Computing and Communications 1. Introduction Ying Cui Department - PowerPoint PPT Presentation

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 (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 3

Outline • Information theory (1948) • Coding theory (1949) • Network coding (2000) • Wireless caching (2014) • Mobile edge computing (2015) 4

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 5

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 6

COURSE OVERVIEW 7

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 1916-2001 • What is ultimate transmission rate of communication? – channel capacity [Shannon1948] C. Shannon, “A mathematical theory of communication,” Bell System Technical Journal, 1948. 8

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) Shannon’s Statue Stata Center MIT 9

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 original paper in 1948 10

Network Coding • Before advent of network coding, intermediate nodes only 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 [Yeung2000] R. Ahiswede, R. Yeung, N. Cai , S. Li and R. Yeung, “Network information flow ,” IEEE Trans. Inf. Theory, Apr. 2000. 11

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 [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. 12

Mobile Edge Computing (MEC) • Computation-intensive and latency-sensitive applications are emerging [Hu2015] – on-device cameras and embedded sensors Navigation Augmented Reality Virtual Reality • Enable cloud computing capabilities and an IT service environment at the edge of the cellular network • Reduce congestion and improve user experience [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. 13

BACKGROUND AND MOTIVATION 14

Evolution of Mobile Commun. Systems Massive MIMO SDN \ NFV D2D \ M2M Spectrum sharing 100 Mbps (DL) 5G 50 Mbps (UL) OFDMA (2020) SC-FDMA W-CDMA Smart house CDMA2000 4G Automated driving TD-SCDMA Digital IoT, AR, VR (2010) TDMA (GSM) 3G CDMA IP telephony Analog (2000) Gaming FDMA 2G HD mobile TV Web, Multimedia 1G (1990) Video conferencing Mobile TV, GPS (1980) Video on demand Text msg Picture msg Voice only 15

Main Drivers: Mobile Internet and IoT Mobile Data Traffic: Mobile Internet & IoT Connections: Thousands of time growth Up to 100 billion 16

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 on services and users sensing • 100 times reduction in energy and cost per bit 17

5G Key Capabilities: The 5G Flower • Performance Requirements • Efficiency Requirements 18

5G Technology Directions Novel Multiple Massive MIMO Full Duplex Access f 0 f 0 f 0 f 0 ... ... ... ... 1 2 M N 1 2 M N 1 2 M F 1 2 M F T R T R 发射机射 接收机射 发射机射 接收机射 频单元 频单元 频单元 频单元 DAC ADC DAC ADC 基带单元 基带单元 近端 远端 业务 业务 Ultra-dense M2M D2D networking 19

5G Challenges • Problem of information transmission with exponential growth can not be solved in a single dimension – computing – caching 20

Computing and Communications 通信性能 计算 （摩尔定律） 通信 （香农定律） 计算能力 “ computation is communication limited and communication is computation limited ” --Prof. T. Cover, Stanford Univ. 21

Caching and Communications 通信性能 存储 （摩尔定律） 通信 （香农定律） 存储能力 22

3C--Caching, Computing and Communications Communications Computing Caching 23

EXAMPLE 1: NETWORK CODING 24

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 25

EXAMPLE 2: CODED CACHING 26

Content Delivery with User Caching 27

Traditional Uncoded Caching Scheme    2, 2, 1 K N M W W 1, c 1, u server W W 2, c 2, u shared link , W W worst-case load=1/2*2=1 D u , D u , 1 2 worst-case: are different D D , 1 2 user requests W W D D 2 1 W W 1, c 1, c user caches W W 2, c 2, c 28

Coded Caching Scheme [Ali2014]    2, 2, 1 K N M W W 1,{1} 1,{2} server W W 2,{1} 2,{2}    ,| | / + 1 2 KM N  shared link W W worst-case load=1/2*1=1/2 D ,{2} D , { } 1  1 2 W  ' , { '} k D k k ' worst-case: are different D D , 1 2 user requests W W D D 2 1 W W 1,{1 } 1,{2 } user caches W W 2,{1 } 2,{2 } [Ali2014] M. A. Maddah-Ali and U. Niesen , “Fundamental limits of caching,” IEEE Trans. Inf. Theory, May 2014. 29

Traditional Uncoded Caching Scheme 2 1 N     N  cached uncached / / M M 3, 3, 1 K N M 3 3 W W 1, c 1, u server W W 2, c 2, u W W 3, c 3, u u , , W W W D , D ,u D , u shared link 1 2 3 worst-case load=2/3*3=2 worst-case: are different , , D D D 1 2 3 user requests W W W D D D 1 2 3 W W W 1, c 1, c 1, c W W W caches 2, c 2, c 2, c W W W 3, c 3, c 3, c 30

Coded Caching Scheme [Ali2014]     3 K   subfiles     3    3, 3, 1 K N M  /    1 K M N W W W 1,{3} 1,{1} 1,{2} server W W W 2,{1} 2,{2} 2,{3} W W W 3,{1} 3,{2} 3,{3}    1 , , W W W W W W    shared link ,| | / + 1 2 KM N D ,{2} D ,{ } D ,{3} D ,{1} D ,{3} D ,{2} 1 2 1 3 2 3  worst-case load=1/3*3=1 W  k ' D , { '} k ' k user requests W W W D D D 1 2 3 W W W 1,{1 } 1,{2 } 1,{3 } W W W caches 2,{1 } 2,{2 } 2,{3 } W W W 3,{2 } 3,{1 } 3,{3 } [Ali2014] M. A. Maddah-Ali and U. Niesen , “Fundamental limits of caching,” IEEE Trans. Inf. Theory, May 2014 . 31