Emerging Technologies in 5Gj25ni/ECE710/5G_Update.pdf · SDN NFV Cloud computing Data center Radio Access Tech: Polar Coding Massive MIMO ... • Mobile/IoT devices everywhere (Peaceful

Emerging Technologies in 5G

ENGINEERING

ELECTRICAL AND COMPUTER ENGINEERING

Broadband Communication Research Group (BBCR)

Dept. of ECE, University of Waterloo200 University Avenue West

Waterloo, Ontario, Canada N2L 3G1

Nan Cheng

Guest Lecture

ECE 710 (Lectured by Professor Xuemin Shen)

March 20, 2018

• Introduction to 5G Networks

• Software-Defined Network

• Big Data Analytics

• Polar Coding

• Vehicular Networks

• Conclusions

Outline

2




• Polar Coding


• Conclusions

Outline

3

Evolution of Mobile Networks

4

Mobile 1G: Foundation of Mobile

5

Mobile 2G: Enabled More Users

6

Mobile 2G: Enabled More Users

7

Mobile 3G: Mobile for Data

8

Mobile 3G: Mobile for Data

9

Mobile 4G: Mobile Broadband

10

What will 5G be?

• 5G mobile networks need

• Support rich applications and services

• Satisfy diversified requirements

• Evolutionary network architecture

• Orchestration of new and existing technologies11

5G Provides A Platform for New

Connected Services

12

VR/AR: Ultra High Data Rates

13

Autonomous Car: Reliable/Latency

14

Autonomous Car: Reliable/Latency

Communicating intent and sensor data even in

challenging real world conditions

15

Internet of Everything (IOE):

Trillions Connectivity

• Very dense & huge no. of devices

• Low power consumption

• Long battery life16

5G: Key Performance Indicators

17

5G Use Cases/Scenarios

Use case development completed at

3GPP SA (TR 22.891):

Enhanced Mobile BroadBand (eMBB):

10-20Gbps network-level data rate.

Ultra-Reliable Low latency

Communications (URLLC):

at most 1ms delay experience

Massive Machine-Type Communication

(mMTC):

106 links/km2 connection density.

18

5G Distinguished Features

5G

Features

Scalable, Flexible,

Sustainable 03.

Diversified Capabilities 02.

Improved KPI 01 06 New Technologies

05Enhanced Security

04 Reduced Costs

19

5G New Opportunities

New Ecosystem

Software-based

Vertical Markets Big Data Analytics

IoT

HetNetsIaaS

New Research Issues

SecurityCloud

20

21

5G Key Technologies

RSU

RSU

3G/4G 3G/4G

3G/4G

802.11p

RSURSU

RSU

RSU

3G/4G 3G/4G

3G/4G

802.11p

RSURSU

Seamless Handover

D2D

VANET scenarios

Cloud

Fog nodes Fog nodes

SDN/NFV Controller

SDN/NFV Controller

Vehicles Vehicles

Core Network:

SDN

NFV

Cloud computing

Data center

Radio Access Tech:

Polar Coding

Massive MIMO

Full Duplex

Millimeter Wave

Access Network:

Vehicular Network

IoT

Fog computing

Big

Data

Analytics/A

rtificial In

tellig

ent




• Polar Coding


• Conclusions

Outline

22

23

5G Requirements on Networking

• Global control

• Data forwarding

• Network management

• Intelligent control with big data and AI

• Flexible

• Scalable

• Larger network

• Hardware/software update

• New application/service

*Some slides are from Prof. Raouf Boutaba slides introducing SDN at University of Waterloo

