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1896 1920 1987 2006 Computing and Communications 1. Introduction Ying Cui Department of Electronic Engineering Shanghai Jiao Tong University, China 2017, Autumn 1 COURSE INFORMATION 2 Lecture Time: Monday 8:00-10:00am, Sep 11-Dec 25

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1896 1920 1987 2006

Computing and Communications

  • 1. Introduction

Ying Cui Department of Electronic Engineering Shanghai Jiao Tong University, China 2017, Autumn

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COURSE INFORMATION

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Lecture

  • Time: Monday 8:00-10:00am, Sep 11-Dec 25 (Week

1-16)

  • Venue: Dongshang 301
  • Instructor: Prof. Ying Cui, IWCT, Dept. of EE

– webpage: http://iwct.sjtu.edu.cn/personal/yingcui/ – email: cuiying@sjtu.edu.cn – office: SEIEE Building 5-301A

  • TA: Junfeng Guo (xxxholic@sjtu.edu.cn)
  • No textbook, research papers as references

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Outline

  • Information theory (1948)
  • Coding theory (1949)
  • Network coding (2000)
  • Wireless caching (2014)
  • Mobile edge computing (2015)

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Requirements and Grading

  • Form 7 study groups with 5 students/group
  • Presentation (40%)

– 30-min presentation for each group, around 6 mins/student – present 5 papers in a related field

  • Report (60%)

– 5-page, double-column report (IEEE conference style, latex) – a review of >=5 papers in a related field and something interesting beyond the existing literature

  • e.g., a comparison of different approaches in different papers, a

new problem formulation and/or solution

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Goal

  • Enrich knowledge of classic and new theories and

technologies in the area of wireless communications

  • Understand how computations and communications

jointly improve performance of wireless networks

  • Develop skills needed to read and write research

papers

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COURSE OVERVIEW

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Information Theory

  • In early 1940s, it was thought impossible to send

information at a positive rate with negligible error probability over a noisy channel

  • In 1948, Claude Shannon surprised the community in

[Shannon1948]

– error probability can be made nearly zero for all communication rates below channel capacity

  • What is ultimate transmission rate of communication?

– channel capacity

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1916-2001

[Shannon1948] C. Shannon, “A mathematical theory of communication,” Bell System Technical Journal, 1948.

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Father of Information Theory

  • -Claude Shannon (1916-2001)
  • Found information theory with a landmark paper

[Shannon1948], in 1948 (at age of 32)

  • Found digital circuit design theory in his master

thesis at MIT, in 1937 (at age of 21)

  • Contribute to the field of cryptanalysis for national

defense during Word War II (by age of 29)

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Stata Center Shannon’s Statue MIT

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Coding Theory

  • How to achieve channel capacity?

– channel coding (forward error correction)

  • Introduce redundancy for controlling errors in data

transmission over a noisy channel

  • Coding theory has been developed during the long

search for simple good codes since Shannon’s

  • riginal paper in 1948

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Network Coding

  • Before advent of network coding, intermediate nodes
  • nly forward incoming data flows

– independent data flows are kept separate

  • Around 2000, R. Yeung et al. proposed network coding

– intermediate nodes not only forward but also process (combine) incoming independent data flows – destination nodes decode desired data flows from receiving combined data flows – combining independent flows better tailors network traffic to network environment

  • Increase network throughput

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[Yeung2000] R. Ahiswede, R. Yeung, N. Cai, S. Li and R. Yeung, “Network information flow ,” IEEE Trans. Inf. Theory,

  • Apr. 2000.
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Wireless Caching

  • Shift of wireless communication services

– connection-oriented to content-oriented services

  • Name content (named data object, NDO)
  • Cache popular contents at wireless edge

– caching at BSs: femto caching by Caire et al. [Carie2013] – caching at end users: coded caching by Ali and Niesen

[Ali2014]

  • Reduce delay, alleviate backhaul burden and load of

wireless links

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[Caire2013] K. Shanmugam, N. Golrezaei, A. Dimakis, A. Molish and G. Caire, “FemtoCaching: wireless video content delivery through distributed caching helpers,” IEEE Trans. Inf. Theory, Dec. 2013. [Ali2014] M. A. Maddah-Ali and U. Niesen, “Fundamental limits of caching,” IEEE Trans. Inf. Theory, May 2014.

