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1896 1920 1987 2006
Computing and Communications1. 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: [email protected]
– office: SEIEE Building 5-301A
• TA: Junfeng Guo ([email protected])
• 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
• What is ultimate transmission rate of communication?
– channel capacity
8
1916-2001
[Shannon1948] C. Shannon, “A mathematical theory of communication,” Bell System Technical Journal, 1948.
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)
9Stata CenterShannon’s Statue MIT
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
11
[Yeung2000] R. Ahiswede, R. Yeung, N. Cai, S. Li and R. Yeung, “Network information flow ,” IEEE Trans. Inf. Theory, Apr. 2000.
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
12
[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.
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
13
Navigation Virtual RealityAugmented 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.
BACKGROUND AND MOTIVATION
14
Evolution of Mobile Commun. Systems
15
1G(1980)
2G(1990)
3G(2000)
4G(2010)
5G(2020)
Analog FDMA
DigitalTDMA (GSM)
CDMA
W-CDMACDMA2000TD-SCDMA
OFDMASC-FDMA
Massive MIMOSDN \ NFV
D2D \ M2MSpectrum sharing
Voice only
Text msgPicture msg
Web, MultimediaMobile TV, GPS
Video on demand
IP telephonyGaming
HD mobile TVVideo conferencing
Smart houseAutomated driving
IoT, AR, VR
100 Mbps (DL)50 Mbps (UL)
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
19
Massive MIMONovel Multiple
AccessFull Duplex
DAC ADC
... ...
接收机射频单元
业务
基带单元
1 2N
TM 1 2N
RM
f0
f0
f0
近端
DAC ADC
... ...
接收机射频单元
基带单元
业务
1 2F
TM 1 2F
RM
f0
远端
发射机射 频单元
发射机射 频单元
Ultra-dense networking M2M D2D
5G Challenges
• Problem of information transmission with exponential growth can not be solved in a single dimension
– computing
– caching
20
Computing and Communications
21
“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
22
Caching and Communications
通信
(香农定律)
存储(摩尔定律)
存储能力
通信性能
3C--Caching, Computing and Communications
23
Communications
Computing Caching
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, 1K N M
server
shared link
user requests
user caches
1 2, ,,D u D uW W
1,cW1,uW
2,cW 2,uW
1,cW
2,cW
1,cW
2,cW
1DW2DW
28
worst-case: are different 1 2,D D
worst-case load=1/2*2=1
Coded Caching Scheme [Ali2014]
2, 2, 1K N M
server
shared link
user requests
user caches
1 2,{2} { }1,D DW W
1,{1}W1,{2}W
2,{1}W 2,{2}W
}1,{1W
}2,{1W
}1,{2W
}2,{2W
1DW2DW
29
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.
'' , { '}kk D kW
/ 1 2,| +| KM N
3, 3, 1K N M
1DW2DW
server
shared link
user requests
caches
Traditional Uncoded Caching Scheme
3DW
c1,W u1,W
c2,Wu2,W
c3,W u3,W
c1,W
c2,W
c3,W
c1,W
c2,W
c3,W
c1,W
c2,W
c3,W
1 2 3, ,u u,u , ,D D DW W W
3
1/M N
3/
2M N cached uncached
worst-case: are different 1 2 3, ,D D D
30
worst-case load=2/3*3=2
3, 3, 1K 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 DW W W W W W
1,{1}W 1,{2}W 1,{3}W
2,{1}W2,{2}W 2,{3}W
3,{1}W 3,{2}W 3,{3}W
}1,{1W
}2,{1W
}3,{1W
}1,{2W
}2,{2W
}3,{2W
}1,{3W
}2,{3W
}3,{3W
subfiles33
/ 1K N
K
M
1DW2DW
3DW
'' , { '}kk D kW
/ 1 2,| +| KM N
31
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.
Load Comparison
• Worst-case load of traditional uncoded caching
• Worst-case load of coded caching
32
( ) (1 / )UR M K M N
local caching gain load without caching
normalized local cache size
relevant if local cache size on order of # of files
C( ) (1 / )1 /
R M K M NKM N
1
local caching
gain
load without caching
normalized global cachesize
global caching
gain relevant if global cache size on order of # of files
relevant if local cache size on order of # of files
coded multicasting gain available simultaneously for all requests
EXAMPLE 3: MOBILE EDGE COMPUTING
33
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)
34
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 other operations
• High requirements for computation capability and computation power
35
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
36
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
37
[email protected]/Personal/yingcui
38