recent advances in wireless small cell networks

Recent Advances in

Wireless Small Cell Networks

Mehdi Bennis and Walid Saad

http://www.cwc.oulu.fi/~bennis/ [email protected]

University of Oulu, Centre for Wireless Communications, Finland

Electrical and Computer Engineering Department, University of

Miami, USA

http://resume.walid-saad.com [email protected]

1

http://www.cwc.oulu.fi/~bennis/

http://resume.walid-saad.com/



Outline

• Part I: Introduction to small cell networks

– Introduction and key challenges

• Part II: Network modeling & analysis

– Baseline models and key tools (stochastic geometry)

• Part III: Interference management

– Interference in a heterogeneous, small cell environment

– Emerging techniques for interference management

• Part IV: Toward self-organizing small cell networks

– Introduction to game theory and learning

– Applications in small cells

• Part V: Conclusions and open issues

2

Part I

Introduction to Small Cell Networks

3

Outline

4/120

Outline

5/120

What happens in one hour?

Around the globe, in one hour:

– 685 million sms messages

– 128 million Google searches

– 9 million tweets

– 1.2 million mobile apps downloaded

– 2880 hours of YouTube videos uploaded

– 50,000 smart phones activated

We need innovative network designs to handle all of this!

6

Technology Convergence

Wireless services

Digital imaging

TV and video

Computing

Gaming

7

8

Main Implications

• Operators dilemma

– Meet the demand and maintain low costs (i.e., revenues an issue)

• Need to decrease the expenditure per bit of data (to avoid

uglier alternatives such as limiting usage)

• Solutions that have been explored in the past few years

– Multiple antenna systems and MIMO

• Cannot provide order of magnitude gains

• Scalability and practicality issues

– Cognitive radio

• Availability of white spaces in major areas at peak hours is questionable

• MIMO and Cognitive radio will stay but must co-exist along

with better, more scalable, and smarter alternatives

• Is there any better, cost-effective solution?

• Operators face an unprecedented increasing demand for mobile

data traffic

• 70-80% volume from indoor & hotspots already now

• Mobile data traffic expected to grow 500-1000x by 2020

• 1000-times mobile traffic is expected in 2020 to 2023

• Sophisticated devices have entered the market

• Increased network density introduces Local Area and Small Cells

• In 2011, an estimated 2.3 million femtocells were already deployed

globally, and this is expected to reach nearly 50 million by 2014

• Explosive online Video consumption

• OTT are on the rise (20% of internet traffic carried out by

NETFLIX and the likes)

Small Cell Networks – A Necessary Paradigm Shift

Macrocell

Small Cells/Low power Nodes

Consumer behaviour

is changing

- More devices, higher

bit rates, always active

- Larger variety of

traffic types e.g. Video,

MTC

9

Facts

Ultimately, the only viable way of reaching “the 1000X”

paradigm is making cells smaller, denser and smarter

• Heterogeneous (small cell) networks operate on licensed spectrum owned by the

mobile operator

• Fundamentally different from the macrocell in their need to be autonomous and self-

organizing and self-adaptive so as to maintain low costs

• Femtocells are connected to the operator through DSL/cable/ethernet connection

• Picocells have dedicated backhauls since deployed by operators

• Relays are essentially used for coverage extension

• Heterogeneous (wired,wireless, and mix) backhauls are envisioned

• Operator-Deployed vs. User-deployed

• Residential, enterprise, metro, indoor,outdoor, rural

Solar panel

@ London’s

Olympics Games Lamp Post Hotpost

10

In a nutshell….

11

In a nutshell….

Macro-BS

Wired

backhaul

wired

Wireless

backhaul

Relay

Femto

Pico

bzzt!

lamppoles

3G/4G/WiFi

Characteristics

• Wireless backhaul

• Open access

• Operator‐deployed

Major Issues

• Effective backhaul design

• Mitigating relay to macrocell

interference

Characteristics

• Wired backhaul

• Operator‐deployed

• Open access

Major Issues

•Offloading traffic from

macro to picocells

• Mitigate interference

toward macrocell users

Characteristics

• Wired backhaul

• User-deployed

• Closed/open/hybrid

access

Major Issues

• Femto-to-femto

interference and femto-to-

macro interference

Characteristics

• Resource reuse

• Operator‐assisted

Major Issues

• Neighbor discovery

• Offloading traffic

D2D

Macrocells: 20-40 watts (large

footprint)

MBS SBS

MBS

MBS SBS

Macro-only Macro +

small cell

(single flow)

Multi-flow or

soft-cell (///)

MBS SBS

A mix

HetNets – Leveraging the spatial domain

coordination

coordination

coordination

DOCOMO’s View (the CUBE)

Reference: METIS

• Small Cell Forum (formerly Femto-Forum) is a governing

body with arguably most impact onto standardization bodies.

• Non-profit membership organization founded in 2007 to

enable and promote small cells worldwide.

• Small Cell Forum is active in two main areas:

1) standardization, regulation & interoperability;

2) marketing & promotion of small cell solutions

Next Generation Mobile Networks (NGMN) Alliance:

• Created in 2006 by group of operators

• Business requirements driven

• Often based on use‐cases of daily networking routines

• Heavily related to Self-Organizing Networks (SON) activities 14

Standardization Efforts

• Three access policies

• Closed access:

only registered users belonging to a closed subscriber group (CSG) can

connect

Potential interference from loud (macro UE) neighbors

• Open access:

all users connect to the small cells (pico/metro/microcells)

Alleviate interference but needs incentives for users to share their access

• Hybrid access:

all users + priority to a fixed number of femto users

Subject to cost constraints and backhaul conditions

• Femtocells are generally closed, open or hybrid access

• Picocells are usually open access by nature and used for offloading macrocell

traffic and achieving cell splitting gains.

15

Small Cell Access Policies

• Recent trials using a converged

gateway Wi-Fi/3G architecture

showed how the technologies

could be combined and exploited

• Several companies are likely to

simultaneously introduce both

technologies for offloading.

- Deployed to improve network coverage and

improve capacity (closed access)

- There is considerable planning effort from the

operator in deploying a femtocell network

- Prediction: there will be more small cells than

devices! (Qualcomm CTW 2012)

- A cheap alternative for data offloading

- Availability of Wi-Fi networks, high data rates

and lower cost of ownership has made it

attractive for catering to increasing data demand

- However, seamless interworking of Wi-Fi and

mobile networks are still challenging

Open Problem

How to combine and integrate 3G/4G/Wi-Fi in a cost effective manner? 16

Small cells vs. Wi-Fi:

- Managed vs. Best effort

- Simultaneously push

both technologies for

offloading

Small Cells vs. WiFi Friends or Foes?

• The backhaul is critical for small cell base stations

• Low-cost backhaul is key!

• What is the best solution?

• Towards heterogeneous small cell backhaul options

• Conventional point-to-point (PtP):

• high capacity

• coverage, spectrum OPEX, high costs

• E-band (spectrum available at 71-76 and 81GHz)

• high capacity

• high CAPEX and OPEX

• Fiber (leased or built)

• high capacity

• recurring charges, availability and time to deploy

• Non-Line of sight (NLOS) multipoint microwave

• good coverage, low cost of ownership

• low capacity, spectrum can be expensive

+ possibly TV White Space...