24

Network Planes

• Data Plane

• responsible for forwarding packets

• Control Plane

• responsible for defining the behavior of the network

• Service Plane

• provides services beyond packet forwarding

• Management Plane

• configures routing protocol, monitors network devices

and handle anomalies

25

Data Plane

• Responsible for moving packets

• local, per-router function

• Input port->output port in a router

• Other data plane functions

• Packet scheduling and buffering

• Packet fragmentation

• Bandwidth metering

• Access control

• Traffic monitoring

26

Control Plane

• Responsible for moving packets

• Network-wide logic

• Determines how packet is routed among

routers along end-end path from source

host to destination host

• Route computation function

• Other control plane functions

• Topology discovery

• Access control list generation

• QoS enforcement

27

Service Plane

• Provides services beyond packet forwarding

• Load balancers, web proxy, web cache

• Firewall

• Intrusion detection system

• Mobility control

28

Management Plane

• Configures routing protocol, monitors network devices

and reacts to anomalies

• Responsible for managing FCAPS

• Fault

• Configuration

• Accounting

• Performance

• Security

• Provisions resources for network services

29

Traditional Networking

• Control plane is vertically integrated with data plane

• Each router runs its own control plane

30

Traditional Routing

• Traditional routing protocols are distributed

• Routers exchange connectivity info. with each other

• Each router builds its own routing table

• Forwarding tables (FIB) are populated based on

information available in the routing table

31

Distributed Control Plane

• Distributed per-router control plane

• Vertically integrated router contains switching hardware,

runs proprietary implementation of Internet standard

protocols (IP, RIP, ISIS, OSPF, BGP) in proprietary router

OS (e.g., Cisco IOS)

32

Traditional Network Element

33

Why Not Traditional Networking in 5G?

• Lack of programmability

• Changes in the network requires manual reconfiguration

• Error-prone and time consuming

• Network becomes hard to scale

• Vendor dependency

• Handful of vendors have monopoly in the market

• Lack of open interface

• Software vertically integrated with hardware

• Over specified

• Slow protocol standardization

34

Why Not Traditional Networking in 5G?

• Less opportunities for innovation

• Only vendors can write the code

• Slow time-to-market for new services/features

• High operational cost

• More than 50%

• Human error still causes most outages

• Buggy software

• Hardware with 20+ million lines of code

• Cascading failures, vulnerabilities, etc.

35

Software-Defined Network (SDN)

• Software Defined Networking (SDN) emerged as a

new networking architecture that

• Separates control plane from data plane

• Software running on a logically centralized server

remotely controls network hardware

• Open standards (vendor neutral)

• Directly programmable

• Facilitates automation & programmability

36

SDN Architecture

37

Network Evolution with SDN

38

Why SDN in 5G?

• Separation of control and data planes

• Enables independent development of new features

without hardware modification

• Global network view at control plane

• Network-wide view allows easier application of policies

• Management Automation

• Minimize human intervention

• Lower operational cost

• Monitoring

• On-demand monitoring of resources

• Dynamically change what to monitor

• Application performance can be improved by advanced

monitoring features

39

Why SDN in 5G?

• Elasticity

• Resources can be scaled up or down on-demand

• More Flexible/Easier Performance Management

• Traffic engineering

• Capacity optimization

• Load balancing

• Faster failure recovery

• Network Function Integration

• On-demand provisioning of resources

• Firewalls, IDS, IPS, etc.




• Polar Coding


• Conclusions

Outline

40

41

Big Data Analytics in 5G

• Proliferation of network traffic (volume and type)

• Highly real-time streaming data (Youtube, Twitch, Douyu)

• Mobile/IoT devices everywhere (Peaceful city, mobile phone,

AR/VR, e-health…)

• Increased complexity of network and traffic monitoring

and analysis

• Difficulty in predicting and generalizing application

behavior

• Too many sources of knowledge to process by human

• Too many black boxes

• Need for aggregated solutions: get the value out of data

42

Data Usage in Mobile Networks

• Mobile data traffic will grow at a compound annual

growth rate (CAGR) of 47%

• Mobile devices will grow at a CAGR of 8%

• Of all IP traffic in 2021, 50% WiFi, 30% wired, 20%

mobile

• By 2021, there will be 8.3B mobile devices, and 3.3B

M2M connections

43

Traffic in Data Center Networks

• IoT/analytics/database workloads are growing fast

• Video and social networking will lead the increase

• Cloud (virtualized data center) traffic dominates

growth

44

Big Data Generations

Data collected:Smart phone

Movement

Traffic

Environment

Electricity

Monitor

Light

Etc.

