Machine learning for Wireless Networks: Challenges and opportunities

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1 Machine learning for Wireless Networks: Challenges and opportunities Mérouane Debbah Mathematical and Algorithmic Sciences Lab, Huawei, France 8 th of June,

2 Huawei [Wow Way] 180,000 Employees 80, No. 70 No. 83 R&D employees Countries R&D Institutes & Centers Interbrand's Top 100 Best Global Brands Fortune Global 500 HUAWEI TECHNOLOGIES CO., LTD. 华为保密信息, 未经授权禁止扩散 Page 2

researchers in applied mathematics 4 R&D centers, Academic

3 Huawei [in France] 950+ employees, among with 150 in R&D HQ in Boulogne- Billancourt 1 Open Lab (opening April 2018) 85 researchers in applied mathematics 4 R&D centers, Academic cooperation with Major Universities HUAWEI TECHNOLOGIES CO., LTD. 华为保密信息, 未经授权禁止扩散 Page 3

4 The cost of Understanding HUAWEI TECHNOLOGIES CO., LTD. 华为保密信息, 未经授权禁止扩散 Page 4

5 Why it works Machine-learning algorithms have progressed in recent years, especially through the development of deep learning and reinforcement-learning techniques based on neural networks. Computing capacity has become available to train larger and more complex models much faster. Graphics processing units (GPUs), originally designed to render the computer graphics in video games, have been repurposed to execute the data and algorithm crunching required for machine learning at speeds many times faster than traditional processor chips. Key Trend Emerging: Specially design chips and Hardware for Machine Learning workloads (Tensor Units). Massive amounts of data that can be used to train Machine Learning models are being generated, for example through daily creation of billions of images, online click streams, voice and video, mobile locations, and sensors embedded in the Internet of Things devices. HUAWEI TECHNOLOGIES CO., LTD. 华为保密信息, 未经授权禁止扩散 Page 5

6 From Learning to recognize HUAWEI TECHNOLOGIES CO., LTD. 华为保密信息, 未经授权禁止扩散 Page 6

7 Why would we learn to optimize in Communications? «A Mathematical Theory of Communication», Bell System Technical Journal, 1948, C. E. Shannon HUAWEI TECHNOLOGIES CO., LTD. 华为保密信息, 未经授权禁止扩散 Page 7

8 Deep Communications now? Models are expensive to obtain The E2E objective function is not defined mathematically High Dimensional space with many parameters Optimization is complex and difficult to perform HUAWEI TECHNOLOGIES CO., LTD. 华为保密信息, 未经授权禁止扩散 Page 8

9 Networks are becoming very complex Autonomous car 4G MBB Voice Data HD Video B2C B2H B2V WTTx 3D Video VoLTE embb urllc Smart Transportation Drone Smart Energy Disaster alert Automotive ecall Tactile Real time gaming internet AR Device remote control V R Multi-person video call WTTx AR/VR mmtc Smart meter Smart manufacturing Sen sor s Source: GSMA/Huawei X Labs One Network 4 Services One Network Hundreds of Services Diversified Services SLA Requirement 9

Wireless AI: Key Technology What is Wireless AI Goal oriented and self control in network management and optimization solution, can overcome the problem when the network cannot be accurately

10 Wireless AI: Key Technology What is Wireless AI Goal oriented and self control in network management and optimization solution, can overcome the problem when the network cannot be accurately expressed with formula based on big data and machine learning technology. Robo CV AI/ML NLP Exp. Sys. ML D L Wireless Brain RTT RRM MBB OSS Wireless Alg. NLPS MBB RRM TRM Network RNP/ O RTT IRF ANT Product Chipset Big Data AI in Wireless Network month s days hours minutes seconds ms RB RTT RRM OSS/SON MBB/Core Execution Feature Carrier Cell RAT Slice Policy & Monitor Conf. and Opti. Feature Comparison HUAWEI HISILICON TECHNOLOGIES SEMICONDUCTOR CO., LTD. Page 10 AI Algorithm Value Data Link Scenario Automatically Manually Target Global probability optimization Wireless Algorithm Local determined optimization Scope E2E network Locally Modelling method Big data, learning Formula, optimization Usage Set the target goal Tune parameters manually

11 AI in wireless network Reconstruct Wireless network using AI technique Wireless Brain Technique trend in AI/ML New deep learning network architecture has been proposed Rapid development of unsupervised learning Development of AI chipset /TPU Trend when AI/ML used in wireless network AI/ML technique is designed into the network pipe, to enable wireless network autonomic Improve the network operations Reduce the complexity of network fault diagnosis Network Planning and Optimization Traffic prediction Experienced network AI in SON Use behavior analysis More Accurate More Intelligent Architecture Dataset Platform Failure Detection and Analysis Network Resource Management More Faster Physical Sub-health detection VoLTE root cause analysis Failure prediction Network security risk analysis CA policy selection Slice resource managemen Intelligent base station MEC deployment HUAWEI HISILICON TECHNOLOGIES SEMICONDUCTOR CO., LTD. Page 11

12 AI in Wireless Algorithm Architecture HF channel map construction E2E Performance learning Network auto configuration Slice resource management Self operation Failure detection AI management platform ASFN adjustment Policy management MBB AI Slice LTE power control PAPR non-linear DTX compensation adaptation AI NE Regression Clustering Classification AI Center Trainer Learner Policy Explorer HF/LF collaboration AI Network Comp mode selection Link Adaption RAN minimal deployment RAN AI chipset AI base station AI Data AI chipset AI service Re-enforcement learning Dynamic optimization GMM/HMM Association rule mining enb AI Agent Decides action Determine state Deep Learning RDL Transfer Learning Graphic algorithms Feature Statistic Collector 1 Collects Statistics 2 Applies Action HUAWEI TECHNOLOGIES CO., LTD.