Milimeter-wave backhaul currently a strong potential

Proactive caching ~30-40% savings (more on this later)

Sub 6 GHz Point-to-Multipoint Backhaul Links 17

The Backhaul – a new bottleneck

18

Radio resource management and Inter-cell

interference coordination

Intra-RAT offloading, inter-RAT offloading

(tighter coordination)

Cell association and

load balancing

Handover and mobility management

Backhaul-aware RRM for small cell networks

Self-organization, self-optimization

Self-healing

Security

Energy Efficiency and

power savings (green small cells)

Modeling and analysis

And many more..

Summary of Challenges

19

Summary of Challenges

• Dense and ad hoc deployment -> new network models

• How to manage interference?

– Key to successful deployment of small cells

• How can we design the small cells in a way to co-exist with the

mainstream wireless system?

– Most critically, mobility and handover

• What is the best backbone to support the small cells?

– Small cells’ performance can be degraded when the backhaul is being

used by other technologies (e.g. WiFi or home DSL)

• How can we handle dense deployments?

• What about energy efficiency?

• Ultimately, can we have a multi-tier wireless network that is

built in a plug-and-play manner?

Challenges in SCNs – Radio Resource Management and Inter-cell interference coordination

Macro-BS

Small cell UE

Small cell BS

Macro UE

Macro UE inside / near femto coverage

• DL interference from the small cell BS to nearby Macro UE

• A Macro UE far from its MBS will be affected the most

Macro-BS

Small cell UE

Small cell BS

Macro UE

• UL interference from nearby macro UE to small cell BS

• A macro UE far from its MBS causes interference toward the

small cell

Aggressor/Victim: small cell/macro Aggressor/Victim: macro/small cell

20

DL UL

Macro-BS

Small cell UE

Small cell BS

Macro UE

Small cell very close to Macro base station

• DL interference from nearby Macro-BS to small cell UE

• Interference from nearby Macro-BS can lower SINR of

small cell UE

• UL interference from small cell UE to nearby Macro-BS

• Many active small cell UEs can cause severe interference to the

Macro-BS

Macro-BS

Small cell UE

Small cell BS

Macro UE

Aggressor/Victim: macro/small cell Aggressor/Victim: small cell/macro

21

DL UL


Macro-BS

Small cell BS

Macro UE

(co-tier) interference among small cell networks

• DL interference among nearby small cell networks • UL interference among nearby small cell networks

Aggressor/Victim: small cell/small cell

Small cell BS

Macro-BS

Small cell BS

Macro UE


Small cell BS

22

DL UL


• UE mobility is faster than the HO parameter settings

• HO triggered when the signal strength of the source cell is too low

Too late HO Too early HO Wrong cell HO

Mobility enhancement for

traffic offloading

Enhancement of small cell discovery is

needed for offloading to small cells

standard macrocell HO parameters are

obsolete

SON enhancements for HetNet

How to control mobility with SON

features needs to be studied?

How long to wait ? What is the

threshold? etc

disruptive to standard scheduling

Need for context-awareness

Macro LPN LPN LPN

23

Challenges in SCNs –

Mobility management and handover

Standard macrocell HO parameters are obsolete

all UEs typically use same set of handover parameters (hysteresis margin and

Time-to-Trigger TTT) throughout the network

- When does a network hands off users as a function of interference, load, speed,

overhead?

- UE-specific and cell-specific handover parameter optimization (e.g., using variable

TTTs according to UE velocity), and applying interference coordination (for high

speed UEs), etc.

- Develop mathematical models and tools that enable detailed analysis of capacity and

mobility in HetNets w/o cumbersome Monte-Carlo pointers to operators

- Interrelated with enhanced ICIC solutions + inter-RAT offloading

- Note that traditionally ICIC and Mobility are treated separately bad!

Macro-2 SBS-1 SBS-2

SBS-3

Macro-1 MUE-1 MUE-2


Mobility and Load Balancing in HetNets

MBS

- Deal with asymetric traffic in DL and UL

- Tackle BS-to-BS interference and UE-to-UE interference (among others)

- Possible options are possible: (i)- adopt same DL/UL duplexing among far away

small cells, or (ii)- different duplexing method among clusters of small cells with

strong coupling.

- Potential gain by alternating between small cell DL and UL+ doing interference

mitigation.

UL

DL DL UL

interference

signal

UL

BS-to-BS interf.

UE-to-UE interf.


Flexible UL/DL for TDD-based Small Cells

SON is crucial for enhanced/further enhanced-ICIC, mobility

management, load balancing, etc.. 26

• Traditional ways of network optimization using

manual processes, staff monitoring KPIs, maps,

trial and errors ..........is unreasonable in SCNs!

• Self-organization and network automation is a

necessity not a privilege. Why?

• Femtocells (pico) are randomly (installed)

deployed by users (operators)

need fast and self-organizing capabilities

• Need strategies without human intervention

• Self-organization helps reduces OPEX

• Homogeneous vs. Heterogeneous deployments

every cell behaves differently

Individual parameter for every cell

• Ongoing discussions on pros/cons of Centralized-

SON, Distributed-SON and Hybrid-SON?

Challenges in SCNs – Self-Organizing Networks (SONs)

• Green communications in HetNets requires redesign at each level. Why?

• Simply adding small cells is not energy-efficient (need smart mechanisms)

• Dynamic switch ON/OFF for small cells

• Dynamic neighboring cell expansion based on cell cooperation

Macro-BS Macro-BS

Small

cell

Small

cell

Dynamic cell ON/OFF

Active Mode

Switch OFF

Switch OFF for power savings Cell range expansion

Dynamic neighboring

cell expansion

Energy harvesting is also a nice trait of HetNets! e.g., autonomous network configuration properties

converting ambient energy into electrical during sleep mode 27

Challenges in SCNs – Energy Efficiency

Part II

Nework Modeling & Analysis in

Small Cell Networks

28

Developing analytically tractable models for cellular

systems is very difficult

• Stochastic Geometry (StoGeo) has been used

in cellular networks with hexagonal base

station model, i.e., macrocell base stations

(grid-based).

With advent of heteregeneous and dense small cell

networks, random and spatial models are needed

• Hexagonal models fairly obsolete

• Need to model HetNets to characterize

performance metrics (Operators want pointers!!)

• Transmission rate, coverage, outage

probability tractability

• Ease of simulation

Wyner model was predominantly used in the 1990’s

• Too idealized; used in Information Theory (IT)

• used in Academia for tractability and analysis

29

Current Cellular Models

Source: J. Andrews, keynote ICC Smallnets, 2012.

• How to model and

analyze multi-tier

wireless networks?

• How to characterize

interference?

• How to derive key

metrics such as coverage

probability, spectral

efficiency etc?

Nuts and Bolts

30

Current Cellular Architectures

Aggregate interference at tagged receiver

......First, let us look at the coverage probability in a 1-tier setting

coverage

probability

31

Baseline Downlink Model (1-tier)

Coverage Probability (1-tier)

Where

Incredibly simple expressions

32 Source: J. Andrews, keynote ICC Smallnets, 2012.

How accurate is this model?

• Fairly accurate, even for

traditional planned

cellular networks.

• Yet, industry is

somewhat reluctant to

use these models due to

possible difficulty in

system level simulations

• Trend is changing for 5G 33

Moving on to K-tier Hetnets

Aggregate interference at tagged receiver 34

K-Tier Small Cell Networks

Theorem 2 [Dhillon, Ganti, Bacelli ’11]: The coverage probability for a typical

mobile user connecting to the strongest BS, neglecting noise and assuming Rayleigh

fading:

Key assumption!

