRQ A4 event SCell addition threshold [dB] -15

RSRQ A4 event SCell addition hysteresis [dB]

3

RACH to SCell procedure time [ms] 12

Time required for adding SCell procedure [ms]

79

RRC measurement report size [bits] 120

Number of MeNB base stations 21

Number of SeNB base stations 84

8.3 Dynamic evaluations – Outdoor comparison As could be seen in the section Dynamic mobility evaluations 3.1.2.2 the NR standalone system had problems with the high radio frequencies in the 3GPP defined scenario. To get better understanding of limitations of the standalone system and the tight integration aspects, evaluations where done on a scenario similar to 3GPP dense urban with the difference of having only outdoor users. The results from these simulations can be seen in Figure 8-1. In the outdoor




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scenario the NR standalone system performs much better than before and the 10th percentile users reach well above the 50 Mbps target also for high loads, something the LTE standalone system fails to do even at medium loads. The non-standalone system provides a throughput gain also in this scenario but not more than the sum of the two standalone systems. This is because most users experience good radio conditions, thus operating in dual connectivity mode and already using the available resources in the networks efficiently. This can also be seen by looking at the retransmissions where there is no real difference between the non-standalone and NR standalone systems, the later already experiencing a low percentage of retransmissions. The conclusion to be drawn is that the NR standalone system fares well in an outdoor scenario where only small gains (relative to the 80% indoor scenario) can be achieved by aggregation with an LTE system.

Figure 8-1 Simulation results with only outdoor users in a 3GPP dense urban scenario with LTE at 3.5 GHz standalone, NR at 26 GHz standalone, and non-standalone deployment with LTE at 3.5 GHz and NR at 26 GHz (a) 10%-ile throughput; (b) Comparisons of average number of DL

transmissions between DC LTE-NR and NR only.

8.4 Annex IV: Distribution of inter-cell interference and signal-to-interference ratio in a representative indoor mm-wave scenario

As an representative scenario for evaluation of inter-cell interferences, let us consider two rooms of equal dimensions, separated by an internal wall containing a glass window, with a total area of 20x10 m2 and 3 m height. One of the rooms has two wooden tables and a brick column acting as reflectors, and is assumed to contain an indoor interfering node. The other room has no furniture for simplicity, and contains an indoor serving node ideally beamforming its main lobe towards the user device. The material of the internal wall is plasterboard, while the surrounding walls are made of concrete. Further details are given in [MMMAG15-IR32].

The results on the desired signal power and interference power, as obtained by Volcano Lab tool, are provided in the next figures.




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Figure 8-2 Desired signal received power level at 10 GHz (top), 30 GHz (middle), and 70 GHz (bottom). Vertical dimension corresponds to y axis, and horizontal dimension to x axis.

a)

b)

c)

d)




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e)

f)

g)

h)

i)

j)

k)

l)

Figure 8-3 Interfering signal received power levels at 70 GHz, according to several representative positions and orientations of the interfering node. Vertical dimension

corresponds to y-axis, and horizontal dimension to x-axis.

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