o-ran: icdt convergence empowered by open source and more · 1 o-ran: icdt convergence empowered by...
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O-RAN: ICDT Convergenceempowered by Open Source and More
Dr. Chih-Lin ICMCC Chief Scientist, Wireless Technologies
CMRI, China Mobile
ETSI-OSA WORKSHOP
Dec. 12, 2018, Sophia Antipolis, France
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2016 2017 202020192018
The LARGEST Scale Trial, IOT Commercialization
Beijing, Shanghai, Guangzhou,Ningbo, Suzhou
（3.5GHz, 7 sites/city）
Key Tech Test（Completed）
PoC Test Pre-commercialTrial
CMCC 5G Trials in Sync with IMT-2020 PG Timeline
Trial: Hangzhou, Shanghai, Guangzhou, Wuhan, SuzhouService test: Beijing, Chengdu, Shenzhen, …
CMCC makes world’s first Holographic video call on 5G SA network in MWCS 2018
CMCC jointly launched the “5G Standalone Sailing Action” together with global partners
(5 cities for trial [500+ sites], 12 cities for9 service & application demo [500+ sites])
Biggest 5G bearer network supporting 120BSs in Hangzhou
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World’s Largest 4G Network
The Most PopularThe Most Popular
~1.3B Pop coverage rate
99%
The Biggest IoTThe Biggest IoT
NB-IoT/LTE-M/2G
384M connections
The Largest ScaleThe Largest Scale The Biggest UserThe Biggest User
700M Subscribers
76% 2G/3G
as of Sep 2018
2G/3G
2.09M Base stations
67%
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Profit
2015 2016
108.5B 108.7B
0.2%
Revenue(>60% from Mobile Data)
2015 2016
668.3B
2017
708.4B
6%
Mobile’ data traffic
2015 2016
2495PB
2017
5681PB
128%
2017
740.5B
4.5%
114.3B
5.1%
12409PB
118%
Explosive Growth of Mobile Data Traffic, but…
Subscribers (4G)
ARPU (4G)
71.264.4-9.6%
+12.6%617M700M
DoU (4G)
+56%2008MB
3132MB
2017 (Oct) 2018 (Oct)
3%92.1B 95B
2017 (Oct) 2018 (Oct)
2017 (Oct) 2018 (Oct)2017 (Oct) 2018 (Oct)
Profit
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Global Scene? OTT?
Company Revenue Profit
2015 2016 2017 2015 2016 2017
China Tele (RMB)
331. 2B (2.1%)
352.3B（6.4%）
366.2B(3.9%)
20.1B (19.2%)
18.0B （-10.22%）
18.6B(3.3%)
China Uni.(RMB)
277.0B (-2.7%)
274.2B（-1.02%）
274.8B(0.2%)
10.6B (-12%)
0.48B （-95.4%）
1.68B(250.5%)
DOCOMO(Yen)
4527.1B (3.3%)
4620.0B（6%）
4769.4B(4%)
548.5B (33.7%)
652.5B(18.96%)
744.5B(14%)
SKT(WON)
17136.7B(-0.2%)
17092B(-0.26%)
17520B(2.5%)
1708B(-6.4%)
1536B (-10%)
1537B(0%)
AT&T (USD)
146.8B (10.84%)
163.8B（11.6%）
160.5(-2%)
13.3 B (104.7%)
13.0B(-2.2%)
29.45B(126.5%)
Verizon(USD)
131.6B (3.57%)
125.98B (-4.3%)
126B(0.04%)
17.9 B(85.76%)
13.127B (-26.7%)
30.1B(129.3%)
Telefónica(EURO)
47.2 B(-6.27%)
52.04B (+10.25%)
52B(0%)
0.75 B (-77%)
2.369B(216%)
3.13B(32%)
No. Company
Value(RMB, Billion)
Value(RMB, Billion)
Value(RMB, Billion)
1 Tecent 1608.1 1936.3 3223.8
2 Alibaba 1520.0 1894.2 2891.8
3 ICBC 1571.8 1700.0 2209.7
4 CMCC 1505.5 1532.9 1356.4
5 CNPC 1455.0 1458.6 1480.6
6 CCB 1360.1 1460.1 1920.1
Market Value (15/16/17) in ChinaOperators’ Annual Revenue and Profit
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Rethink Fundamentals
Rethink Protocol Stack
Rethink Fronthaul
Rethink Base Station
Rethink Ring & Young
RAN
“Towards Green & Soft” IEEE WCNC Keynote, Apr.8, 2013“SDX: How Soft is 5G?”, IEEE WCNC Keynote, Mar. 21,2017
“Towards Green & Soft: A 5G Perspective” IEEE Comm. Magazine, Vol.52, Feb.2014“5G: rethink wireless communication for 2020+”, Philosophical Trans. A. 374(2062), 2015“On Big Data Analytics for Greener and Softer RAN,” IEEE Access, vol.3, Mar. 2015. “New paradigm of 5G wireless internet”, IEEE JSAC, vol.34, no.3, March 2016“Big Data Enabled Mobile Network Design for 5G & Beyond,” IEEE Comm. Magazine., vol. 55, no. 9, Jul.2017. “Big Data Driven Intelligent Wireless Network: Architecture, Use Cases, Solutions and Future Trends ,” IEEE VT Magazine, 2017
CT/DT/IT ConvergenceOpen SourceBusiness Model
Green Communication Research Center established in Oct. 2011, initiated 5G Key Tech R&D.
