artificial intelligence for 5g: challenges and opportunies · goal oriented and self control in...

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

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(Mbps)

5G网络目标体验速率分布图 100%

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6000

5G eMBBTarget4000

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Ave. 100Mbps

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$1600亿 IDC AR & VR 16Q2 :

(Billion)

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Frequencies

(MHz)

Region 1 Region 2 Region 3

EU Africa Arab C.I.S N.A L.A Asia

3400-3600 Y Y Y Y Y Y Y

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Wireless AI: Key Technology in 5G

HISILICON SEMICONDUCTOR HUAWEI TECHNOLOGIES CO., LTD.

Wireless AI: Key Technology in 5G AI/ML

ML CV

Robo NLP

Exp. Sys. …

DL

What is Wireless AI

ms

seconds

minutes

hours

days

months

Execution

AI Algorithm Wireless Algorithm

Value Data Link

Scenario Automatically Manually

Target Global probability optimization

Local determined optimization

Scope E2E network Locally Modelling

method Big data, learning Formula , optimization

Usage Set the target goal Tune parameters manually

Big Data

Network

TRM RRM

RTT IRF ANT

NLPS MBB

RNP/O

Chipset

Product

Wireless Alg.

AI in Wireless Network Comparison

RTT

RRM

MBB

OSS

Wireless Brain

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.

RB

Feature

Feature Carrier Cell RAT Slice

RTT RRM

OSS/SON

MBB/Core Conf. and Opti.

Policy & Monitor

HUAWEI TECHNOLOGIES CO., LTD.

AI in Wireless

AI NE AI Network AI Data AI chipset AI service

AI base station

Network auto configuration

Failure detection Policy management

RAN minimal deployment

RAN AI chipset

DTX adaptation

Self operation

MBB AI Slice

E2E Performance learning

Comp mode selection Link Adaption

PAPR non-linear compensation

• Regression • Clustering • Classification

• Deep Learning • RDL • Transfer Learning • Graphic algorithms

Slice resource management

ASFN adjustment

• Re-enforcement learning • Dynamic optimization • GMM/HMM • Association rule mining

HF/LF collaboration

LTE power control

HF channel map construction

AI management platform

Alg

orith

m

Arch

itectu

re

AI Center Trainer • Learner • Policy • Explorer

eNB AI Agent • Decides action • Determine state

Feature Statistic Collector • 1 Collects Statistics • 2 Applies Action

HISILICON SEMICONDUCTOR HUAWEI TECHNOLOGIES CO., LTD.

Failure Detection and Analysis

Network Planning and Optimization

More Accurate

More Intelligent

More Faster

Wireless Brain

Network Resource Management

Architecture

Platform

Dataset

AI in wireless network

Physical Sub-health detection VoLTE root cause analysis Failure prediction Network security risk analysis

Traffic prediction Experienced network AI in SON Use behavior analysis

CA policy selection Slice resource management Intelligent base station MEC deployment

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

New deep learning network architecture has

been proposed

Rapid development of unsupervised learning

Development of AI chipset /TPU

Technique trend in AI/ML

Trend when AI/ML used in wireless network

Reconstruct Wireless network using AI technique

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

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

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. 2017. http://bit.ly/2lMCUQ6

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http://bit.ly/2lMCUQ6

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

0

5000

10000

15000

20000

25000

30000

2014 2015 2016 2017 2018 2019

Traf

fic [P

Byte

s/M

onth

]

Year

Mobile Video Traffic

All Mobile Traffic

[2] Cisco, “Visual Networking Index: Forecast and Methodology, 2016-2021”, White Paper, Feb. 2017. 21

[3] Sandvine, “Global Internet Phenomena: Latin America & North America”, White Paper, June 2016.

HAS

HAS HAS

HAS

HAS

63%

Outline






• The future:



A media streaming system • The big picture of HTTP adaptive streaming (DASH, HLS):

• The last hop: Buffers and required rates

Wireless Channel

Video Decoder

Encoding rate v

Wireless throughput r

Frame rate m

BS: Tx queue UE: Play-out buffer

Wireless transmit rate w

BackhaulRate b

HAS policy: not standardized

Demands back-propagate 23

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:

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[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. 2014

Construction principle for most HAS policies

A closer look on HAS traffic: YouTube

• 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

https://www.youtube.com/watch?v=YE7VzlLtp-4

Manifest parser: http://www.h3xed.com/blogmedia/youtube-info.php

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http://www.h3xed.com/blogmedia/youtube-info.php





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

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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

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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)

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Outline






• The future:



T-Mobile USA deployed static rate limitation in Nov. 2015 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).

T-Mobile’s problem: RAN flooded by video traffic

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A/V decoder

HAS policy

Modem

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. 2017

First results expected in Q3 2017

Transfer expected to eRAN 20A in Sep. 2018

Our solution: Dynamic and soft admission control

Internet

PHY

BS queue for each UE

Video identification and parameter extraction

q=1

Video Server

q=2

q=3

eNB

UE

Play-out buffer

Video player selects quality q for next video segment

Target rate: r

Different quality representations

Wireless transmit rate Encoding rate of quality

q

qv

Wireless throughput

r

Frame rate

m

qv

eNB Scheduler

w

Adaptive rate control

Available in eRAN 12.1 (2017B)

Project target

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ARRM: Architecture

• Base station or RAN controller: 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

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ĥ

Radio

Maps

CQI

eNB

Long-term channel state prediction (LTCP) Anticipatory scheduler

Short-term channel state prediction (STCP)

Play-out buffer model

Buffer state

Streaming rate estimation (SRE)

Context Information

QCD, SVMs

SVMs

E[ĥ] Allocated PRBs

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

Fill buffer Allocation too costlyConsume buffer

Required bit rate

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[5] S. Sadr and S. Valentin, “Anticipatory Buffer Control and Resource Allocation for Wireless Video Streaming," arXiv:1304.3056v1, 2013

[6] Z. Lu and G. de Veciana, “Optimizing stored video delivery for mobile networks: The value of knowing the future,“ in Proc. INFOCOM, 2013

Cell edge

Fill buffer Allocation too costlyConsume buffer

Required bit rate

Buffer evolution and stalling time

Improvement over [11]: Feasible solution even when we have stalls

ARRM playback buffer model

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• 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

ARRM Scheduler: Formulation as Linear Program

Trade-off: allocated resources versus stalling time

Non-empty initial buffer

Buffer evolution

Stalling time

Limited BS resources

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, 2016.

