Artificial Intelligence for 5G: Challenges and Opportunies. Merouane Debbah Huawei France Research Center

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1 Artificial Intelligence for 5G: Challenges and Opportunies Merouane Debbah Huawei France Research Center

2 单击此处编辑母版文本样式 第二级 第三级 第四级» 第五级 2017/1/18 3

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4 单击此处编辑母版文本样式 第二级 第三级 第四级» 第五级 2017/1/18 (Mbps) G网络目标体验速率分布图 100% 80% 60% 40% 20% 0% 5G embbtarget4000 Ave. 100Mbps (Mbps) 5

5 单击此处编辑母版文本样式 (Billion) 第二级 IDC AR & VR 16Q2 : 第三级 第四级» 第五级 $1600 亿 2017/1/18 6

6 单击此处编辑母版文本样式 第二级 第三级 第四级» 第五级 2017/1/18 7

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12 单击此处编辑母版文本样式 第二级 Frequencies (MHz) 第三级 第四级» 第五级 Region 1 Region 2 Region 3 EU Africa Arab C.I.S N.A L.A Asia Y Y Y Y Y Y Y 2017/1/18 14

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14 Wireless AI: Key Technology in 5G

15 Wireless AI: Key Technology in 5G 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 ML DL Exp. Sys. Big Data Wireless Brain RTT RRM MBB OSS Wireless Alg. RRM NLPS MBB TRM Network RNP/O RTT IRF ANT Product Chipset AI in Wireless Network Comparison months days hours minutes seconds ms RB RTT RRM OSS/SON MBB/Core Execution Feature Carrier Cell RAT Slice Policy & Monitor Conf. and Opti. Feature HUAWEI HISILICON TECHNOLOGIES SEMICONDUCTOR CO., LTD. Page 15 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

16 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 HF/LF collaboration LTE power control PAPR non-linear compensation DTX adaptation Regression Clustering Classification Comp mode selection Link Adaption RAN minimal deployment RAN AI chipset AI base station AI NE AI Network AI Data AI chipset AI service AI Center Trainer Learner Policy Explorer 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.

17 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 management Intelligent base station MEC deployment HUAWEI HISILICON TECHNOLOGIES SEMICONDUCTOR CO., LTD. Page 17

18 Handling mobile video traffic: Solutions and future challenges Stefan VALENTIN Principal Researcher Leader of the Context-Aware Optimization Team Mathematical and Algorithmic Sciences Lab, Paris Research Center, Huawei, France February 2017

19 Outline Mobile video traffic: Load and main characteristics 3 solutions to handle mobile video traffic Traffic shaping: T-Mobile and us Radio Resource Management: Buffers and radio maps Traffic profiling: Real-time traffic analytics by machine learning The future: QoE-estimation at the edge VR and cloud rendering

20 Relevance of mobile video QoE IBM s survey in 42 countries [Jan. 2017]: 73% of world s population, 90% of global GDP Data is all about video: The mobile internet is gradually morphing into a video distribution network for both digital entertainment and social media. 66% of customers often experience buffering or stalling and are more likely to blame the telecom company Half of the respondents would switch service providers if the quality were bad enough. [1] IBM, Telecom companies are failing on customer experience despite consumer trust, White paper, Jan

21 Traffic [PBytes/Month] Video traffic is taking over mobile networks! Mobile video made 60% of all mobile data traffic in 2015 and is predicted to increase to 78% by 2021 [1] HTTP Adaptive Streaming (HAS) is the dominating share of that traffic [2], most of this traffic is encrypted by TLS/SSL Mobile Video Traffic All Mobile Traffic HAS HAS HAS 63% HAS HAS Year [2] Cisco, Visual Networking Index: Forecast and Methodology, , [3] Sandvine, Global Internet Phenomena: Latin America & North White Paper, Feb America, White Paper, June

22 Outline Mobile video traffic: Load and main characteristics 3 solutions to handle mobile video traffic Traffic shaping: T-Mobile and us Radio Resource Management: Buffers and radio maps Traffic profiling: Real-time traffic analytics by machine learning The future: QoE-estimation at the edge VR and cloud rendering