• Single tier cellular network (K=1):

Only depends on SIR target and path loss

• K-tier network with same SIR threshold for all tiers (practical?)

Interestingly, same as K=1 tier.

Neither adding tiers nor base stations changes

the coverage/outage in the network!

- Network sum-rate increases linearly with number of BSs 35 Source: J. Andrews, keynote ICC Smallnets, 2012.

How accurate is the K-tier model?

Source: J. Andrews, keynote ICC Smallnets, 2012. 36

Summary

• How good is the Poisson assumption?

• Femtocells: deployments fairly random but distribution is known

• Macrocells: have some structure but definitely not grid-like

• Picocells: some randomness due to the deployment at hotspots

• How good is the independence assumption?

• Femtocells: fairly good since users typically don’t know the locations of operator

deployed towers

• Picocells and macrocells: questionable since both are operator deployed

Need novel tools that capture more realistic models in small cell and heterogeneous

networks

Need models that actually incorporate space and time correlation (open problem),

correlation patterns, etc

37

Open Issues in Stochastic Geometry

• Most results assume base stations to transmit all the time;

• untrue in practical systems

• Biasing and cell association and load balancing

• Push traffic toward open access underload picocells

• Achieving cell splitting gains

• Uplink SINR model much harder

• Requires a thorough study

• Interference management, scheduling, MIMO, mobility management and load

balancing

• Take-away messages

• Stochastic Geometry

Most importantly, operators want pointers for their network deployments.

Gradually embraced by industry

38

Part III

Interference Management

39

LTE-A: Goals

• Greater flexibility with wideband deployments • Wider bandwidths, intra-band and inter-band carrier aggregation

• Higher peak user rates and spectral efficiency • Higher order DL and UL MIMO

• Flexible deployment using heteregenous networks • Coordinated macro, pico, remote radio heads, femto, relays, Wi-Fi

• Robust interference management for improved fairness • Better coverage and user experience for cell edge users

bps bps/Hz bps/Hz/km2 Towards Hyper-Dense Networks

40

Inter-cell Interference Coordination in LTE/LTE-A

• LTE (Rel. 8-9)

• Only one component carrier (CC) is available

Macro and femtocells use the same component

carrier

Frequency domain ICIC is quite limited

41

• LTE-A (Rel. 10-11)

•Multiple CCs available

•Frequency domain ICIC over multiple CCs is possible

•Time domain ICIC within 1 CC is also possible

•Much greater flexibility of interference management

Source: Ericsson

ICIC in LTE-A: Overview

• Way to get additional capacity

cell splitting is the way to go

• Make cells smaller and smaller and make the network

closer to user equipments

• Flexible placement of small cells is the way to address

capacity needs

How do we do that?

In Release-8 LTE, picocells are added where users

associate to strongest BS.

Inefficient

Release-10 techniques with enhanced solutions are

proposed

Cell range expansion (CRE)

Associate to cells that ”makes sense”

Slightly weaker cell but lightly loaded

42

e.g., Why not offload the UE to

the picocell ? Source: DOCOMO

Inter-cell Interference Coordination

Time-Domain

ICIC Frequency-

Domain ICIC

Spatial Domain

ICIC

Orthogonal

transmission,

Almost Blank

Subframe, Cell

Range

Expansion, etc

Orthogonal

transmission,

Carrier

aggregation,

Cell Range

Expansion, etc

A combination

thereof +

coordination

beamforming,

coordinated

scheduling, joint

transmission,

DCS, etc

• ICIC and its extensions are study items in SON

43

Inter-cell Interference Coordination -

Time Domain

• Typically, users associate to base

stations with strongest SINR • BUT max-SINR is not efficient in

SCNs

• Cell range expansion (CRE) ?

• Mandates smart resource

partitioning/ICIC solutions

• Bias operation intentionally allows

UEs to camp on weak (DL) pico cells • RSRP = Reference signal

received power (dBm)

• Pico (serving) cell RSRP + Bias

= Macro (interfering) cell RSRP

•Need for time domain subframe partitioning

between macro/picocells

• In reserved subframes, macrocell does not

transmit any data •Almost Blank Subframes (ABS) + duty cycle

Macro

Pico Pico

Limited footprint of pico due

To macro signal

Subframes reserved for macrocell transmission

Macro Pico

Pico

Increased footprint of pico

When macro frees up resources

Subframes reserved for picocell transmission

44

Inter-cell Interference Coordination -

Time Domain • (Static) Time-Domain Partitioning

• Negotiated between macro and

picocells via backhaul (X2)

• Macro cell frees up certain

subframes (ABS) to minimize

interference to a fraction of UEs

served by pico cells

• All picocells follow same pattern

Inefficient in high loads with non-

uniform user distributions

• Duty cycle: 1/10,3/10,5/10 etc

• Reserved subframes used by

multiple small cells

• Increases spatial reuse

• Adaptive Time-Domain Partitioning

• Load balancing is constantly

performed in the network

• Macro and picocells negotiate

partitioning based on

spatial/temporal traffic distribution.

0 1 2 3 4 5

time

6 7 8 9 0 1 2 3 4 5 6 7 8 9

0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9

50% Macro and Pico; Semi-Static

0 1 2 3 4 5

time

6 7 8 9 0 1 2 3 4 5 6 7 8 9

0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9

25% Macro and Pico; Adaptive

Macro DL

Pico DL

Macro DL

Pico DL

Possible

transmission No

transmission

Data

transmission

No

transmission

Data

transmission

#1 Macro Pico

#1

45

Inter-cell Interference Coordination ABS

• Inter-cell interference coordination is necessary for effective femto/pico deployment

• Almost blank subframe (ABS)

• During defined subframes, the aggressor cell does not transmit its control + data

channel to protect a victim cell

• ABS pattern transmitted via X2 (dynamic) for macro/pico

• Macro/pico aggressor/victim

or via OAM (semi static) for macro/femto (=victim/aggressor)

• Issues with the UEs who should know

those patterns + detect weak cells.

• Common reference, sync and primary broadcast

should be protected

• Co-existence of legacy and new devices in pico CRE zone

• Need for enhanced receivers for interference suppression of

residual signals transmitted by macro cells

Macro-BS Small cell UE

Femto BS Aggressor

Macro UE Victim

FBS DL

Macro DL

Data

transmission No TXABS

Macro Pico Legacy device

New device

Example of macro/femto ICIC through ABS 46

f1

MBS

- Push macrocell traffic to picocells through biasing

- Using same biaising parameters for all small cells is bad!

- Need to optimize cell-specific range expansion bias, duty cycle, transmit power

according to traffic, QoS requirements, backhaul and/or deployment costs, etc

- What happens in ultra dense networks with more than 4 Picos per sector (viral

deployment).

- Inside-outside approach where indoor small cells can also help offload traffic.

CRE bias-2

CRE bias-1

Inter-cell Interference Coordination (Recap)

No ICIC CRE results in low

data rate for cell-edge UEs

Fixed CRE b= 6 dB is good

for cell-edge UEs

Fixed CRE b = 12 dB is

detrimental for ER PUEs

(why?)