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Mobile Edge Computing (MEC)

  • Computation-intensive and latency-sensitive

applications are emerging [Hu2015]

– on-device cameras and embedded sensors

  • Enable cloud computing capabilities and an IT service

environment at the edge of the cellular network

  • Reduce congestion and improve user experience

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Navigation Virtual Reality Augmented Reality

[Hu2015] Y. C. Hu, M. Patel, D. Sabella, N. Sprecher, and V. Young, “Mobile edge computing - a key technology towards 5g,” ETSI White Paper, vol. 11, 2015.

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BACKGROUND AND MOTIVATION

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Evolution of Mobile Commun. Systems

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1G

(1980)

2G

(1990)

3G

(2000)

4G

(2010)

5G

(2020)

Analog FDMA Digital TDMA (GSM) CDMA W-CDMA CDMA2000 TD-SCDMA OFDMA SC-FDMA Massive MIMO SDN \ NFV D2D \ M2M Spectrum sharing

Voice only Text msg Picture msg Web, Multimedia Mobile TV, GPS Video on demand IP telephony Gaming HD mobile TV Video conferencing Smart house Automated driving IoT, AR, VR

100 Mbps (DL) 50 Mbps (UL)

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Main Drivers: Mobile Internet and IoT

Mobile Data Traffic: Mobile Internet & IoT Connections: Thousands of time growth Up to 100 billion

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Vision of 5G Life

  • Fiber-like access data rate
  • “Zero” latency user

experience

  • Up to 100 million

connections/km^2

  • Consistent experience

under diverse scenarios

  • Smart optimization based
  • n services and users

sensing

  • 100 times reduction in

energy and cost per bit

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5G Key Capabilities: The 5G Flower

  • Performance

Requirements

  • Efficiency

Requirements

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5G Technology Directions

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Massive MIMO Novel Multiple Access Full Duplex

DAC ADC ... ... 接收机射 频单元 业务 基带单元 1 2 N T M 1 2 N R M f0 f0 f0 近端 DAC ADC ... ... 接收机射 频单元 基带单元 业务 1 2 F T M 1 2 F R M f0 远端 发射机射 频单元 发射机射 频单元

Ultra-dense networking M2M D2D

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5G Challenges

  • Problem of information transmission with

exponential growth can not be solved in a single dimension

– computing – caching

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Computing and Communications

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“computation is communication limited and communication is computation limited”

  • -Prof. T. Cover, Stanford Univ.

通信 （香农定律） 计算 （摩尔定律）

计算能力 通信性能

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Caching and Communications

通信 （香农定律） 存储 （摩尔定律）

存储能力 通信性能

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3C--Caching, Computing and Communications

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Communications Computing Caching

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EXAMPLE 1: NETWORK CODING

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Information Exchange

  • Node A transmits x1 to Node C via Relay B and Node

C transmits x2 to Node A via Relay B

  • Network coding approach uses one transmission less

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EXAMPLE 2: CODED CACHING

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Content Delivery with User Caching

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Traditional Uncoded Caching Scheme

2, 2, 1 K N M   

server shared link user requests user caches

1 2

, ,

,

D u D u

W W

1,c

W

1,u

W

2,c

W

2,u

W

1,c

W

2,c

W

1,c

W

2,c

W

1

D

W

2

D

W

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worst-case: are different

1 2

, D D

worst-case load=1/2*2=1

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Coded Caching Scheme [Ali2014]

2, 2, 1 K N M   

server shared link user requests user caches

1 2

,{2} { } 1 , D D

W W 

1,{1}

W

1,{2}

W

2,{1}

W

2,{2}

W

} 1,{1

W

} 2,{1

W

} 1,{2

W

} 2,{2

W

1

D

W

2

D

W

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worst-case load=1/2*1=1/2 worst-case: are different

1 2

, D D [Ali2014] M. A. Maddah-Ali and U. Niesen, “Fundamental limits of caching,” IEEE Trans. Inf. Theory, May 2014.