23 A media streaming system The big picture of HTTP adaptive streaming (DASH, HLS): HAS policy: not standardized The last hop: Buffers and required rates BS: Tx queue UE: Play-out buffer Wireless Channel Video Decoder Backhaul Rate b Wireless transmit rate w Wireless throughput r Encoding rate v Frame rate m Demands back-propagate 23

24 Adaptive streaming: traffic generation HAS policy is a load scheduler with 3 main components: 1. Predict throughput for next time slots 2. Select video quality (bitrate) V 3. Schedule download of next segment at V A blueprint: Construction principle for most HAS policies [4] Z. Li, X. Zhu, J. Gahm, R. Pan, H. Hu, A. Begen, and D. Oran, Probe and adapt: rate adaptation for HTTP video streaming at scale, IEEE JSAC, Apr

25 A closer look on HAS traffic: YouTube Manifest parser: e-info.php Since 2013, YouTube: Consistently uses H.264 in MP4 containers and often offers VP9 in the WebM container Uses DASH with separate representations for audio and video We study: TCP/IP packet traces of YouTube player application in Android 6.1 on the Motorola Nexus 6 Big Bucks Bunny (BBB) movie streamed via LTE (Orange, high throughput) Video: 9m56s duration, high motion, 4 qualities 1080p/4Mbps, 720p/2Mbps, 460p/1Mbps and 360p/0.6Mbps Only H.264/MP4 version is streamed, encoding rate is extracted with ffprobe 25

26 The 3 phases of a streaming session Example of clearly defined buffer/steady state/depleting phases. Cumulative sum of streaming data over time for Android YouTube App via LTE, 9:56 min BBB movie, constant 480p quality 26

27 The 3 phases with a DASH quality change Cumulative sum of streaming data over time for Android YouTube App via LTE, 9:56 min BBB movie, 480p until 3min and then 720p 27

28 Observations from YouTube traffic Further observations: High dynamic of encoding rate does not show: But GoP structure can be identified from MTU runs Large GoPs are used for many videos, usually 120-GoP Client opens a large number of ports (~50) per session but only few are used for streaming Client uses persistent HTTP: Ports are kept open Client abandons buffer in case of a quality change Two phases with different rate requirements: Filling phase: High rate required initially, with quality changes and stalls Steady state: Constant average rate, close to average video encoding rate Begin of steady state can be detected from observing traffic rate Conclusions for wireless scheduler design: Filling phase requires separate treatment with high priority Otherwise: Steady state assumption requires CBR (on the average) 28

29 Outline Mobile video traffic: Load and main characteristics 3 solutions to handle mobile video traffic Traffic shaping: T-Mobile and us Radio Resource Management: Buffers and radio maps Traffic profiling: Real-time traffic analytics by machine learning The future: QoE-estimation at the edge VR and cloud rendering

30 T-Mobile s problem: RAN flooded by video traffic T-Mobile USA deployed static rate limitation in Nov as part of their Binge On program [12] Video traffic is identified at the P-Gateway and rate is limited to 1.5 Mbit/s for this traffic [12, 13]. This limit forces HAS players to choose 480p quality, which is medium quality in most services Business case: Limiting video load allows T-Mobile to offer contracts without data cap at the same capex [12]. Problem 1: Service providers have to provide tags in order to identify the video => Requires cooperation and easy to exploit Problem 2: Static rate limit increases buffering time. The result is poor QoE due to higher initial playback delay, fast forward time, stalling time etc. Problem 3: Static limit always penalizes video traffic bad publicity for T-Mobile [14]. The chosen limit is too low for modern handsets ( 1080p displays) and delivers poor QoE for interactive services (e.g., 360 video). 30