GreenGreen SoftSoft
Rethink Air Interface & Spectrum
Rethink Signaling & control
Rethink Shannon
AirInterface
To enable wireless signal to “dress for the occasion” via SDAI
To start a green journey of wireless systems, EE/SE
To make network application/load aware
Embracing verticals How it affects the traditional SDOs? What’s Big Data’s role in 5G era
For no more “cells” via C-RAN
To enable Soft RAN via NGFI (xHaul)
To make BS “invisible” via SmarTile
To enable User Centric Cell and flexible AI via MCD
Efficiency Agility
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C-RAN: Revolutionary Evolution of RAN
…
RRU
RRURRU
RRU
RRU
RRU
RRU
Virtual BS Pool
Distributed RRU
High bandwidth optical transport
network
Real‐time Cloud for centralized processing
…
“Soft BS ” in C-RAN virtualization/Cloudification
C-RAN has been deemed as a 5G essential enabling element (2011)
CU/DU based two-level RAN Arch
• CU-DU Arch identified in RANP (Mar 2017)
• E1 SI approved in RANP 76 (June 2017)
• E1 WI approved in RANP 78 (Dec 2017)
Centralized Control and/or ProcessingCollaborative Radio, Real-Time Cloud , Clean System
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RAN Transformation: a Journey
2012-2013 2014
C-RAN baseband pool R&D1, Design, development and test on front-end accelerator2, First soft 4G BS PoC based on COTS platform3, 1st field trial in commercial networks4, Adding RAN in ETSI NFV ISG WP
20162015C-RAN PoC development1, OTA test with commercial EPC , RRU and UE2, Proposal of NGFI (xHaul) concept3, Proposal of CU-DU architecture
C-RAN field trials1, Large-scale field trials in over 10 cities2, PoC field demonstration on virtualized C-RAN3, Evaluation of NGFI & design of CU/DU architecture, anchor CU for reliability4. Established IEEE 1914 WG5. Engage ITU-T 5G FG
5G C-RAN1, Continuous refinement on design of CU-DU architecture and the interface2, In-house PoC development of gNBwith CU-DU, MANO and cloud platform3, Carrier-grade cloud platform proposal accepted by Openstack
2017 2018
5G smart RAN1, C-RAN Alliance launched & CU-DU architecture accepted by 3GPP2, Proposal of RDA concept for the first time with AI-based wireless big data architecture3, In-hours PoC development on cloud-based CU-DU with demonstration with commercial RRU&UE
O-RAN：1、RDA2、AI3、MEC4、……
PCIe CPRICNRT-Linux+
Driver
CU_DU VM
SmarTileFront-End
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C-RAN Alliance: Re-launch to focus on 5G C-RAN
• 5G C-RAN WP v.1 released on Nov. 18, 2016• Refocus C-RAN community effort, promote the C-RAN cloudification via
• Standardizing the interface of SW/HW, northbound -interface of MANO for CU
• Enhancing the ETSI NFV MANO for RAN• Optimizing the hypervisor layer• PoC and field trial
• 20+ partners
WG 5: MANO(Ericsson Co-lead)
WG 4: Virtualization layer(NokiaCo-lead)
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WG 2: VNF Split and function definition(Huawei & Ericsson Co-lead)22WG 3: Common HW platform(NokiaCo-lead)
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WG 1: Use case definition (ZTE Co-lead)
Compute storage network acc. abstract layer
Network Storage compute CommonAccelerator
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Virtual layer
Hardware
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1010
Virtualization Layer(Kernel, Hypervisor, vSwitch, SDS, etc.)
VM:
CU-U
VM:
DU
VM:
MEC
BSS/OSS
VM:
CU-C
COTS Hardware
Server Switch Storage
VM:Big
Data/AI VNFM
Accelerator
VIM
NFVO
RAN-NFV towards Cloudification
In 2016, by promoting Linux community, realizing the RT-linux version for RAN real-time requirement, and currently published in the Linux official release version
In 2017, MWC in Barcelona, achieved the first set of RAN prototype based on NFV architecture that the NFVO could mange multiple RAN cloud platform.