45

How is Data Generated in 5G?

• What is monitored in the network?

• System and services

• Available, reachable, energy consumption

• Resources

• Expansion planning, maintain availability

• Performance

• Round-trip-time, throughput

• Changes and configurations

• Documentation, revision control, logging

• NOC: Network Operations Center

• Coordinate tasks

• Report status of network and services

• Process network-related incident and complaints

46

Hierarchy of Network Data Analytics

• Multiple layers of network data analytics

• Historical analytics:

• Service plan creation, network planning, subscriber profiling

• Near real-time analytics:

• Network optimization, location-based services

• Real-time analytics:

• Dynamic policy, self-optimizing networks

• Predictive analytics:

• Traffic demand forecasting, fault avoidance

• Data is richer when associated to context: layer,

location, time

47

Big Data Analytics in Smart City

48

Big Data Analytics in Cloud

• Leverage high performance of Cloud techniques

• Help delivery the real-time results

• Flexible and easy-to-use visualization

49

Big Data Based Intelligence

• Real-time situational awareness

• Continuous monitoring

• Data collection & historical record

• Learning and intelligence

• Visualization

• Smart application enablement

• Traffic control, planning, optimization

• Emergency response

• Event planning & management

50

Ex1. Dynamic Resizing in a Data Center

• If we know/can predict the workload distribution, we

can control the active servers

51

Ex2. Traffic Differentiation

52

Ex3. Bandwidth Defragmentation

Bandwidth defragmentation based on real-time monitoring and forecasting

53

Ex4. Network Data Visualization

54

Artificial Intelligence (AI) in Big Data

• AI is required in all layers of

next-generation networks

• Ultimate goal is to automate

the management of the

network and make it more

efficient

• AI models are improved with

big data collected over time

55

Fundamental AI Algorithms

• Supervised learning

• Classification

• Regression

• Unsupervised learning

• Clustering

• Anomaly detection

• Semi-supervised learning

• Reinforcement learning

• Transfer learning

• Deep learning

56

Supervised Learning

• Classification

• Identifying to which of a set of categories (sub-populations) a

new observation belongs

• Regression

• Predicting variable is continuous, such as price

57

Unsupervised Learning

• Clustering

• Dimensionality reduction

• Anomaly detection

58

Evaluation Metrics in AI Algorithms

• Accuracy

• A = (TP+TN)/all

• Precision

• p = TP/(TP+FP)

• Recall

• R = TP/(TP+FN)

• F-score

• F = 2/(1/p+1/r)

Classified Positive Classified Negative

Actual Positive True Positive (TP) False Negative (FN)

Actual Negative False Positive (FP) True Negative (TN)

59

An Example: Network Anomaly Detection

• Detect network anomaly (prob. = 1%)

• Data: positive=10 (anomaly); negative=9990 (normal)

• Alg. 1: Predict all: normal

• Alg. 2: Some machine learning algorithm

60

An Example: Network Anomaly Detection

Classified

Positive

Classified

Negative

Actual Positive TP: 0 FN: 10

Actual Negative FP: 0 TN: 9990

Classified

Positive

Classified

Negative

Actual Positive TP: 9 FN: 1

Actual Negative FP: 20 TN: 9970

Alg. 2: Some machine learning algorithmAlg. 1: Predict all: normal

Accuracy

A = (TP+TN)/all

Precision

p = TP/(TP+FP)

Recall

R = TP/(TP+FN)

F-score

F = 2/(1/p+1/r)

• Accuracy

• A = 99.9%

• Precision

• p = 0

• Recall

• R = 0

• F-score

• F = 0

• Accuracy

• A = 99.79%

• Precision

• p = 0.31

• Recall

• R = 0.9

• F-score

• F = 0.46

61


• True positive rate (TPR)