31 Main idea: Detect video stream and state by traffic profiling, perform dynamic bandwidth throttling according to video state and cell load Solves problem 1: Traffic profiling works accurately without tags, even with encrypted traffic. Solves problem 2: Buffering state is identified and not limited. Rate limit is only applied in steady state (streaming), which accounts for most of the time and traffic. Solves problem 3: Bandwidth throttling is performed dynamically in a slow manner. At high load, backpressure is applied to adaptive video client in order to choose lower load. This works with all adaptive streaming clients (DASH, HLS, ). Research project to define dynamic bandwidth throttling as a robust control problem Started Feb First results expected in Q Our solution: Dynamic and soft admission control Transfer expected to eran 20A in Sep Video Server q=3 q=2 q=1 Internet Different quality representations BS queue for each UE enb Scheduler Adaptive rate control Target rate: r Wireless transmit rate w PHY Project target Wireless throughput r Modem HAS policy Encoding rate of quality q v q Play-out buffer A/V decoder Frame rate m UE Video identification and parameter extraction v q Available in eran 12.1 (2017B) enb Video player selects quality q for next video segment 31

32 Base station or RAN controller: ARRM: Architecture Runs a buffer model to be aware of the user s buffer state. Allocates fraction of channel resources to K users over a prediction horizon of T time slots Predicts wireless channel rate and streaming rate, may identify state changes enb QCD, SVMs Streaming rate estimation (SRE) Play-out buffer model Buffer state Radio Maps Long-term channel state prediction (LTCP) E[ĥ] Anticipatory scheduler Allocated PRBs ĥ SVMs Short-term channel state prediction (STCP) CQI Context Information 32

33 ARRM: Main idea Fill playback buffer in advance at high SINR, consume buffer at low SINR => No resources required at poor coverage => Spectral efficiency gain Toy example for one user moving between 2 cells: Cell edge Cell edge Fill buffer Fill buffer Allocation too costly Allocation too costly Required bit rate Required bit rate Consume buffer Consume buffer [5] S. Sadr and S. Valentin, Anticipatory Buffer Control and Resource Allocation for Wireless Video Streaming," arxiv: v1, 2013 [6] Z. Lu and G. de Veciana, Optimizing stored video delivery for mobile networks: The value of knowing the future, in Proc. INFOCOM,

34 Buffer evolution and stalling time ARRM playback buffer model Improvement over [11]: Feasible solution even when we have stalls Comments: Buffer limit Z allows to trade off capacity versus buffer size Large buffer wastes channel capacity if the user drops the video or jumps in it Time-index in V covers HAS 34

35 ARRM Scheduler: Formulation as Linear Program Trade-off: allocated resources versus stalling time Non-empty initial buffer Buffer evolution Stalling time Limited BS resources Maximum buffer size Linearization of constraints (3) and (4): Proof based on symmetry [8] D. Tsilimantos, A. Nogales-Gomez, and S. Valentin, Anticipatory radio resource management for mobile video streaming with linear programming, in Proc. ICC,

36 System Simulation: Parameters [9] Proof of concept scenario: Video user move from left to right cell Large number of best effort users randomly dropped 36

37 Exploiting memory by anticipation Small buffer: Require more channel resources to fulfill the minimum bitrate constraint before the buffer runs empty Large buffer: Higher spectral efficiency but a higher risk that user drops the video. More accurate prediction required. We can expect high spectral efficiency gains by filling the user s playout buffer in advance 37

38 ARRM: Stalling duration and spectral efficiency [8] Highway scenario, K=20 users, Z=20 Mbits of play-out buffer, 4 video bitrates V Pareto fronts: Choose γ to trade off spectral efficiency and stalling time Spectral efficiency to guarantee 10% average stalling time per stream Up to 3 times higher spectral efficiency at the same QoS 38

39 ARRM: Stalling probability [8] Multi-user, highway model, Z = 20 Mbits, 4 video bitrates V Probability of zero stalls Number of supported users with guaranteed less than 10% stalling probability Up to 5 times more users supported at the same QoS 39

40 Computational time [8] Empirical cdf of one optimization for ARRM with T = 20 slots Measured on: Intel Xeon CPU running at 3.3 GHz running CPLEX v12.6 with C interface 40