End of 2017, unified testing (8 vendors) RAN-NFVI platform and published RAN technical requirements for 5G
In 2018, OPNFV community set up C-RAN project that engage in performance optimization especially in hardware accelerator achievement
In 2018, completed PNF model definition and TOSCA description, adopted by NFV standards
In 2018, completed the PFN software management use case design, code development and integration test, released in ONAP Casablanca
In 2014, MWC in Barcelona, achieved the first set of soft base station prototype integrated with vEPC and demoed with commercial US
In 2015, MWC in Barcelona, achieved the first set of wireless base station prototype based on virtualization platform that realized the voice call and video transmission
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Tech Scheme Hyper threading SR-IOV NUMA affinity #VMs
SR-IOV √ × 12-15
SR-IOV √ √ 7
DPDK+OVS √ × 12-14
DPDK+OVS √ - 5
DPDK+OVS × - 6
• The maximum #VM with the same physical resources, required metric of average delay and maximum jitter.Key test items:
Performance test: test for different transmission path/packet size/technic scheme on one VM; performance test on multiple VMs with restrict resource.
Stability test: with full-load and noisy neighbor programManpower saving: 15 days saved for each vendor.
Automatic Testbed for RAN-NFVI (for evaluation)
Compute-0SR-IOV
Compute-1OVS-DPDK
VIM
TestCenter (Traffic generator)
Automation PlatformFloating IP or DVR
TestVM
OpenStack API
TestVM
TestVM
TestVM
1st automatic Testbed for virtualization layer
Tech scheme vendors Avg latency Max jitter
SR-IOV - 22us 60us
DPDK+OVS Vendor A 22us 41us
DPDK+OVS Vendor B 42us 62us
DPDK+OVS Vendor C 74us 58.59us
• Throughput: in SR-IOV case, thp is close to full rate; in OVS+DPDK, the packet size is 516 byte or larger, thp is close to full rate
• Delay: The data packet setting to 516 bytes for statistics, the delay gap among the manufacturers are observed
Network capability assessment
Network capacity test
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Key objectives• centralizing near-real time radio resource management for
multiple eNBs/gNBs• exposing a “logical” RAT agnostic network state (“near real
time”) through a radio network information base (R-NIB)• ability for different control applications to induce physical
network change by reading and writing to R-NIB through a standardized northbound interface
• FH interface with OAM spec
xRAN Forum (2016)
WGs1. Northbound Interfaces WG2. Controller WG3. Southbound Interfaces WG4. Front-haul Interfaces WG M-CORD
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WAIA: Wireless AI Alliance (2017)
Mission to deliver the Use Cases, Architecture, Platform, Solutions for Data Driven Intelligent RAN to
increase the Efficiency, Performance and Functionality of 5G & Beyond
Alliance Goals
Founders Members
BeiHangUniv.
Zhejiang Univ.
BUPT
Sponsors
CUST
Requirements WG Architecture WG
Tech & Field Trial WG Platform WG
Standards WG
2017.11 2018. 2 2018. 6 2019. 22018 MWC
Arch. Concept Demo
Release Field Test Results
2019 MWC2st version Arch Demo
2017.08/10 2018. 5• 3GPP RAN3 SI Approved“RAN centric Data Collection & Utilization”• Field Test
1st version WP
MBBF Demo29th Aug Future Forum19th Oct WWRF
TR on Use Case & Requirements
2018. 12
TR on Data Driven ArchitectureJoint WP release
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Bringing AI to the RAN
Network PlanningNetwork Deployment
Network Operation & maintenance
Protocol stack & Signaling
Radio Resource Management
PHY layer Optimization
Statistic/Semi-Statistic
SensingMulti-dimensional
/cross-layer context info(user, application, network)Radio Environment Map…
Intelligent Decision Making
Machine/deep LearningOffline model training &online decision making
Operation & Management Plane Control & Data Plane
Classic Communication Theory Meets Data Technology
PredictionUser behavior
(trajectory, location)Traffic fluctuation
Service type……
Real-Time
Customized Network Strategy
Data Driven Machine Learning Based
Complex Network Optimization
Predication oriented configuration &Decision making
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From “Green Soft” to “Open & Smart”
• Open Interface/API• White Box• Open Source• …
• Intent Driven• Embedded Intelligence• Big Data Analytics & AI• …
Open(O-RAN, 2018)
Smart(WAIA,2017)
GreenGreen
SoftSoft
Data Driven Intelligent RAN Proposed by CMCC
[1] Chih-Lin I, etc.,“On Big Data Analytics for Greener and Softer RAN,” IEEE Access, vol.3, Mar. 2015. [2]S. Han, Chih-Lin. I, etc., “Big Data Enabled Mobile Network Design for 5G & Beyond,” IEEE Comm. Magazine, 2017[3] Chih-Lin. I, Q. Sun, etc.,“Big Data Driven Intelligent Wireless Network: Arch, Use Cases, Solutions & Future Trends ,” IEEE VT Magazine, 2017.