Maximum buffer size

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System Simulation: Parameters [9]

• Proof of concept scenario: Video user move from left to right cell

• Large number of best effort users randomly dropped

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Exploiting memory by anticipation

• We can expect high spectral efficiency gains by filling the user’s playout buffer in advance

Large buffer: Higher spectral efficiency but a higher risk that user drops the video. More

accurate prediction required.

Small buffer: Require more channel resources to fulfill the minimum bitrate constraint before the buffer runs empty

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Up to 3 times higher spectral efficiency at the same QoS

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

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ARRM: Stalling probability [8]

Multi-user, highway model, Z = 20 Mbits, 4 video bitrates V

Up to 5 times more users supported at the same QoS

Probability of zero stalls

Number of supported users with guaranteed less than 10% stalling probability

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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

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Current field tests: Scenario

•Traffic configuration •Ue0~Ue5: Video traffic, fixed Rate:1.2Mbps, fixed segment duration:10s •Ue6~Ue9:Background User, file size:625kBytes, file interval:5s

•Scenario 1Cell, 6Video User+ 4Bk User Video User access the network in turns Simulation time :480 seconds RSRP: UE0,UE6：-71.78dB UE1,UE2,UE3,UE7,UE8:-116.78dB UE4,UE5,UE9:-121.78dB

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Collaboration with Wireless BU, Shanghai

Field test results: 6 video UE and 4 background UE

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0.3

0.4

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0.9

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1 1.5 2 2.5 3 3.5 4

vMOS CDF

scheme2 with para set 2 scheme1 PF

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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Stalling Ratio


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1500 3500 5500 7500 9500 11500

Initial Delay (ms)


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900 1100 1300 1500 1700 1900 2100 2300 2500 2700

Avg. Download Tput during playing stage (kbps)


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ARRM: Architecture

• Base station or RAN controller: 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

43

ĥ

E[ĥ] Radio

Maps

Allocated PRBs

CQI

eNB

Long-term channel state prediction (LTCP) Anticipatory scheduler

Short-term channel state prediction (STCP)

Play-out buffer model

Buffer state

Streaming rate estimation (SRE)

Context Information

QCD, SVMs

SVMs


Matrix Completion for radiomaps


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 Thresholding1.

Page 46

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), 1956-1982. Chicago


Singular Value Thresholding Algorithm

Rank reduction operator


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


Example: Berlin pathloss map reconstruction

Page 49

KAPSM [4]

Singular Value Thresholding

Operation Time3: •Singular Value Thresholding: approximately 4sec •KAPSM: approximately 30sec 3. Matlab Implementation for the 5000 measurement scenario


Finding the informative entries


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

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, http://momentum.zib.de/

Crowdsourced signal strength for downtown Berlin, http://opensignal.com/

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Illustration for the Berlin map

Original Pathloss Map Pathloss Map: Missing entries, 40% of the complete data

Reconstructed Pathloss Map

Pathloss map can be approximately reconstructed using a small number of measurements

[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, 2016. Submitted to 53

Accuracy for the Berlin map

APSM [8]

Singular Value Thresholding

4.5 dB accuracy gain

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. 2015. 54

Limitation

• Areas with large errors remain

• These areas should be prioritized in drive tests

Original Pathloss Map Reconstructed Pathloss Map

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• Users collect the n’th sample at time tn 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

Channel prediction with Bayesian spatio-temporal inference

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Data-based simulation • Scenario: Berlin coverage map of 56 km2 from MOMENTUM project (T-Mobile), street data from

OpenStreetMap

• Vehicular mobility for 100 users generated by SUMO, users leave map

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Simulation results

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Simulation results

59 [9] Q. Liao, S. Valentin, and S. Stanczak, “Channel Gain Prediction in Wireless Networks Based on Spatial-Temporal Correlation”, in Proc. IEEE SPAWC, Jun. 2015.

With 150 m localization error

RM

SE [

dB

]

Simulation Results: MSE vs. Prediction Horizon for SNR=10 dB

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

Channel gain prediction for the Jakes-like fading channel

60 [10] S. Mekki, M. Amara, A. Feki, and S. Valentin, “Channel gain prediction for wireless links with Kalman filters and expectation-maximization,” in Proc. WCNC, 2016

What is video quality?

• No one knows exactly but it’s like an elephant

Slide inspired by Christian Timmerer https://multimediacommunication.blogspot.co.uk/

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https://multimediacommunication.blogspot.co.uk/



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?!

E2E

E2E

Not necessarily E2E

[11] M. Seufert, et al., “A Survey on Quality of Experience of HTTP Adaptive Streaming," IEEE Communications Surveys & Tutorials, Sep. 2014.

[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. 2002. 62

QoE model

QoE estimate

Video bit-rate Estimator

Waiting time Estimator

Estimates of QoE factors

…

…

Inputs: Time series (IAT, packet size,…)

…

Non-estimated QoE factors

…

• 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

QoE estimation at the edge (QoE3)

Missing

Ready and accurate

Neural networks, deep learning,

Be careful!

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Thank you www.huawei.com

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