Mute ABS performs poorly due

to resource under-utilization

On average 125% gain

compared to RP+23% compared

to Fixed CRE b = 12 dB

Inter-cell Interference Coordination –

Case study

Macro-BS MUE-2

MUE-1

SBS

High velocity

SUE-1 SUE-2

Range expansion

1 2 3 4 5 6 7 8 9 10

1 2 3 4 5 6 7 8 9 10

MBS

SBS

Frame duration

1 2 3 4 5 6 7 8 9 10

1 2 3 4 5 6 7 8 9 10

MBS

SBS

Frame duration

1 2 3 4 5 6 7 8 9 10

1 2 3 4 5 6 7 8 9 10

MBS

SBS

Frame

duration

1 2 3 4 5 6 7 8 9 10

1 2 3 4 5 6 7 8 9 10

MBS

SBS

Frame duration

Zero power almost blank subframe (ABS) in 3GPP LTE Release-10

A possible approach for enhancing mobility performance

Reduced power ABS in 3GPP LTE Release-11

generalized ICIC approach that simultaneously improves capacity and mobility.

This is time-domain ICIC+mobility but same

thing can be considered for frequency

Zero-ABS Soft-ABS

Mobility and Load Balancing

(capacity/mobility tradeoffs)

Inter-cell Interference Coordination

• Further enhanced ICIC (f-eICIC) for non-CA based

deployment

• Some proposals:

• At the transmitter side in DL combination of

ABS + power reduction (soft-ABS)

• At the receiver side in DL use of advanced

receiver cancellation

Macro Pico

X2 X2

How to distribute the primary and secondary CCs to

optimize the overall network performance?? 50

Cross scheduling

• Further enhanced ICIC (feICIC) for CA based

deployment

• Several cells and CCs are aggregated

• Up to 5 CCs (100 MHz bandwidth)

• Cross scheduling among CCs is

possible

• Primary CC carrying

control/data information and

rest of CCsc carrying data

and vice-versa

• Greater flexibility for

interference management

Inter-cell Interference Coordination - Frequency Reuse

Protecting cell edge users using FFR

X2

X2

X2

X2

X2

X2

HFR

FFR

SFR

Static FFR vs. Reuse 1 51

• Several configurations exist (full, hard, soft, fractional) frequency reuse

• Requires coordination through message exchange (X2)

• Relative Narrowand Transmit Power Indicator (RNTP) for DL

• High Interference Indicator (HII) for UL

• Interference Overload Indicator (OI) for UL; reactive

• Frequency partitioning in HetNet LTE Rel. 8/9

• Static FFR

• Partition the spectrum into subbands and assign a given subband to a cell in a coordinated

manner that minimizes intercell interference

• E.g., N=1/3 FFR yields improvements in terms of SINR albeit lower spectral efficiency

• Dynamic FFR

• Assignments based on interference levels/thresholds as well as scheduling users based on

CQI from users feedbacks.

Inter-cell Interference Coordination - Carrier Aggregation

• Carrier aggregation is used in LTE-A via Component

Carriers (CCs)

• Macro and Pico cells can use separate carriers to

avoid strong interference

• Carrier aggregation (CA) allows additional flexibility

to manage interference

Macrocells transmit at full power on anchor

carrier (f1) and lower power on second carrier

(f2), etc

Picocells use second carrier (f2) as anchor carrier

Partitioning ratio limited by number of carriers

But trend is changing (multiflow CA/3GPP release-

12) (in some cases) Aggressor is victim and victim is aggressor

CC1 CC2 CC3 CC4 CC5

100 MHz

freq.

CC1 CC2 CC3 Macro

CC1 CC2

CC3 Pico

freq.

CC1

CC2 S

macro pico UE

pico macro UE

aggressor

victim

aggressor

victim

How/when to swap victim/aggressor roles? 52

Co-tier Interference Management

Macro-BS

Macro UE


FBS-2

FBS-1

FBS-3

Resources are assigned by a central controller

More efficient resource utilization than the

distributed approach

Needs extra signaling between the BSs and the

controller

Highly computational

Resources are assigned autonomously by BSs

Less complexity

High signaling overhead

Requires long time period to reach a stable resource allocation

Low resource efficiency

53

• In dense network deployments, femto-to-

femto interference can be severe

• especially for cell edge users

• Assigning orthogonal resources among

neighboring femtocells protects cell edge UEs

albeig low spectral efficiency

• Need dynamic ICIC techniques which are

scalable to accommodate multiple Ues

• Key: Assign primary CCs and secondary CCs

depending on interference map, dynamic

interference mitigation through resource

partitioning

• Centralized vs. Decentralized approaches

- UE makes measurement

- Identifies its interfering neighbors according to a predefined SINR

threshold

• BSs send cell IDs of the interfering neighbors to the central

controller (through the backhaul)

• The central controller maps this information into an interference

graph where each node corresponds to a BS, and an edge

connecting two nodes represents the interference between two BSs

FBS-3

FBS-2

Centralized

controller

FBS-1

#3

#1,3 #2

Interference

Feedback

#3

#2

#1,3

- Using conventional Graph Col + GB‐DFR attains a

significant capacity improvement for cell‐edge Ues

- GB-DFR provides higher throuputs than Graph col.

- Nearly all UEs achieve an SINR exceeding 5 dB

Graph Coloring

5x5 grid case

DL

Focus on F2F

“Graph-Based Dynamic Frequency Reuse in Femtocell Networks,” IEEE VTC 2011 (DOCOMO)

Co-tier Interference Management (Centralized Approach)

Interfering Neighbor Discovery

54

http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5956438



Dynamic interference environment

- Number and position of neighbors change during the

Operation

- Fixed frequency planning is sub‐optimal

Dynamic assignment of resources!

Multi‐user deployment

- Users in same cell experience different interference

conditions

- Resource assignment should depend on UE measurements

to maximize resource utilization

Classify resources according to their foreseen usages

Reserved CC

– Allocated to cell edge UEs

– Protected region

Banned CC:

– Interfering neighbors are restricted to use the RCC

allocated to the victim UE

– This guarantees desired SINR at cell edge UEs

Auxiliary CC:

– Allocated to the UEs facing less interference

– Neighbors are not restricted

– Increases resource efficiency, especially, for the

multi‐user deployments

FBS-3

FBS-2

A B C

A

B

C

FBS-1

FBS-2

FBS-3

C B

A

C

C

Example

“Decentralized interference coordination via autonomous component carrier assignment ,” IEEE

GLOBECOM 2011

Co-tier Interference Management (Distributed Approach)

3 CCs

55


http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6162441&contentType=Conference+Publications&searchWithin%3Dp_Authors%3A.QT.Bharucha%2C+Z..QT.%26ranges%3D2011_2012_p_Publication_Year%26searchField%3DSearch_All

• 5x5 grid model, 40 MHz system bandwidth

• Tradeoff between SINR and user capacity

• Proposed approach has more flexibility in assigning

component carriers according to its traffic

• The proposed approach outperforms the static schemes,

especially for cell edge users.

SINR improvements for users at the cost of lower capacity

Extensions:

Issues with convergence and scalabilities yet to be

addressed

Multi-antenna extension

“Decentralized interference coordination via autonomous component carrier

assignment,” in proc. IEEE GLOBECOM 2011 (DOCOMO)

Co-tier Interference Management (Distributed Approach)

56


Part IV

Toward Self-Organizing

Small Cell Networks

57

Self-Organizing Networks

• Manual network deployment and maintenance

is simply not scalable in a cost-effective

manner for large femtocell deployments

– Trends toward Automatic configuration and

network adaptation

• SON is key for

– Automatic resource allocation at all levels

(frequency, space, time, etc.)