CT/IT/DT CT/IT
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5G DU
5G CN
AAU/RRU
NMS MANO
Open Interface & Architecture MEC
Big Data Analytics & AI
5G CU
RAN NFVI
CU-C
NGFI-I
NGFI-II
E2 E1
CU-U
Vision of O-RAN
Intelligence &Standardization
Open Source &Virtualization
White box &Reference design
O-RAN: Open & Smart Ecosystem for 5G RAN (Feb 27, 2018, MWC18)
• E2, E3 Interface Standardization• Open Interface of protocol stack• Open Capability of Edge Computing
• Open Interface (NGFI-I/NGFI-II)
•Open-source Software, white-box reference design
CU
DU
AAU/RRU
Intelligent Management
• Big Data-based RRM• Intelligent computing-based apps
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O-RAN Founding Meeting in MWCS18 and 2nd Symposium in MWCA18
MWCS18, June, 2018
•12 Board members [Operators]- New members: Bharti Airtel/China Telecom/KT/Singtel/ SKT/Telefonica/Telstra
Board(EC inside)
Technical Steering Committee
WG 1 WG 2 WG n
15 Board members [Operators]- New Members: Verizon、Reliance、 Telecom Italia Mobile
• 100+ participants, including 20+vendors, e.g., Ericsson, Nokia, ZTE, Qualcomm, Samsung, Intel, in the symposium and WGs meeting
O‐RAN symposium, Sep, 2018
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C-RAN AllianceO-RAN Alliance
xRAN forum
WG1: Use cases
WG2: RAN VNF
WG3: COTS platform
WG4: Virtualization
WG5: MANO
WG1:Use cases & Overall architecture
WG2:RIC(non-RT) & A1 interface
WG3:RIC(near-RT) & E2Interface
WG4:NGFI Interface specification
WG5:Key Interfaces and Stack Reference Design
WG6:Cloudification and MANO Enhancement
WG7:White-Box Hardware
WG1: Northbound Interfaces
WG2: Controller
WG3: Southbound Interfaces
WG4: Front-haul Interfaces
WGs mapping to O-RAN from C-RAN, xRAN
WAIA works as an academic innovation input to inject AI to O-RAN alliance
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O-RAN WhitepaperWG1: Use cases & Overall architecture
WG3: RIC(near‐RT) & E2 Interface
WG4: Open FH Interface
WG7: White‐Box Hardware
WG6: Cloudification & MANO Enhance
TSC Co-Chair
CMCC & AT&T
DT & CMCC
DOCOMO & Verizon
CMCC & AT&T
O-RAN Working Group (WG) Structure & Whitepaper
WG2: RIC(non‐RT) & A1 Interface
AT&T & ORANGE
Orange & DOCOMOWG5: openF1/W1/E1/X1/Xn inf.
CMCC & AT&TWG8: Stackreference design
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Open source is a key focus
Open Front Haul
F1
Open sourcefunction
Open source subject Relevant LF community
Non-RT RIC RAN orchestration, AI&dataset
ONAP
A1
Near-RT RIC
Controller framework, AI-enhanced RRM modules
None
E2
CU protocolstack
E1/F1 None
CU-CP/CU-UP
DUsoftware
NGFI None
Protocol stack , algorithm,etc.
NFVI platform
Real-time enhancement OPNFV/KVM/Qemu/Akraino
Accelerator Abstract layer(AAL)
OPNFV/Qemu/KVM/DPDK/Akraino
VIM OPNFV/Openstack/K8s
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Some preliminary work (Pre-open source/Pre-O-RAN)
eNB/gNB
Tools CN
NGFI Interface de/encapsulation (U/C/S/M plane)
DMaaP
nRT‐RIC framework (relevant RRM)Radio database/LB/Mobility/QoS/DC
Monitoring toolsLog/trace/data dump
…
Online embedded AI process
Platform Adaptive layer msg mem lock clock thread Trace Agent
4G DU pooling framework;RLC/MAC/PHY
5G DU pooling framework;RLC/MAC/PHY
4G CU modular framework RRC/PDCP
5G CU modular framework
RRC/SDAP/PDCP
TNL
Dashboard OAM (VID)
NRT-RIC framework
AIEnv
Hadoop Spark
Tensor‐flow
Offline AI process
Embedded AI preprocessdata cleaning/feature selection/normalization/policy reserve
HW
Embedded 3rd party entity(MEP/UPF etc)
Customized server
100% 100%30% 30%
(100%)10%
100%
0%
20%0%
10%
S1AP/NgAP/X1AP/XnAP
ASN.1
(100%)(100%)
EPC
RRU/SoC
100%
100%
Accelerator (FPGA/NP/ARM) Acc + Interface
20%
20%
Hypervisor&VIMAccelerator management
KVM enhanceme
nt
Accelerator Abstract Layer
Container enhancement
20% 100% 0%10%
Integration test
80%
ONAP/DCAE frame work(external data collocation framework) (50%)
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Potential option
Linux foundation
O-RAN
Near-RT RIC
CU protocol stack
Non-RT RIC
NFVI platform
SystemIntegration
All the open source function would upstream into the O-RAN branch.
The integration job would also be in O-RAN branch.