41 Current field tests: Scenario Scenario 1Cell, 6Video User+ 4Bk User Video User access the network in turns Simulation time :480 seconds Collaboration with Wireless BU, Shanghai RSRP: UE0,UE6:-71.78dB UE1,UE2,UE3,UE7,UE8: dB UE4,UE5,UE9: dB Traffic configuration Ue0~Ue5: Video traffic, fixed Rate:1.2Mbps, fixed segment duration:10s Ue6~Ue9:Background User, file size:625kbytes, file interval:5s 41

42 Field test results: 6 video UE and 4 background UE Initial Delay (ms) scheme2 with para set 2 scheme1 PF Stalling Ratio scheme2 with para set 2 scheme1 PF vmos CDF scheme2 with para set 2 scheme1 PF Avg. Download Tput during playing stage (kbps) scheme2 with para set 2 scheme1 PF 42

43 Base station or RAN controller: ARRM: Architecture Runs a buffer model to be aware of the user s buffer state. Allocates fraction of channel resources to K users over a prediction horizon of T time slots Predicts wireless channel rate and streaming rate, may identify state changes enb QCD, SVMs Streaming rate estimation (SRE) Play-out buffer model Buffer state Radio Maps Long-term channel state prediction (LTCP) E[ĥ] Anticipatory scheduler Allocated PRBs ĥ SVMs Short-term channel state prediction (STCP) CQI Context Information 43

44 Matrix Completion for radiomaps HUAWEI TECHNOLOGIES CO., LTD. Page 44

45 HUAWEI TECHNOLOGIES CO., LTD. Page 45

46 Radiomaps Reconstruction based on Matrix Completion To complete the missing entries we should solve a rank minimization problem. NP HARD We solve the convex relaxation of the rank minimization problem: Several techniques have been proposed to solve this optimization. A fast and computational efficient technique is the Singular Value Thresholding 1. Nuclear Norm (sum of singular values) 1. Cai, J. F., Candès, E. J., & Shen, Z. (2010). A singular value thresholding algorithm for matrix completion. SIAM Journal on Optimization, 20(4), Chicago HUAWEI TECHNOLOGIES CO., LTD. Page 46

47 Singular Value Thresholding Algorithm Rank reduction operator HUAWEI TECHNOLOGIES CO., LTD. Page 47

48 Improvements over state-of-the-art non-adaptive reconstruction techniques Example: Berlin Pathloss map reconstruction: Size of Area 7500m X 7500m Size of Pixel 50m Number of Base Stations: 187 HUAWEI TECHNOLOGIES CO., LTD. Page 48

49 Example: Berlin pathloss map reconstruction KAPSM [4] Singular Value Thresholding Operation Time 3 : Singular Value Thresholding: approximately 4sec KAPSM: approximately 30sec 3. Matlab Implementation for the 5000 measurement scenario HUAWEI TECHNOLOGIES CO., LTD. Page 49

50 Finding the informative entries HUAWEI TECHNOLOGIES CO., LTD. Page 50

51 Finding the informative entries URS: Sample the N extra entries at random QbC: run different algorithms in parallel and sample the N extra entries that score the largest error HUAWEI TECHNOLOGIES CO., LTD. Page 51

52 Required context information: Radio maps A radio map is a data set of channel measures and positions Both measures may be inaccurate and incomplete Tasks: (1) complete radio map, (2) predict channel state Operator measured path loss in db for downtown Berlin, strongest server, 56 km2, 50 x 50 m pixels, Crowdsourced signal strength for downtown Berlin, 52

53 Illustration for the Berlin map Original Pathloss Map Pathloss Map: Missing entries, 40% of the complete data Pathloss map can be approximately reconstructed using a small number of measurements Reconstructed Pathloss Map [7] S. Chouvardas, S. Valentin, M. Draief and M. Leconte, "A Method to Reconstruct Coverage Loss Maps Based on Matrix Completion and Adaptive Sampling", ICASSP, Submitted to 53