• Not just a buzzword

– It will eventually make its way to practice

Large

picocell

footprint

with fewer

users

Small

picocell

footprint

with more

users 58

The feedback

foundations

The large

system

foundations

The statistical

inference

foundations

The dynamics

foundations

The intelligence

and protocol

foundations

The traffic

foundations

The economic

and legal

foundations

The uncertainty

foundations

The physics

foundations

The security

foundations

The coding

foundations

Physics Game Theory & Learning

Evolutionary Biology

Micro-economics

Queuing Theory

Wireless

Cryptography

Discrete Mathematics

Network Information theory

Free Probability

Random Matrix Theory

Control Theory

Future Communication

Networks

Toward Self-Organization: Tools

We focus on game-theoretic/learning aspects 59

60

Introduction

• What is Game Theory?

– The formal study of conflict or cooperation

– How to make a decision in an adversarial environment

– Modeling mutual interaction among agents or players that are rational

decision makers

– Widely used in Economics

• Components of a “game”

– Rational Players with conflicting interests or mutual benefit

– Strategies or Actions

– Solution or Outcome

• Two types

– Non-cooperative game theory

– Cooperative game theory

• Close cousins: Reinforcement learning

61

Heard of it before?

• In Movies

• Childhood games – Rock, Paper, Scissors:

which one to choose?

– Matching pennies:

how to decide on heads or tails?

• You have witnessed at

least one game-theoretic

decision in your life

Non-cooperative game theory

• Rational players having conflicting interests

– E.g. scheduling in wireless networks

• Often…

– Each player is selfish and wishes to maximize his payoff or

‘utility’

• The term ‘utility’ refers to the benefit that a player can

obtain in a game

• Solution using an equilibrium concept (e.g., Nash), i.e., a

state in which no player has a benefit in changing its strategy

• Misconception: non-cooperative is NOT always competition

– It implies that decisions are made independently without

communication, these decisions could be on cooperation!

62

Nash Equilibrium

• Definition: A Nash equilibrium is a strategy profile s*

with the property that no player i can do better by

choosing a strategy different from s*, given that every

other player j ≠ i .

• In other words, for each player i with payoff function ui

, we have:

• Nash is robust to unilateral deviations

– No player has an incentive to change its strategy

given a fixed strategy vector by its opponents

63

64

Example: Prisoner’s dilemma

• Two suspects in a major crime held for interrogation in separate

cells

– If they both stay quiet, each will be convicted with a minor offence and

will spend 1 year in prison

– If one and only one of them finks, he will be freed and used as a witness

against the other who will spend 4 years in prison

– If both of them fink, each will spend 3 years in prison

• Components of the Prisoner’s dilemma

– Rational Players: the prisoners

– Strategies: Confess (C) or Not confess (NC)

– Solution: What is the Nash equilibrium of the game?

• Representation in Strategic Form

Prisoner’s Dilemma

P2 Not Confess Confess

P1 Not Confess -1,-1 -4,0

P1 Confess 0,-4 -3,-3

Nash Equilibrium Pareto optimal

65

• P1 chooses NC, P2’s best response is C

• P1 chooses C, P2’s best response is C

• For P2, C is a dominant strategy

Design Consideration

• Existence and Uniqueness

Utility of

player 2

given

strategies

of players

1 and 2

Utility of player 1 given strategies of

players 1 and 2

Pareto optimality Nash equilibrium?

-Convexity/concavity of payoff

function

- Best response is standard function

(positivity, monotonicity,

scalability)

-Potential game

66

Non-cooperative Games

• Pure vs. mixed strategies

– Existence result for Nash in mixed strategies (1950)

• Complete vs. incomplete information

• Zero-sum vs. Non zero-sum

• Non zero-sum are games between multiple players

– Two player games are a special case

• Matrix game vs. continuous kernel games

• Static vs. Dynamic

– Evolutionary games

– Differential games

– …..

67

More on NC games

• Refinements on Nash

– To capture wireless characteristics or other stability

notions

• Stackelberg game

– Important in small cell networks due to hierarchy

• Correlated equilibrium

– Useful for coordinated strategies

• Special games

– Potential/Supermodular games (existence of Nash)

• Bayesian games, Wardrop equilibrium

• …..

68

Cooperative Game Theory

• Non-cooperative games describe situations where the

players do not coordinate their strategies

• Players have mutual benefit to cooperate

• Namely two types

– Nash Bargaining problems and Bargaining theory

– Coalitional game

• Bargaining theory

• For both

– Applications in wireless networks are numerous

69

Bargaining Example

Rich Man Poor Man

Can be deemed unsatistifactory

Given each Man’s wealth!!!

Might be a

better scheme ! !

Bargaining theory

and the Nash

bargaining solution!

I can give you 100$ if

and only if you agree

on how to share it

70

Coalitional Games • Definition of a coalitional game (N,v)

– A set of players N, a coalition S is a group of cooperating players

– Worth (utility) of a coalition v

• In general, v(S) is a real number that represents the gain resulting

from a coalition S in the game (N,v)

– User payoff xi : the portion of v(S) received by a player i in coalition S

• Characteristic form

– v depends only on the internal structure of the coalition

• Partition form

– v depends only on the whole partition currently in place

• Graph form

– The value of a coalition depends on a graph structure that connects the

coalition members

71

CF vs. PF

In Characteristic form: the

value depends only on

internal structure of the

coalition

72

Cooperative Games

Class I: Canonical Coalitional

Games

Class II: Coalition Formation Games Class III: Coalitional Graph

Games

1

3

2

4

1

3

4

2

1 3

2

4

- The grand coalition of all users is an optimal structure.

-Key question “How to stabilize the grand coalition?”

- Several well-defined solution concepts exist.

- The network structure that forms depends on gains and costs from cooperation.

-Key question “How to form an appropriate coalitional structure (topology) and

how to study its properties?”

- More complex than Class I, with no formal solution concepts.

- Players’ interactions are governed by a communication graph structure.

-Key question “How to stabilize the grand coalition or form a network

structure taking into account the communication graph?”

Solutions are complex, combine concepts from coalitions, and non-

cooperative games

73/124

• For a general N-player game, finding the set of NEs is

not possible in polynomial time!

• Unless the game has a certain structure

• We talk about learning the equilibrium/solution

• Some existing algorithms

– Fictitious play (based on empirical probabilities)

– Iterative algorithms (can converge for certain classes of

games)

– Best response algorithms

• Popular in some games (continuous kernel games for example)

– Useful Reference

• D. Fundenberg and D. Levine, The theory of learning in games, the

MIT press, 1998.

74

Learning in Games

Learning Algorithms

• Distributed Implementation/Algorithm

– Which information can be collected or exchanged

– How to obtain knowledge and state of system

– How to optimize action/strategy

• Distributed Implementation/Algorithm

– Convergence? Speed? Efficiency?

– Overhead and complexity

(communication/computation/storage)

Observe

Analyze and

learning

Optimize

Adapt Cognitive cycle

- Q-learning, fuzzy Q-learning

-Evolutionary based learning

- Non-regret learning

- Best response dynamics

- Gradient update

75

Examples:

Access Control in Small Cell Networks (Nash game)

User Association in Small Cell Networks (Matching

game)

Cooperative interference management

(Coalitional game)

76

To Open or To Close?