LFN
DU softwareOPNFVONAP
KVM OpenStack QEMU
O-RAN open source work mirrored under LF
Deep Learning&AI
AcumosAI
Akraino
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Timeline consideration
Open-sourced CU+DU(Q2, 2019)
• Modularized L1/L2/L3 function
• Shared Framework to reduce the R&D cost
• Common platform based• Pooling gain to support the
tidal effect
Open-sourced Near RT RIC(Q4, 2020)
• Open-sourced RRM framework to implement embedded AI-based function block
• Unified abstraction of the function block to adapt to the complex environment
Open-sourced Non RT RIC(Q2, 2020)
• Data set with unified structure to share
• Machine learning and prediction function optimization for radio network
Big data & Intelligent tools: Hadoop, Tensorflow, Torch, etc. Enhanced NFVI in existed community: KVM, Containor, GPU, FPGA, accelerator.
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Reference design is opened step by step
HW reference
design
HW reference
designLow-level
driverLow-level
driverFPGA CodeFPGA Code
Key Alg. RealizedKey Alg. Realized
BBU software
BBU software
HW White-Box: The scale effect of the reference design lowers cost.
White-Box Hardware to Reduce the Cost
ADC
DAC
PATx
LNA
ADC
DAC RX
PA
LNA
Digital Transceivers PAs Filter Antenna
Digital Processor
RX
Tx
To specify and release a complete reference design of a highperformance, spectral and energy efficient white box basestation. Within the scope any kinds of design material arenot precluded, such as documentation of reference hardwareand software architectures, detailed design of schematic,POC hardware, test cases for verification & certification forall BS types and usage scenarios and so on.
White-box Small BS Demo in MWCS18
Targeting white-box small BSs trial, from end of 2018 to Q2 2019, in Guangdong, Jiangsu, Anhui
Hardware reference
design
• Third party software
• Explore OPEN source for software
White-BoxHW
Chip vendors
Hardware vendors
Software vendors
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White-Box Hardware: Sub 6GHz Small BS for Indoor Coverage
Dual mode: LTE + NRKey features:
• Bandwidth: LTE 50MHz, NR100MHz• Ant#.: LTE 2T2R, NR 4T4R• Output power: 24dBm per channel• Power supply: Optical Fiber Composite• Low-Voltage Cable• Front haul media: Fiber
Progress:1. 1st version hardware completed.2. Debugging for software function. 3. Updating for cost & performance.
Single mode: NRKey features:
• Bandwidth: NR 100MHz• Ant# : NR 2T2R•Output power: 24dBm per channel•Power supply: POE++•Front haul media: CAT6 class cable
Progress:1. Selecting key IC solution.2. Evaluating the power consumption & FPGA resource.
Open Reference HW DesignOpt 7-2
Opt 8
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Wireless AI Alliance (WAIA), Progress
2017.11 2018. 2 2018. 11 2019. 02
2018 MWC WP v1.1Arch Concept Demo
2019 MWC Demo (2nd version )Release Field Test Results
2st version WP
2017.08/10 2018. 05
TR on Use Case & Requirements1st version WP
MBBF Demo
29th Aug Future Forum19th Oct WWRF
Data driven Intelligent RAN Arch (demo @MWC18)
Executive Summary1 Introduction2 Wireless Big Data & AI for Wireless
Network 3 Potential Solutions for Typical Use Case
3.1 Big Data Driven Wireless Channel Modelling
3.2 AI Based Positioning, Mobility Prediction &
Handover Optimization3.3 Deep Learning Based Deconding/Demodulation
3.4 Deep Reinforcement Learning for Network Slicing
3.5 Learning Based Traffic Prediction and Optimization3.6 Alarm root cause analysis
4 Network Architecture Framework5 Platform Capabilities/Environment6 Impact on the Standardization7 Summary
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AI empowered RAN optimization Use Cases
Time~100msms s min Hours Day/Month
RBs
Carrier
gNodeB
Slice
NearReal-Time(Control & User Plane)
Network Plan & Deployment Network Optimization & ConfigEnergy savingCell Splitting & Merging RF parameter optimization
Network Energy Saving
QoS/QoE optimization Load Balance Interference Management Multi-connectivity
Mobility Management
Cell
NonReal-time(Management Plane）
Slice Resource Management
Resources
Multi-user scheduling Link adaptation AI empowered PHY opt
AI DPD
Real-Time(PHY)
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AI-empowered EE Improvement： AI enabled MCES By the full integration with BD platform and taking full advantage of multidimensional data, MCES is developed to find the low traffic cells and deactivate/activate them appropriately without any performance deterioration
• By mid 2018, MCES is deployed for > 15 provinces & 400,000+ cells.• The total energy saving is over 16 million kWH (since mid 2017).
