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 2018, Autumn 1

COURSE INFORMATION 2

Lecture • Time: Monday 8:00-9:40am, Sep 10-Dec 24 (Week 1- 16) • Venue: Zhong 107 • 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 • No textbook, research papers as references 3

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

Requirements and Grading • Presentation (40%) – present one paper – about 12 mins/person – last 4 weeks • Report (60%) – 3-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 computing 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 • 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) Stata Center Shannon’s Statue 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

Optimization • Optimization is an abstraction of the problem of making the best possible choice from a set of candidate choices – portfolio optimization, device sizing in electronic circuits, data fitting • Optimization has become an important tool in many areas – electronic design automation, automatic control systems, and optimal design problems arising in civil, chemical, mechanical, and aerospace engineering • The general optimization problem is very difficult to solve • A few problem classes can be solved reliably and efficiently [Boyd2004] Boyd and L. Vandenberghe, Convex optimization, Cambridge university press, 2004. 12

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 et al. [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. 13

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. 14

Wireless Multimedia Transmission • Video providing immersive viewing experience – multi-view video (MVV) • capture from different angles with multiple cameras • user can freely select among multiple view angles – 360 virtual reality (VR) video • capture from all directions with omnidirectional camera • user can freely watch the scene of interest in any viewing direction – large size, heavy burden to wireless networks unicast • Improve transmission efficiency current multicast FoV user1 Original view Synthesized view viewing direction n v 1.0 1.0 User 1 r � FoVs 1.0 1 current c 1.0 1.0 that may 4 1.0 1.5 FoV be 2.0 User 2 3 2.5 Multicast Scene 2.0 2.0 user2 r � 2.0 1.0 watched 2 2.5 2 Sever 2.5 2.5 3.0 User 3 3.0 3.0 4.0 1 4.0 r � 3.0 current n h 3 3.5 1 2 3 4 5 6 7 8 FoV 4.0 4.0 4.0 4.0 User 4 tiled 360 VR video 15 user3 r � 4.0 4

BACKGROUND AND MOTIVATION 16

Evolution of Mobile Commun. Systems Massive MIMO D2D \ M2M Spectrum sharing 100 Mbps (DL) 50 Mbps (UL) 5G OFDMA SC-FDMA (2020) MIMO 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 17

Main Drivers: Mobile Internet and IoT Mobile Data Traffic: Mobile Internet & IoT Connections: Thousands of time growth Up to 100 billion 18

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 19

5G Key Capabilities: The 5G Flower • Performance Requirements • Efficiency Requirements 20

5G Technology Directions Novel Multiple Massive MIMO Full Duplex Access f 0 f 0 f 0 f 0 ... ... ... ... N N F M F 1 2 M 1 2 M 1 2 M 1 2 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 21

5G Challenges • Problem of information transmission with exponential growth can not be solved in a single dimension – computing – caching 22

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. 23

3C—Communications, Computing and Caching Communications Computing Caching 24

EXAMPLE 1: NETWORK CODING 25

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 26

EXAMPLE 2: CODED CACHING 27

Content Delivery with User Caching 28

Traditional Uncoded Caching Scheme = = = K 2, N 2, M 1 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 1 2 W W 1, c 1, c user caches W W 2, c 2, c 29

Coded Caching Scheme [Ali2014] = = = K 2, N 2, M 1 W W 1,{1} 1,{2} server W W 2,{1} 2,{2} Í = = S K S ,| | KM / N + 1 2 shared link Å W W worst-case load=1/2*1=1/2 D ,{2} D , { } 1 Å W 1 2 Î k ' S D , S � { '} k k ' worst-case: are different D D , 1 2 user requests W W D D 1 2 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. 30