machine learning for wireless networks: challenges and ...jfro/journeesprecedentes/... · rnp/ o...

1

Machine learning for Wireless

Networks: Challenges and

opportunities

Mérouane Debbah

Mathematical and Algorithmic Sciences Lab, Huawei, France

8th of June, 2018

HUAWEI TECHNOLOGIES CO., LTD. 华为保密信息，未经授权禁止扩散

Huawei [Wow Way]

180,000Employees

1580,000 No. 70170+ No. 83R&D Institutes

& Centers

R&D employees

Interbrand'sTop 100 Best Global Brands

Countries Fortune Global 500


HQ in Boulogne-

Billancourt

1 Open Lab (opening

April 2018)

85 researchers in

applied

mathematics

4 R&D centers,

Academic

cooperation with

Major Universities

950+ employees,

among with 150

in R&D

Huawei [in France]


The cost of Understanding


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.


From Learning to recognize


Why would we learn to optimize in Communications?

• « A Mathematical Theory of Communication », Bell System Technical Journal, 1948, C. E. Shannon


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

9

Networks are becoming very complex

B2C

B2H

B2V

WTTx

4GHD Video

Voice

MBB

Data

Smart

Transportation

Smart Energy

AR/VR

WTTx

VoLTE

Smart

manufacturingSmart

meter

3D

Video

DroneuRLLC

eMBB

mMTC

One Network 4 Services One Network Hundreds of

Services

V

R

Tactile

internetAR

Multi-person video

call

Autonomous car

Real time gaming

Disaster alert

Automotive

ecall

Sen

sor

s

Device remote control

Source: GSMA/Huawei X Labs

Diversified Services SLA

Requirement

HISILICON SEMICONDUCTORHUAWEI TECHNOLOGIES CO., LTD.

Wireless AI: Key Technology AI/ML

MLCV

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

TRMRRM

RTT IRF ANT

NLPSMBB

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

RTTRRM

OSS/SON

MBB/Core Conf. and Opti.

Policy & Monitor

HISILICON SEMICONDUCTORHUAWEI 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

HUAWEI TECHNOLOGIES CO., LTD.

35pt

32pt

) :18pt

AI in Wireless

AI NE AINetwork

AI Data AI chipset AI service

AI base station

Network auto configuration

Failure detectionPolicy management

RAN minimal deployment

RAN AI chipset

DTX adaptation

Self operation

MBB AI Slice

E2E Performance learning

Comp mode selectionLink 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

mA

rchite

cture

AI Center Trainer• Learner• Policy• Explorer

eNB AI Agent• Decides action• Determine state

Feature Statistic Collector• 1 Collects Statistics• 2 Applies Action


35pt

32pt

) :18pt

Example


35pt

32pt

) :18pt

Why do we need energy efficiency in communications?


35pt

32pt

) :18pt


35pt

32pt

) :18pt

Deep Forward Neural Network


35pt

32pt

) :18pt

GEE Maximization in Interference Limited Networks


35pt

32pt

) :18pt

GEE Maximization Problem


35pt

32pt

) :18pt

ANN Training & Validation


35pt

32pt

) :18pt

Online Implementation


35pt

32pt

) :18pt

Building the training set


35pt

32pt

) :18pt

Neural Network Architecture


35pt

32pt

) :18pt

GEE Comparison. LMMSE reception is used.


35pt

32pt

) :18pt

Power Consumption. LMMSE is used


35pt

32pt

) :18pt

Example

30

Edge User THP Affect User Experience Than Average THP

25

%

<1 1~

5

5~10 10~1

5

>15Mbps

15

%

20

%21

%19

%

Edge User Throughput and VoLTE Background

100 + 50 + + =Feature Parameter

> 𝟏𝟎𝟑𝟎Combination

Too Many Factors Affect Edge Throughput and VoLTE, Make

Optimization Too Difficult

VoLTE PLR(Packet Loss Rate) Correlated with

MOS

4.15 4.08 3.84 3.73.02 2.51 2.04

0

2

4

6

MOS

PLR

31

AI Assisted VoLTE and Edge User THP Optimization

> 𝟏𝟎𝟑𝟎Combination

240Combination

150 Factors TopN Factors

Combination

1

Optima

Data Modeling

Recognition

Extracting Feature

Agent Evaluation

Reinforcement Learning

Transfer Learning

1st Cell, 14 Round Converge

…

Experience

Library

Agent Evaluation

Reinforcement Learning

N Cell, 7 Round Converge

∙∙∙Importance

0 1

0.75

0.65

0.1

0.08

Factor1

Factor2

Importance Ratio Evaluation Importance Ratio Rank

Top N Factors AnalysisOptimal Value

Search

32

Innovation in Turkey – Edge THP Optimization

Test Area: Ankara Scope: 699 Site, 2281LCell

Ankara Top 10% Gain Cells

50%

40%

32%

Throughput Gain Distribution

0 - 10% 10 - 30%

Whole Network Gain:

User Num

4

6

8

10

9/7

/20

17

9/9

/20

17

9/1

1/2

01

7

9/1

3/2

01

7

9/1

5/2

01

7

9/1

7/2

01

7

9/1

9/2

01

7

9/2

1/2

01

7

9/2

3/2

01

7

9/2

5/2

01

7

9/2

7/2

01

7

9/2

9/2

01

7

10/1

/201

7

10/3

/201

7

10/5

/201

7

10/7

/201

7

10/9

/201

7

10/1

1/2

01

7

User Throughput < 1Mbps Ratio (%)

Optimization

Optimization

<1 Mbps Use Ratio Decrease while User Num Increase.

33

Innovation in Turkey – VoLTE Optimization

Test Area: Bursa Scope: 188 Site, 877 LCell

Whole Network

Gain:

58%

10%

32%

DL PLR Gain Distribution

0 - 10% 10 - 30% >30%

62%20%

18%

UL PLR Gain Distribution

0 - 10% 10 - 30% >30%

Bursa Top 10% Gain Cells

00.5

11.5

22.5

33.5

9/7

/20

17

9/9

/20

17

9/1

1/2

01

7

9/1

3/2

01

7

9/1

5/2

01

7

9/1

7/2

01

7

9/1

9/2

01

7

9/2

1/2

01

7

9/2

3/2

01

7

9/2

5/2

01

7

9/2

7/2

01

7

9/2

9/2

01

7

10/1

/201

7

10/3

/201

7

10/5

/201

7

10/7

/201

7

10/9

/201

7

10/1

1/2

01

7

DL Packet Loss Rate (%)

0.30.35

0.40.45

0.50.55

0.60.65

9/7

/20

17

9/9

/20

17

9/1

1/2

01

7

9/1

3/2

01

7

9/1

5/2

01

7

9/1

7/2

01

7

9/1

9/2

01

7

9/2

1/2

01

7

9/2

3/2

01

7

9/2

5/2

01

7

9/2

7/2

01

7

9/2

9/2

01

7

10/1

/201

7

10/3

/201

7

10/5

/201

7

10/7

/201

7

10/9

/201

7

10/1

1/2

01

7

UL Packet Loss Rate (%)

Optimization

Optimization

DL/UL PLR Decrease Dramatically

PLR: Packet Loss Rate


35pt

32pt

) :18pt

Model versus Data

machine learning for wireless networks: challenges and ...jfro/journeesprecedentes/... · rnp/ o...

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