• TPR = TP/(TP+FN)

• False positive rate (FPR)

• FPR = FP/(TN+FP)

• Receive operating characteristics (ROC) curve

• FPR vs TPR

62


• Confusion Matrix




• Polar Coding


• Conclusions

Outline

63

64

5G Requirements on Channel Coding

• Low decoding error

• Low coding/decoding delay

• Low decoding complexity

• Easy to implement

• Low-cost chips

• Advanced coding techniques

• Multiple code lengths

• HARQ

• Rate matching

65

Polar Codes

• Novel error correction coding mechanisms that have resulted in achieving performance very close to the limits

• Polar codes is the first coding scheme proved to achieve Shannon capacity

• Easy to implement

• 5G channel coding standard for control channel

*Some slides are from Prof. Arikan’s slides introducing polar codes at UC Berkeley,

http://www2.egr.uh.edu/~zhan2/ECE6332/slidesarikan.pdf

66

Channel Model

67

Binary Symmetric Channel Model

68

Channel Capacity

69

Main Idea of Polar Codes

70

Aggregate and Redistribute Capacity

71

Combining

72

Splitting

73

Polarization Is Common

74

Practical Polarization

Kernel

75

Two Bit-Channels

76

Binary Erasure Channel (BEC)

Erasure probability: ε

Capacity: 1- ε

77

Calculate Capacity of BEC(0.5)

0.5

0.5

0.25

0.75

78

Size 8 Construction

79

Size 8 Bit Channel Capacity

0.50.250.06250.000039

0.1211

0.1914

0.6836

0.3164

0.8086

0.8789

0.9961

80

Polar Code Example, N=8, Rate=1/2

81


82


83

Polarization

N=128 N=256

N=512 N=1024

84

Polarization Theorem

85

Decoding: Successive Cancellation

86

Performance of Polar Code (1)

87


88


Polar vs LDPC (Low-density parity check) – short code

89


Polar vs LDPC (Low-density parity check) – long code




• Polar Coding


• Conclusions

Outline

90

91

Vehicular Networks (VANETs)

Vehicle-to-Vehicle

(V2V) communications

Vehicle-to-Infrastructure

(V2I) communications

Safety applications

ITS applications

Entertainment

applicationsVehicle-to-Everything (V2X)

communications

92

VANETs Application

Traffic Notification

Remote Control

Mobile Office

Speed Management

Software Update

Collision Avoidance

Visibility Enhancing

Cooperative Driving

Road

Safety

Passenger

Infotainment

Vehicle Traffic

Optimization

Car

Manufacturer

Services

Internet access

93

Application – Safe Driving

Intersection Collision Warning Co-operative Collision Warning Lane Change Warning

Approaching Emergency vehicle Rollover Warning Work Zone Warning

Coupling/Decoupling Road Information Sharing Electronic Toll Collection

94

Application – Safe Driving

95

Application – Traffic Management

• Transportation efficiency

• Traffic jam costs 160 billion

in US in 2017

• $1,200 per driver

• 20% more fuel wasted

96

Application – Infotainment

97

Application - Self-Driving (1)

98


Sensing error Scope limitation No coordination Prohibitive

99


• Improve sensing scope• V2V: 300 meters

• V2I: even longer

• 3D sensing

• Add redundancy to sensing• Assist decision making

• Information accuracy

• AI can better utilize V2X information

• Coordination• Coordination among vehicles

• Edge-based decisions for a set of vehicles

• Improve efficiency, reduce cost

100

5G Requirements on V2X Communication

19/03/2018

101


102


103

Device-to-Device Communication

• Reliable: BS controlled

• Efficiency: spectrum reuse

• High data rate: proximate

• Low delay: proximate

• Offload the cellular

• Context-aware: discover the

surroundings and communicate

with nearby devices

W. Sun, E. G. Ström, F. Brännström, K. C. Sou, and Y. Sui, “Radio resource management for D2D-

based V2V communication,” IEEE Trans. Veh. Technol., vol. 65, no. 8, pp. 6636–6650, Aug. 2016.