54 Accuracy for the Berlin map APSM [8] 4.5 db accuracy gain Singular Value Thresholding Runtime (Matlab for 5000 samples): Singular Value Thresholding: approximately 4 s APSM: approximately 30 s [8] M. Kasparick, R. L. G. Cavalcante, S. Valentin, S. Stanczak, M. Yukawa, "Kernel-Based Adaptive Online Reconstruction of Coverage Maps With Side Information," IEEE TVT, Vol. PP(99), Jul

55 Limitation Areas with large errors remain These areas should be prioritized in drive tests Original Pathloss Map Reconstructed Pathloss Map 55

56 Channel prediction with Bayesian spatio-temporal inference Users collect the n th sample at time t n and provide it to a central data base, the sample contains: Timestamped location: Corresponding channel gain: Basic model: Linear regression where base function Φ is expressed by a superposition of Gaussian Kernels The hyperparameters of this kernel are found by minimizing the negative log marginal likelihood 56

57 Data-based simulation Scenario: Berlin coverage map of 56 km 2 from MOMENTUM project (T-Mobile), street data from OpenStreetMap Vehicular mobility for 100 users generated by SUMO, users leave map 57

58 Simulation results 58

59 RMSE [db] Simulation results With 150 m localization error [9] Q. Liao, S. Valentin, and S. Stanczak, Channel Gain Prediction in Wireless Networks Based on Spatial-Temporal Correlation, in Proc. IEEE SPAWC, Jun

60 Simulation Results: MSE vs. Prediction Horizon for SNR=10 db Channel gain prediction for the Jakes-like fading channel For a wide range of SNR and Doppler frequencies, our predictors KEM and PF: 1. Show very low prediction error on the average 2. Outperform ARIMA for a prediction horizon up to 18 ms [10] S. Mekki, M. Amara, A. Feki, and S. Valentin, Channel gain prediction for wireless links with Kalman filters and expectationmaximization, in Proc. WCNC,

61 What is video quality? No one knows exactly but it s like an elephant Slide inspired by Christian Timmerer 61

62 Some methodology Subjective, objective or estimated [ITU-T P.800.1] Subjective: MOS (ITU-T P.910): a generally accepted method of subjective measurement. Details are defined by P.910 (ACR,ACR-HR,DCR and PC methods). P.NAMS (ITU-T P.1200): the standard of non-intrusive assessment of audiovisual media streaming quality established by ITU-T. P.NATS (ITU-T P.1203, Oct. 2016): Parametric bitstream-based quality assessment of progressive download and adaptive audiovisual streaming services over reliable transport Objective (QoE factors): PSNR, playback starting delay, buffering duration, streaming rate, stability [11] Estimated: JNDMetrix [12], vmos, U-vMOS, AI?! [11] M. Seufert, et al., A Survey on Quality of Experience of HTTP Adaptive Streaming," IEEE Communications Surveys & Tutorials, Sep E2E E2E Not necessarily E2E [12] M. H. Brill, J. Lubin, P. Costa and J. Pearson, "Accuracy and cross-calibration of video-quality metrics: new methods from ATIS/T1A1, in Proc. Int. Conf. on Image Processing, Sep

63 QoE estimation at the edge (QoE 3 ) Inputs: Time series (IAT, packet size, ) Estimates of QoE factors QoE estimate Video bit-rate Estimator Waiting time Estimator QoE model Neural networks, deep learning, Be careful! Non-estimated QoE factors Ready and accurate Missing Target: Build a system to estimate video QoE in real time, inside a mobile network Challenges: Estimation of some QoE factors based on incomplete information (no end-to-end knowledge, limited observation window of the input time series) Feasibility vs. accuracy trade off: QoE-factors have to be obtainable in a practical mobile network. Limiting to such feasible factors may cost accuracy, which should be minimized. Validation: Against QoE y based on full information (end-to-end, complete time series), study effect of estimation error and minimize it by factor selection and estimator improvement 63

64 Thank you