'

' , { '}

k

k D k

W

/ 1 2 ,| + | KM N   

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3, 3, 1 K N M   

1

D

W

2

D

W

server shared link user requests caches

Traditional Uncoded Caching Scheme

3

D

W

c 1,

W

u 1,

W

c 2,

W

u 2,

W

c 3,

W

u 3,

W

c 1,

W

c 2,

W

c 3,

W

c 1,

W

c 2,

W

c 3,

W

c 1,

W

c 2,

W

c 3,

W

1 2 3

, ,u u , u,

,

D D D

W W W

3 1 / M N  3 / 2 M N 

cached uncached

worst-case: are different

1 2 3

, , D D D

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worst-case load=2/3*3=2

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3, 3, 1 K N M   

server shared link user requests caches

Coded Caching Scheme [Ali2014]

1 2 1 3 2 3

,{2} ,{ } ,{3} ,{1} ,{3} ,{2} 1 ,

,

D D D D D D

W W W W W W   

1,{1}

W

1,{2}

W

1,{3}

W

2,{1}

W

2,{2}

W

2,{3}

W

3,{1}

W

3,{2}

W

3,{3}

W

} 1,{1

W

} 2,{1

W

} 3,{1

W

} 1,{2

W

} 2,{2

W

} 3,{2

W

} 1,{3

W

} 2,{3

W

} 3,{3

W

subfiles

3 3 / 1 K N K M              

1

D

W

2

D

W

3

D

W

'

' , { '}

k

k D k

W

/ 1 2 ,| + | KM N   

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worst-case load=1/3*3=1

[Ali2014] M. A. Maddah-Ali and U. Niesen, “Fundamental limits of caching,” IEEE Trans. Inf. Theory, May 2014.

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Load Comparison

  • Worst-case load of traditional uncoded caching
  • Worst-case load of coded caching

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( ) (1 / )

U

R M K M N   local caching gain load without caching normalized local cache size

relevant if local cache size

  • n order of # of files

C(

) (1 / )1 / R M K M N KM N    １

local caching gain load without caching normalized global cache size global caching gain

relevant if global cache size on order of # of files relevant if local cache size

  • n order of # of files

coded multicasting gain available simultaneously for all requests

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EXAMPLE 3: MOBILE EDGE COMPUTING

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Navigation

  • Monitor and control the movement of a craft or

vehicle from one place to another

  • Four general categories

– land navigation – marine navigation – aeronautic navigation – space navigation

  • Most popular navigation systems:

– Global Positioning System (GPS) – BeiDou Navigation Satellite System (BDS)

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Computations in Navigation

  • Obtain location information

– obtain accurate locations of multiple users at the same time

  • Plan route

– integrate a series of factors to better plan a path

  • Process panoramic images

– process a series of images due to forward, backward and

  • ther operations
  • High requirements for computation capability and

computation power

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Augmented Reality (AR)

  • A live direct or indirect view of a physical, real-world

environment whose elements are "augmented" by computer-generated sensory input such as sound, video, graphics or GPS data

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Augmented Reality (AR)

  • Five critical components in an AR application:

– a video source

  • obtain raw video frames from mobile camera

– a tracker

  • track user position

– a mapper

  • build environment model

– an object recognizer

  • identify known objects

– a render

  • prepare processed frame for display

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cuiying@sjtu.edu.cn iwct.sjtu.edu.cn/Personal/yingcui

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