Deep integration with BD platform Collecting
varied dataXDR/MR/PM/CM …
Using ML to forecast the user trajectory and
service profile
Near Real time interaction with RAN by MML to achieve
precise energy saving
Data Set MCES1.0:
• MR/PM/CM for half year；Real time PM every 15min• Distributed deployment
MCES2.0：• MR/PM/CM for half year； Real time PM every 15min• Cloud deployment
MCES3.0：• MR/PM/CM for half year； Real time PM every 15min• Real time XDR from S1-U for user location and service profile• Cloud deployment
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AI-empowered Cell Splitting and Merging of Indoor System
Requirements of Indoor Scenarios:
Static topology can not meet the requirements of high throughput and
tidal effect
Manual modification leads to high cost and low efficiency
Centralized baseband processing pool support dynamic topology
AI-based cell splitting and merging solution:• Splitting and merging pattern
design based on load prediction
• Power parameters optimization DU/AAU
Cell merging
DU/AAU
DU/AAU
DU/AAU
CU
DU/AAU3
DU/AAU5
CUDU/AAU1
DU/AAU2
DU/AAU4
DU/AAU6
Field trial of cell merging in Ningbo:•27 DUs/AAUs•2 Cells•Option 6•Internal data(ms/s) is collected for data analysis
Cell Splitting
Basic prediction models are under training:• Load prediction and
cell status clustering are trained via the data of commercial network
• Interference prediction is trained via the simulation data
Rules are pre-defined according to the KPIs
Load prediction
Interference prediction
Cell Statusclustering
Pre-defined rules
Cell merging/spli
tting decision
Parameters Generation
Topology decision
Data collection and Pre-processing (ETL)
KPIMonitoring
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AI-empowered Crosslayer Optimization
• To identify application types without DPI• To provide real-time KQI/QoE measurement• To derive and expose next sub-second/second level status to network applications for optimization• To predict KQI/QoE and derive optimal RRM to enforce for QoE guarantee
• Identified the optimal profile of super parameters
Accuracy
• Verify AI enabled HD VoD streaming KQI/QoE evaluation & QoSguarantee in 4G trial network (with ZTE in Fujian, 2018.10 ~ 2019.6)
• Verify AI enabled Cloud VR QoE evaluation and QoS guarantee in 5G trial network (with Nokia in Shanghai, 2018.9 ~ 2019.6)
IP length = 800 IP length =1500
Progress： Trial Network built (in Fujian) and now is debugging；Test cases and spec completed. Next Step: Data collection, QoE modeling
Progress： Lab environment built, data sets and ML algorithm to be identified; Test cases being discussed. Next Step: To refine the application identification, VR QoE modeling, QoSguarantee solution etc.
• Achieved >95% accuracy for 11 typical applications identification with 13-layer CNN network
QoE model of naked eye 3D with a fixed BW.
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1. Data cleaning and feature
selection
2. Cells scene clustering by user distribution data
3. ML model training for
different cluster cells
4. Solution generating dynamically using field
test data
AI-empowered Network Load Balancing: tested in LTE network
D1
A
BC
D2
D3
FDD 1800
F1
F2
FDD 900
CapacityLayer
Coverage Layer
Enhancing Coverage
Layer
With dramatic growth of unlimited data user plan theDOU doubled in 2017 compared to 2016 of ChinaMobile. Up to 7 carriers/cell on different frequencyband will be collocated in a single site includingFDD LTE. How to steer the traffic in a balanceddistribution among different site confirmationsbecomes a big challenge to the operation
①
②③
④
• Big data platform built and use hive data warehouse to structure the raw data collected by the wireless network (>2000 Cells)•Feature extraction/selection, clustering/classification algorithms identified to realize cells scene partition strategy• 1st round data collection completed and preliminary analysis is being made. 2nd round data collection and relative test continues. • Data analysis and test to be completed at end of 2018.
Smart Load Balancing with Guangxi Mobile
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Smart UE Grouping
Interference
3D-MIMO optimization Multi-user scheduling CQI/RANK/TM
ACK/NACKCSI
Doppler frequency offset
ActionState Wireless Environment
Reward: PER/Throughput
Beam Optimization with Massive MIMOwith ZTE in Guangdong
AI-empowered Beam Optimization & Link Adaptation
Progress: Test for 2 month (Sep & Oct), obvious gain of user number and throughput can be observed;Next Step: Single BS test completed, and test report to be completed in Nov; Location of multiple BSs & test scheme to be initiated.
Avg. user number and throuput for 65 Cells
Smart Link adaptationwith Huawei in Shanghai
Progress: Data collection and preliminary analysis completed, up to 10%-15% gain can be achieved..Next Step: Data collection continue, analysis report to be completed; Timeline to be set for test in in LTE network.