13 Example HUAWEI TECHNOLOGIES CO., LTD. Page 13

14 Why do we need energy efficiency in communications? HUAWEI TECHNOLOGIES CO., LTD. Page 14

15 Why do we need energy efficiency in communications? HUAWEI TECHNOLOGIES CO., LTD. Page 15

16 HUAWEI TECHNOLOGIES CO., LTD. Page 16

17 HUAWEI TECHNOLOGIES CO., LTD. Page 17

18 HUAWEI TECHNOLOGIES CO., LTD. Page 18

19 HUAWEI TECHNOLOGIES CO., LTD. Page 19

20 Deep Forward Neural Network HUAWEI TECHNOLOGIES CO., LTD. Page 20

21 GEE Maximization in Interference Limited Networks HUAWEI TECHNOLOGIES CO., LTD. Page 21

22 GEE Maximization Problem HUAWEI TECHNOLOGIES CO., LTD. Page 22

23 ANN Training & Validation HUAWEI TECHNOLOGIES CO., LTD. Page 23

24 Online Implementation HUAWEI TECHNOLOGIES CO., LTD. Page 24

25 Building the training set HUAWEI TECHNOLOGIES CO., LTD. Page 25

26 Neural Network Architecture HUAWEI TECHNOLOGIES CO., LTD. Page 26

27 GEE Comparison. LMMSE reception is used. HUAWEI TECHNOLOGIES CO., LTD. Page 27

28 Power Consumption. LMMSE is used HUAWEI TECHNOLOGIES CO., LTD. Page 28

29 Example HUAWEI TECHNOLOGIES CO., LTD. Page 29

30 Edge User Throughput and VoLTE Background Edge User THP Affect User Experience Than Average THP 15 % 20 % 21 % 25 % 19 % VoLTE PLR(Packet Loss Rate) Correlated with MOS MOS <1 1~ 5 5~10 10~1 5 >15 Mbps PLR Too Many Factors Affect Edge Throughput and VoLTE, Make Optimization Too Difficult Feature + = Parameter Combination 30 > 10 30

AI Assisted VoLTE and Edge User THP Optimization 150 Factors TopN Factors Optima >

Extracting Feature Importance Ratio Evaluation Modelin g Recognitio n Importance

31 AI Assisted VoLTE and Edge User THP Optimization 150 Factors TopN Factors Optima > Combination 240 Combination 1 Combination Data Top N Factors Analysis Extracting Feature Importance Ratio Evaluation Modelin g Recognitio n Importance Ratio Rank Optimal Value Search Experience Library Transfer Learning Factor Factor Importance 1 st Cell, 14 Round Converge Agent Evaluation Reinforcement Learning N Cell, 7 Round Converge Agent Evaluation Reinforcement Learning 31

9/7/2017 9/9/2017 9/11/2017 9/13/2017 9/15/2017 9/17/2017 9/19/2017 9/21/2017

10/9/2017 10/11/2017 Innovation in Turkey Edge THP Optimization Test Area: Ankara

10 8 6 4 Ankara Top 10% Gain Cells User Throughput < 1Mbps Ratio (%) Optimization

32 9/7/2017 9/9/2017 9/11/2017 9/13/2017 9/15/2017 9/17/2017 9/19/2017 9/21/2017 9/23/2017 9/25/2017 9/27/2017 9/29/ /1/ /3/ /5/ /7/ /9/ /11/2017 Innovation in Turkey Edge THP Optimization Test Area: Ankara Scope: 699 Site, 2281LCell <1 Mbps Use Ratio Decrease while User Num Increase Ankara Top 10% Gain Cells User Throughput < 1Mbps Ratio (%) Optimization Whole Network Gain: Throughput Gain Distribution User Num 32% 40% 50% Optimization 0-10% 10-30% 32

9/7/2017 9/9/2017 9/11/2017 9/13/2017 9/15/2017 9/17/2017 9/19/2017 9/21/2017 9/23/2017 9/25/2017 9/27/2017 9/29/2017 10/1/2017 10/3/2017 10/5/2017 10/7/2017 10/9/2017 10/11/2017 9/7/2017 9/9/2017

33 9/7/2017 9/9/2017 9/11/2017 9/13/2017 9/15/2017 9/17/2017 9/19/2017 9/21/2017 9/23/2017 9/25/2017 9/27/2017 9/29/ /1/ /3/ /5/ /7/ /9/ /11/2017 9/7/2017 9/9/2017 9/11/2017 9/13/2017 9/15/2017 9/17/2017 9/19/2017 9/21/2017 9/23/2017 9/25/2017 9/27/2017 9/29/ /1/ /3/ /5/ /7/ /9/ /11/2017 Innovation in Turkey VoLTE Optimization Test Area: Bursa Scope: 188 Site, 877 LCell DL/UL PLR Decrease Dramatically Bursa Top 10% Gain Cells DL Packet Loss Rate (%) Optimization Whole Network Gain: DL PLR Gain DistributionUL PLR Gain Distribution 32% 18% UL Packet Loss Rate (%) Optimization 10% 58% 20% 62% 0-10% 10-30% >30% 0-10% 10-30% >30% PLR: Packet Loss Rate 33

34 Model versus Data HUAWEI TECHNOLOGIES CO., LTD. Page 34

Mobile AI: Challenges and Opportunities

Mobile AI: Challenges and Opportunities Mérouane Debbah Mathematical and Algorithmic Sciences Lab, Huawei Geneva, January 29th, 2018 1 Networks are becoming very complex 4G MBB Voice Data HD Video B2C