77

FUE FUE

Base Station

Closed access Open access for one FAP

78

To Open or To Close?

• Tradeoff between allocating resources and

absorbing MUEs/reducing interference

• Optimizing this tradeoff depends on the locations of

the MUEs, the number of interferers, etc.

• The choices of the FAPs are interdependent

– If an FAP absorbs a certain MUE, it may no longer be

beneficial for another FAP to open its access

• So, Open or Closed?

– Neither: Be strategic and adapt the access policy

– Noncooperative game!

79

Formally…the Femto Problem

• Consider the uplink of an OFDMA system with

– M underlaid FAPs, 1 FUE per FAP, and N MUEs

– Assuming no femtocell-to-femtocell interference

– An MUE connects to one FAP

– For simplicity, we use subbands instead of subcarriers, i.e., each FAP

has a certain contiguous band that it can flexibly allocate

• Noncooperative game

– Players: FAPs

– Strategies: close or open access (allocate subbands)

– Objective: Maximize the rate of home FUE (under a constraint)

Fraction of subband

allocated by FAP m to MUE n

Coupling of actions in SINR

(next slide)

80

Formally…

• Zoom in on the SINR:

• Game solution: Nash equilibrium

• Does it exist?

– Oh not again

- Coupling of all FAPs actions

- Only MUEs not absorbed by others

are a source of interference

- Discontinuity in the utility function

81

Existence of Nash equilibrium

• Common approaches for finding a Nash equilibrium mostly

deal with nicely behaved functions (e.g., in power control,

resource allocation games, etc.)

– Discontinuity due to open vs. closed choice

• P. J. Reny (1999) showed that for a game with discontinuous

utilities, if

– The utilities are quasiconcave

– The game is better-reply secure, i.e.,

• Our game satisfies both properties => Pure strategy Nash

exists

Non-equilibrium vector

Strategy of an

arbitrary FAP m

82

Simulation results (1)

A mixture of

closed

and open access

emerges at

equilibrium

83

Improved

performance

For the worst-case

FAP (equilibrium

is a more

fair scheme

than all-open)

Simulation results (2)

Access point assignment in small cell networks

84

A macro-cellular wireless

network

A number of small cell base

stations

Different cell sizes

A number of wireless users

seeking uplink transmission

How to assign users to access

points?

More challenging than

traditional cellular

networks

Access point assignment

• The problem is well studied in classical cellular networks but..

– ..most approaches focus on the users point of view only in the presence

of one type of pre-fixed base stations

– Do not account for different cell sizes and offloading

• New challenges when dealing with small cell base stations

• Three decision makers with different often conflicting objectives:

– Small cells who want to ensure good QoS, Improve macro-cell coverage

via offloading (cell range expansion)

– Users that want to optimize their own QoS

– Macro-cells seeking to ensure connectivity

• Can we address the problem using a fresh small cell-oriented

approach?

– “A College Admissions Game for Uplink User Association in Wireless

Small Cell Networks”, IEEE INFOCOM 2014

85

Student A

Access point assignment as a matching game

1- Student A

2- Student B

1- U Miami

2- FIU

How to match students (workers) to colleges (employers)?

How to assign wireless users to access points (SCBS and macro) ?

86 Student B

1- FIU

2- U Miami

1- Student B

2- Student A

87

Simulation results

Performance

advantage

increasing

with the

users density

88

Notes and Future Extensions

• Adapts to slow mobility by periodic re-runs as well as to

quota changes and users leaving or returning

• Can we design a college admissions game that can handle

fast dynamics, i.e., handovers?

– Combine with dynamic games

• How to accommodate traffic and advanced schedulers?

– Use concepts from polling systems and queueing theory

• Ideally, we can build a matching game that enable us to

design heterogeneous networks where assignment is made

based on preferences and service types!

– Explore new dimensions in network design and resource allocation

– Different classes of matching games to exploit

Cooperative Interference Management

• We consider the downlink problem

• Femto access points can form a coalition to share the

spectrum resource (i.e., subchannels), reducing the co-tier

interference

``Cooperative Interference Alignment in Femtocell Networks,'‘ IEEE Trans. on Mobile Computing, to

appear, 2012

Macro base

station

Femto

access

point

f4

Macro

users

f1

f2 f3

m2

m1 Coalition S2

Coalition S1

89

• Coalition formation game model

– Players: Femto access points

– Strategy: Form coalitions

– Value of any coalition

Transmission rate

Interference from

femto access points

not in the same coalition

Interference from

macrocell


90

• Not all femto access points can form coalition, since they may

not be able to exchange coalition formation information

among each other

• Cooperation entails COSTS

• We model it via power for information exchange (more

elaborate models needed)

Macro base

station

Femto

access

point

f4

Macro

users

f1

f2 f3

m2

m1 Coalition S2

Coalition S1


91


Chance of cooperation is small

(information cannot be exchanged

among femto access points)

Many femto access points

can form coalition

Too congested

92

Solution is

co-opetition...

Learning how to self-organize in a

dense small cell network?

M. Bennis, S. M. Perlaza, Z. Han, and H. V. Poor," Self-Organization in Small Cell Networks: A Reinforcement

Learning Approach," IEEE Transactions on Wireless Communications 12(7): 3202-3212 (2013)

93

http://www.informatik.uni-trier.de/~ley/db/journals/twc/twc12.html#BennisPBHP13

Femtocell networks aim at increasing spatial reuse of spectral resources, offloading,

boosting capacity, improving indoor coverage

• BUT inter-cell/co-channel interference Need for autonomous ICIC, self-

organizing/self-configuring/self-X interference management solutions to cope with

network densification

• Many existing solutions such as power control, fractional frequency reuse

(FFR), soft frequency reuse (SFR), semi-centralized approaches …

We examine a fully decentralized self-organizing learning algorithm based on local

information, robust, and without information exchange

•Femtocells do not know the actions taken by other femtocells in the network

•Focus is on the downlink

•Closed subscription group (CSG)

•No cross-tier nor co-tier cooperation + No carrier aggregation (no leeway !!)

Toward Evolved SON

94

Due to their fully-decentralized nature, femtocells need to:

- Estimate their long-term utility based on a feedback (from their UEs)

- Choose the most appropriate frequency band and power level based on the accumulated

knowledge over time (key!)

- A (natural) exploration vs. exploitation trade-off emerges;

i. should femtocells exploit their accumulated knowledge OR

ii. explore new strategies?

- Some reinforcement learning procedures (QL and its variants) implement (i)-(ii) but

sequentially

- Inefficient

- Model-based learning.

Solution (in a nutshell)

Proposed solution is a joint utility estimation + transmission optimization where

the goal is to mitigate interference from femtocells towards the macrocell network

+ maximize spatial reuse

• (i)-(ii) are two learning processes carried out simultaneously!

• Every femtocell independently optimizes its own metric and there is no

coupling between femtocell’s strategies (correlation-free);

• for correlation/coordination other tools are required

95

..”Behavioral” Rule..

- History

- Cumulated

rewards

Play a given

action

Ultimately,

maximize the

long-term

performance

...

FBS

Should i explore? Should i exploit?