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Limited application
Poor generality
Large feedback overhead
Simple PA model Respective modeling for different
manufactures & PA types
Large bandwidth High cost
Local deployment Local deployment & training Limited resources & data
Traditional Linearization Multiband & Ultra wide BW
Digital and hybrid BF, Lens antenna
Big Data & AI
White-box RRU Generalized model：different PA types & manufactures
Cloud deployment Flexible API encapsulation
Applicable tomultiple architectures
Low overhead & cost
Future Requirements
One DA/AD to multiple PAs in hybrid beamforming arch
Data collection &preprocess
Model training & optimization
Model deployment &
distribution
• PA types• Memory properties• Nonlinear characteristic
• ML: #neurons & #NN layers• Time delay & nonlinear order
• Cloud deployment• Distributed to RRUs
• Generalized DPD to reduce cost and support white-box RRU• Flexible model training & deployment in the cloud, open API
encapsulation capability• Meet the requirements of smart & open networks
AI-DPD
• PA types: 5 PAs from different manufactures• BWs: 20M, 100M, 160M • DPD Precision: NMSE Loss -37dB ~-45dB• DPD performance: ACPR -45dB ~ -55dB
Preliminary Results
AI based DPD
AM
AM AI based DPD+PA
AI based PA
After PA
After DPD+PA
Original Sigal
Practical Test
Similar perf as traditional method
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Solid Progress in 3GPP (Jun 2018)
“RAN Centric Data Collection and Utilization in NR” SI approved (3GPP RAN3)
• Study use cases and benefits of RAN centric Data utilization• Identify necessary standard impact on data collection and utilization for the defined use cases and scenarios• If necessary, investigate introducing a logical entity/function for RAN centric data collection & utilization
•RRM measurement•L2/L1 measurement quantities•Sensor data for UE orientation/altitude
Definition
•Procedure for collection of UE, L1/L2 RAN node
•Signaling procedure for distributed & central analysis
Collection
•SON, RRM enhancement•Edge computing, URLLC and LTE V2X•Radio network information exposure,
Utilization
Network Operation(CMCC lead) RAN centric data collection&utilization RAN3
Approved Rel-16 SIs
Recent Progress • SI phase : Oct. 2018-2-Jun, 2019, first meeting in Oct. 2018
• WI phase: Jun. 2019- Dec. 2019
• Work Group：RAN3/RAN2
• Approved the TR skeleton in Oct. 2018 meeting
• Identified Use Cases in Oct. 2018 meeting
• Capacity and Coverage Optimisation
• Mobility Optimisation
• Load Sharing and Load Balancing Optimisation
• RACH Optimisation
• Energy Saving
• Mobile Drive Test (MDT) Use Cases
• Potential Use Cases to be indentified
• Edge Computing Optimisation
• LTE V2X Optimisation
• mMIMO optimization use cases
• URLLC Optimization
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ITU-T Focus Group on “Machine Learning for 5G and Future Networks”
Background
Working Groups of ML5G
FG-ML5G established by ITU-T SG 13, 6-17 November 2017 Mission: The Focus Group will draft technical reports and
specifications for machine learning for future networks, including interfaces, network arch, protocols, algorithms & data formats.
CMCC Contributions
WG 1: Input 9 Use Cases
WG2 and WG3Actively involved in the technical discussion regarding ML algorithms, data formats and impact on network (esp. RAN) architecture.
WG1: Use cases, services and requirementsSpecify important use cases, technical requirements and standardization gapWG2: Data formats & ML technologies (CMCC co-chair)Analyze ML technology and data formats for communication networks, with special focus on the uses cases of WG1WG3: ML-aware network architectureAnalyse comm. network arch from viewpoint of ML & standardization gap
1 Personalized Mobile Edge Caching
2 RAN-assisted Transmission Control Protocol (TCP) Window Optimization
3 Machine Learning based Radio Network Planning and Radio Resource Management for Network Slicing
4 cell splitting and merging in indoor distribution system for ML5G
5 Load balance among cells for ML5G
6 User Profile Prediction to Improve the Energy-Efficiency of Radio Access Network
7 Machine Learning based Handover Optimization
8 Machine Learning based Link Adaptation Optimization
9 Big-data-aided channel modelling and prediction
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Can WBD & AI Simplify Future Mobile Standards?
Increasing Complicated Network
Consistence High Quality Experience
Introducing the unified flexible IT style interface to meet the diversified control and management requirements and simply the traditional case by case interface design with enhanced flexibility
Introducing the loosely-coupled IT framework based on the unified interface to allow diversified data driven network optimization implementation to simplify the dedicated case by case specification works
Diversified vertical services
Data Driven
Machine Learning
IT+CT+DT
Standards
AI Embedded
EfficientInformation
Model for Data Collection
Unified flexible IT style
Interface
Autonomous Algorithmupgrade
Intent DrivenFunctionalityOrchestration
Open SourceDe-facto Standards Standardized Framework
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Source Encoding
Channel coding
Modulation MIMO OFDM
Channel
Source decoding
Channel decoding
De-Modulation
Channel Estimation & Equalization
De-OFDM
Mul
tiple
Den
se
Laye
r
Nor
mal
izat
ion
Laye
r
0...010...0
f(s)
s
Mul
tiple
Den
se
Laye
r
Nor
mal
izat
ion
Laye
r
g(y)
x y
0 .0 1.. .0 .10 .9 50 .0 2.. .0 .0 1
S’
Fadi
ng &
noi
se
laye
r
channelTransmitter Receiver
( | )p y x
(c) auto-encoder based communication system
(a) Conventional building blocks based communication systemTransmitter
Receiver
Machine Learning Module
(b) AI enabled building blocks optimization
AI-Enabled PHY Preliminary Exploration : Facilitating Software Upgrade of Protocols
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Open Wireless Data Sets
Application layer data
User Related data
Networklayer data
PHYlayer data
QoE prediction
Mobility pattern prediction
Cell traffic prediction
QoE/QoS optimization
Load balance
Network Energy Saving
Massive MIMO BeamOptimization
Network qualityprediction
User distribution prediction
Signaling data（XDR/DPI）
NMS/OMC(MRO，KPI，PM, NRM）, MDT
Real time / near real time data in BS（log, buffer, event, procedure)
Data collection
DataProcessing
Data cleaning Data association Data desensitization
Service type/featuresKQI, QoE, etc.