104

D2D-based V2V Communication

• Outage probability

• Reliability constraint (10-5)

• Latency constraint

105

D2D-based V2V Communication

• Optimization Problem

• Maximize the CUEs’ rate

• Satisfy VUE’s constraints on reliability and delay

• Transmit 12800 bits with Pout<10-5

a. CUE sum rate b. VUE transmitted bits

Intelligent Transmission

106

Measurement Big Data

Machine LearningContext Insight

Context-Aware Design

• V2V communication

measurement

• Measurement data analysis

• Identify the context factors

• Design context-aware

intelligent protocol

• Each vehicle broadcast safety message beacon periodically.

• Position, speed, heading, acceleration, turn signal status, etc.

• For safety applications in ITS, and self-driving.

• Requirements

• Delay (beacon per 100 ms)

• Reliability (packet delivery ratio)

• Fairness (vehicles equally important)

• Connectivity (dense urban scenario)

107

V2V Safety Beaconing in VANETs

[1] N. Cheng, F. Lyu, J. Chen, W. Xu, H. Zhou, S. Zhang, and X. Shen, "Big Data Driven Vehicular Networks," IEEE Network, submitted.

[2] F. Lyu, N. Cheng, H. Zhou, W. Xu, W. Shi, J. Chen, and M. Li, "DBCC: Leveraging Link Perception for Distributed Beacon Congestion Control in VANETs," IEEE

Internet of Things Journal, submitted.

108

A Time-Division Broadcasting Scheme

• Requirements

• Delay

• Frame length 100 ms

• Reliability

• Only one vehicle transmit in a slot

• Fairness

• Dedicated slots for each vehicle

• Connectivity

• V2V performance is significantly affected by environment.

• Vehicle speed, distance

• Obstacles

• Packet may be lost even no collision. Cannot be solved by

simple TDMA MAC!

• Question:

• What factors impact the V2V performance in practice?

• How to consider the real V2V performance to improve vehicular

beaconing?

109

However?

110

V2V Measurement in Shanghai

Data Type Contents

GPS Speed, height, location

V2V DSRC Measurement Packet delay, loss

Video (front camera) Forward video

(For NLoS/LoS labelling)

Data Volume 110 GB

Measurement in Suburban, Highway, and UrbanVideo screenshot (LoS)

Video screenshot (NLoS)

People Square

• Overall PDR is high

throughout urban areas

• NLoS time is much less than

LoS time.

• NLoS significantly degrades

the V2V performance

111

Insights from Measurement Data

• Target: Max Safety Benefit

• Every vehicle assign minimum

beacon rate

• According to LoS/NLoS to

neighbors, calculate link weight

• Increase beacon rate of vehicle with

highest link weight, until max rate

• Increase beacon rate of vehicle with

2nd highest link weigth…

• Until channel capacity

112

Context-Aware MAC Design

?How to get LoS/NLoS conditions？

113

Online NLoS Condition Detection

Feature Target

PDR_1s PDR_5s PDR_10s LoS/NLoS

1 1 0.89 1

0.5 0.72 0.86 0

0.5 0.65 0.55 0

… … … …

114

Performance Evaluation




• Polar Coding


• Conclusions

Outline

115

• 5G network has much more strict KPIs

• 5G network enables a whole set of potential

applications/services

• 5G uses a series of new technologies:

• SDN

• Big data analytics/AI

• Polar coding

• V2X communication

Conclusion

116

117

THANKS!

Documents

Emerging Technologies in 5Gj25ni/ECE710/5G_Update.pdf · SDN NFV Cloud computing Data center Radio Access Tech: Polar Coding Massive MIMO ... • Mobile/IoT devices everywhere (Peaceful