96

Basic Model

Maximize the long-term transmission rate of every

femtocell (selfish approach)

SINR of MUE

SINR of FUE

97

• The cross-tier interference management problem is

modeled as a strategic N.C game

• The players are the femto BSs

• The set of actions/strategies of player/FBS k is the

power allocation vector

• The utility/objective function of femtocell k

• Rate, power, delay, €€€ or a combination thereof Here transmission rates are considered

• At each time t, FBS k chooses its action from the finite

set of actions following a probability distribution:

Game Model

98

• Femtocells are unable to observe current and all previous actions

• Each femtocell knows only its own set of actions.

• Each femtocell observes (a possibly noisy) feedback from its UE

• Balance between maximizing their long-term performance AND

exploring new strategies-----------okay but HOW?

• A reasonable behavioral rule would be choosing actions yielding

high payoffs more likely than actions yielding low payoffs, but in any

case, always letting a non-null probability of playing any of the

actions

• This behavioral rule can be modeled by the following probability

distribution:

(x)

Entropy/Perturbation

Information Aspects

Maximize the long-term

performance utility +

perturbation 99

• At every time t, every FBS k jointly estimates its long-term utility function and

updates its transmission probability over all carriers:

Other SON variants can be derived in a similar way

Both procedures are

done simultaneously!

Utility

estimation

Strategy

optimization

This algorithm converges to

the so-called epsilon-close Nash

!!!

Players learn their utility faster than the

Optimal strategy

Proposed SON Algorithm

Learning

parameters

100

First scenario 2 MUEs, 2 RBs, K=8

FBSs

Convergence of SON 1 learning algorithms with respect to the Best NE.

The temperature parameter has a considerable impact on the performance

Parameters

Macro BS TX power

Femto BS TX power

Numerical Results

•The larger the temperature parameter is,

the more SON explores, and the

algorithm uses more often its best

transmission configuration and

converges closer to the BNE.

•In contrast, the smaller it is, femtocells

are more tempted to uniformly play all

their actions

101 Altruism vs. Selfishness

Myopic vs. Foresighted

Second scenario

• 6 MUEs, 6 RBs, K=60 FBSs

Average femtocell spectral efficiency vs. time for SON and best response learning algorithm

0 1 2 3 4 5 6 7 8 9 101.5

1.6

1.7

1.8

1.9

2

2.1

2.2

Convergence Time x 1000

Avera

ge S

pectr

al E

ffic

iency (

bps/H

z)

SON1

SON2

SON3

SON1 SON-RL

SON2:

SON1(+imitation)

SON3:

Best response

- no history

- myopic (maximize

performance at every

time instant)

SON1 outperforms SON2 and SON3

Being foresighted yields better performance in the long term

Numerical Results

102

Now, let us add some implicit

coordination among small cells

M. Bennis et al. ”Learning Coarse correlated equilibria in small cell networks," IEEE International Conference

on Communications (ICC), Ottawa, Canada, June 2012.

103

© Centre for Wireless Communications, University of Oulu

The cross-tier interference management problem is modeled as a

normal-form game

At each time instant, every small cell chooses an action from its

finite set of action following a probability distribution:

The Cross-Tier Game

104


(Classical) Regret-based learning procedure

105

Player k would have obtained a higher performance

By ALWAYS playing action

e.g.,


Given a vector of regrets up to time t,

Every small cell k is inclined towards taking actions yielding

highest regret, i.e.,

Regret-based Learning

106

..From perfect world to reality...

In classical RM, each small cell knows the explicit expression of its utility function

and it observes the actions taken by all the other small cells full information

Impractical and non scalable in HetNets


• Remarkably, one can design variants of the classical regret

matching procedure which requires no knowledge about other

players’ actions, and yet yields closer performance. How?

• (again) trade-off between exploration and exploitation,

whereby small cells choose actions that yield higher regrets

more often than those with lower regrets,

– But always leaving a non-zero probability of playing any of

the actions (perturbation is key!)

Regret-based Learning

107


The temperature parameter represents the interest of small

cells to choose other actions than those maximizing the regret,

in order to improve the estimation of the vector of regrets.

The solution that maximizes the behavioral rule is:

Exploration vs. Exploitation

108

Boltzmann

distribution Always positive!!

Decision function mapping past/history + cumulative regrets into future


Numerical Results

109

0.2 0.4 0.6 0.8 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

femtocell density in %

Aver

age

fem

toce

ll s

pec

tral

l ef

fici

ency

[bps/

Hz]

reuse 1

reuse 3

SON-RL; [Bennis ICC'11]

regret-based

Average femtocell spectral efficiency versus the density of

femtocells for SON learning algorithms.

2X increase

• Can small cells self-organize in a decentralized manner?

Yes!

- no information exchange

- solely based on a mere feedback

- Robust to channel variations and imperfect feedbacks

- No synchronization is required unlike some other learning algorithms!

•Numerous tradeoffs are at stake when studying self-organization

•Open Issues:

•How to speed up convergence?

•Introduce QoS-based equilibria?

•Optimality is not always what operators want!!

Take Home Message

110

”When Cellular Meets WiFi in Wireless

Small Cell Networks”

M. Bennis, M. Simsek, W. Saad, S. Valentin, M. Debbah, "When Cellular Meets WiFi in Wireless Small Cell

Networks," IEEE Commun. Mag., Special Issue in HetNets, Jun. 2013.

111

MBS

Goal: A cost effective integration of small cells and WiFi! (dual-mode)

- Distributed cross-system traffic steering framework is needed, whereby SCBSs leverage the

(existing) Wi-Fi component, to autonomously optimize their long-term performance over

the licensed spectrum band, as a function of traffic load, energy expenditures, and users’

heterogeneous requirements.

- Different offloading policies/KPIs: (i)-load based, (ii)-coverage based, and (iii)-a mix + Account

for operator-controlled and user-controlled offloading.

- Leverage more contextual information: Offloading combined with long-term scheduling +

users’ contexts

Backhaul

LTE/WiFi (access level)

LTE/WiFi (backhaul level)

Cellular-WiFi Integration aka Inter-RAT Offloading


The cross-system learning framework is composed of the

following interrelated components:

• Subband selection, power level allocation, and cell range

expansion bias:

– Every SCBS learns over time how to select appropriate

sub-bands with their corresponding transmit power levels

in both licensed and unlicensed spectra, in which delay-

tolerant traffic is steered toward the unlicensed spectrum.

– Besides, every SCBS learns its optimal CRE bias to offload

the macrocell traffic to smaller cells.

• Context-Aware scheduling: Once the small cell acquires its

subband, the scheduling decision is traffic-aware, taking into

account users’ heterogeneous QoS requirements (throughput,

delay tolerance, and latency).

Cross-System Learning (in a nutshell)


Numerical Results

•SCBSs are uniformly distributed within each macro sector, while

considering a minimum MBS-SCBS distance of 75 m.

•The path-loss models and other set-up parameters were selected

according to 3GPP recommendations for outdoor picocells (model 1)

•NUE = 30 mobile UEs were dropped within each macro sector out

of which N_hotspots = 2/3 N_UE /K are randomly and uniformly

dropped within a 40 m radius of each SCBS, while the remaining UEs are

uniformly dropped within each macro sector.

•The traffic mix consists of different traffic models following the

requirements of NGMN

•The bandwidth in the licensed (resp. unlicensed) band is 5 MHz (resp. 20

MHz). The simulations are averaged over 500 transmission time intervals

(TTIs).