Trajectory, behavior, etc.
Cell configuration,Load, interference, etc.
Measurement report, CSI, CQI, etc.
O Domain Unified XDR
Open dataset AI Model Training Wireless network application
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Current membership (as of Dec.12th, 2018)
More than 80 members applying, and close to 50 have completed so far since the process opened in late September
www.o-ran.org
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Timeline consideration
1. Enhancement to A1 spec.2. Enhancements to near-RT RIC
architecture and E2 interface3. Additional use cases/scope4. NFVI spec.5. Release White box base station
2018 Q2 2018 Q3 2018 Q4 2019 Q1 2019 Q2
1. Define the overall architecture and function splits
2. First release of O-RAN whitepaper
1. Identify & finalize use cases & POCs for each WG
2. First Release of FH interface3. Define key capabilities of NFVI and VIM4. Launch the open source community for
RAN network
1. Initial results of field trial/demo of each WG.2. First Release of A1 and E2 interface specs.,
and AI/data analytics support.3. First release of specification of VIM and
orchestration interfaces4. First internal release of TD-LTE BBU
framework software including CU/DU
1.MWC19 demos2. First Release of F1/X2 enhancement specs3. Second Release of FH specs4. Finalize 2019 planning for architecture
refinements and new use cases5. Embedded AI based LB demo by using open
source framework
1. First Release of E1/W1 enhancement specs
2. First Release of white-box hardware
3. First release of 5G NR CU/DU
2019 Q3 2019 Q4
• Industry promotion & pre-commercialization on open source of protocol stack and NFVI
2018~2019
• Open interface spec;• Initial solution design for
RIC and White Box
2019~2020 2020~2021
• Commercial deployment of RIC, and White Box for small cells
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OAI has been a key partner in C-RAN
• CMCC finished the 1st 20M TDD version based on internal platform, and C-RAN branch was created in 2015: • 1st ping call was successful with commercial UEs under 20M TDD OAI, which was deployed on the VM• 1st LTE eNB live migration prototype: the whole LTE protocol (L1/2/3) VM was successfully migrated without call drop
• OAI SW has been one of the key components in C-RAN prototypes: • Performance improvement has been done to meet the requirements of C-RAN prototypes. For example, Mini C-
RAN/NGFI prototype, NFV-based C-RAN+MEC prototype, etc
1st ping call with commercial UE in 2015 for 20M TDD Cloud BBU pool prototype supporting live migration
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Collaboration Options b/w OAI and O-RAN (s.t. IPR Alignment)
Core Networkfrom OAI
eNB/gNBfrom
O-RAN partners
eNB/gNBfrom
O-RAN partners
UE from OAI
CUfrom
O-RAN partners
DUfrom OAI
…
F1 W1
E1 E2
I. Interoperability test: network elements (e.g. 5GC/EPC) or function blocks (e.g. DU) from OAI to do interoperability testing with the elements from O-RAN Alliance
II. Reference implementation based on O-RAN specifications: F1, W1, E1, E2, NGFI-I, etc.III. Testing tools provision: UE software, various simulators, etcIV. Implementation of NR protocol stack based on modularized software architecture
(nr-)dlsim is the unitary simulator for PDSCH/PDCCH
(nr-)ulsim unitary simulator for PUSCH
(nr-)prachsim unitary simulator for PRACH
(nr-)pucchsimPHY
(e.g. physical channels)
MAC(e.g. logical channels)
RLC(e.g.AM/UM)
PDCP
SDAP
RRC
OAIO-RAN
OAIO-RAN
OAIO-RAN
OAIO-RAN
OAIO-RAN
OAIO-RANII III IV
I
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Summary: Deep CT/IT/DT Deep Convergence
• Theme: From Green & Soft to Open & Smart
• E2E Soft 5G Arch: SDX• SBA(Telecom 4.0, NFV/SDN), UCN/C-RAN• Large Scale Trial: 5 Cites for Trial, 12 Cities for Service Demo [>500 Sites/City ] • 5G Joint Innovation Center: 14 Open Labs, IOX• 120M/200M/320M/800M OneNET Subs in 2016/2017E/2018E/2020
• New Frontier: ‘Open & Smart’• O-RAN: 8 WGs, O-RAN WP v1.0• Open Source: ONAP, LF, ONF, OAI, TIP, ...• A holistic approach towards open and programmable RAN. • Architecture design, Interface specification, Open source development, Whitebox
reference designs.