Numerical Results

Convergence of the cross-system learning algorithm vs. standard

independent learning

Oscillations


Total cell throughput

vs. number of users.

per UE throughput as a

function of the number of UEs.

• While in the macro-only case, cell edge UEs get low throughput gains, adding K = 2 small cells is shown to boost users’ cell

edge throughput under “HetNet” offload

• 50% increase in cell edge UE throughput with K = 2 multimode small cells “HetNet+WiFi”.

• small cell users benefit from the small cells’ multimode capability (K = 2 SCBSs) + gap further increases when adding more

small cells (K = 6 SCBSs).

• Offloading is shown to improve not only the performance of SCUEs, but also MUEs, for K = {2, 4, 6} SCBSs.

What’s next? –Recent Trends

Source: Ovum, April 2013

Average Daily Mobile Messaging Volumes

Mobile Non-Cloud Traffic

Mobile Cloud Traffic

Mobile Data Traffic Mobile Non-Cloud vs. Cloud Traffic

WhatsApp

Billions of Users — No Interoperability Between Services

800M 175M 250M 300M 100M 100M 1.06B 1B+

Wireless Fabric Social Fabric

People

Sensors & Machines

Social relationships &

ties Social Influence

Crowd-Place sourcing

Storage

Caching Apps &

Contexts

Emotions

GPS Foursquare

LBS

Cloud computing

SDN Energy

Multidimensional Big Data Analytics

Increasingly multi-dimensional complex networks

119

Toward Context-Aware Networks

• We can show

that using

context data

can

significantly

improve the

performance of

wireless

networks

• Foundations of

context-aware

wireless

systems

When Social Meets Wireless

• Offline social network: use stable social relationships to offload data traffic in the OffSN

• OnSN: probability that same content is requested

• “Exploring Social Ties for Enhanced Device-to-Device Communications in Wireless Networks”, IEEE GLOBECOM 2013

120

121


• If a mobile user downloads a certain content,

what is the likelihood that his “social friend”

will request the same content?

– Indian buffet process!

122


• Customers => mobile users

• Content => the dishes, relationship => social

• We can “predict” who will share content =>

improve D2D performance and traffic offload

• Problem: Limited backhaul capacity, data- and control- plane separation

• Proposal: Proactive caching for 5G networks

• How?

• Leverage users’ predictable demands, storage, and social relationships to offload

the backhaul and minimize peak demand.

• Proactively caching strategic contents at the network edge (BSs and UEs) yields

significant gains.

•Tools

• Supervised and unsupervised Machine learning

• Clustering communities, classification and regression

• Predicting social ties and influence within a social community

• Learning and influencing users and contents over large graphs

Proactive Caching for 5G

It’s time to render

our networks more

intelligent than

EVER before!

Proactive Caching for 5G

"Social and Spatial Proactive Caching for Mobile Data Offloading", IEEE International Conference on

Communications (ICC), 2014.

”Exploring Social Networks for Optimized User Association in Wireless Small Cell Networks with Device-to-

Device Communications”, IEEE Wireless Communications and Network Conference (WCNC), 2014

MBS

LTE/WiFi

SBS-1

SBS-2

5G Leverage Context/Content/Social

Demands/interactions

Understand users’

behavior, demands,

etc

Need a framework

that is context-aware,

assesses users’

current situation and

be anticipative by

predicting required

resources,

Anticipate

disruptions, outages,

etc

Networked Society

Internet of everything!

•Classical networking paradigm have been restricted to physical layer aspects overlooking aspects related to users’

contexts, user ties, relationships, proximity-based services

• Traditional approaches are unable to differentiate individual traffic requests generated from each UE’s application

=> does not take advantage of devices “smartness”

•Urgent need for a novel paradigm of predictive networking exploiting (big) data, contexts, people, machines,

and things.

•Context information includes users’ individual application set, QoS needs, social networks, devices’ hardware

characteristics, batter levels, etc

• Over a (predictive) time window which contents should SBSs pre-allocate? when (at which time slot should it be

pre-scheduled)? to which UEs ? And where in the network (location of files/BSs)?

• Leverage storage, computing capabilities of mobile devices, social networks via D2D, etc

Where/when/what to cache?

Predictive/Proactive Networking

Part VI

Release 12 and Beyond

Open Issues

125

Release 12 and beyond

Macro-BS FUE

f1/booster f2

Small cell BS

Non-fiber based connection

LTE multiflow / inter site CA

Soft-Cell concepts

• Facilitate “seamless” mobility between macro and pico layers

• Reduced handover overhead, increased mobility robustness, less loading to the core network

• Increased user throughput with carrier aggregation or by selecting the best cell for uplink and

downlink

• Wide-area assisted Local area access

TDD Traffic Adaptive DL/UL Configuration

DL is dominant

UL is dominant

Macro-BS

• Depends on traffic load and distribution

• Interference mitigation is required for alignment

Of UL/DL

• Flexible TDD design DL DL DL UL DL UL UL UL

126


FDD

TDD

Hetero-

CA

Licensed

Band

f1 f2

UL DL

f3 f4

UL DL f5ULDL

f6ULDL

CA btw LB & ULB

CA btw FDD & TDD

Unlicensed

Band

LTE or WiFi

Utilization of various frequency resources

Aggregation of FDD and TDD carriers

Aggregation of unlicensed band (LTE or

WiFi)

Source: LG Electronics

• Intra-RAT Cooperation

• CoMP based on X2 interface

• More dynamic eICIC

• Maximized energy saving

Carrier based ICIC for HeNB

Macro/Pico-Femto, Femto-Femto

Multi-carrier supportable HeNB

M1

M2F1

F3P3

F4 F5

F2

F6

F7

F8

F9

F10

F11

F12

F13

F14

Source: LG Electronics 127


• Inter-RAT Cooperation

Hotspot area service via the inter-RAT

connection between the cellular and Wi-Fi

network

LTE & Wi-Fi aggregation at co-located

transceiver site may also be considered

Measurement and signaling across intra/inter-

RAT nodes will be supported


• Relaying on Carrier Aggregation

Carrier aggregation for backhaul and

access link

Access link optimization/enhancement

with HD relay operation

Multiple antenna transmission

techniques for relaying

Mobile Relay

Multi-hop Relay


Hotspot 1 Hotspot 2

Inter-RAT Network

Pico Node

eNB

Wi-Fi AP

Tx Power Off

DeNB

Relay

Access link optimization

CA on backhaul link and access link Multiple

Antennas

UE 128


• 3D Beamforming

Source: KDDI

• Machine Type

Communication

New revenue streams

• Many devices

• Low-cost terminals essential

- Address conclusions from

Rel-11 study

• Support machine-type traffic

efficiently

• Handle priority and QoS

appropriately


129 Source: Ericsson

Advertisements

• Acknowledgement to Merouane Debbah, Francesco

Pantisano, Meryem Simsek, Stefan Valentin, and all our

collaborators

• Acknowledgement to funding agencies: NSF

• IEEE ICC’14: Workshop on Small cells June 2014

– Interested in SON techniques? => Book

130

Conclusions • Small cell networks are likely to proliferate in next-

generation wireless systems

• Many technical issues to address: interference, topology,

self-organization, etc.

• New tools, inclusive of stochastic geomtery, game theory,

learning

• Co-existence of..

– Small and macro-cells

– Multiple games!

131

Small is Beautiful..

Our job is to make

it smarter!

132

Finally….

Thank You

Questions?

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