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E-book: Network Coding: An Introduction

(Massachusetts Institute of Technology), (California Institute of Technology)
  • Format: PDF+DRM
  • Pub. Date: 14-Apr-2008
  • Publisher: Cambridge University Press
  • Language: eng
  • ISBN-13: 9780511402098
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  • Format: PDF+DRM
  • Pub. Date: 14-Apr-2008
  • Publisher: Cambridge University Press
  • Language: eng
  • ISBN-13: 9780511402098
Other books in subject:

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Network coding promises to significantly impact the way communications networks are designed, operated, and understood. The first book to present a unified and intuitive overview of the theory, applications, challenges, and future directions of this emerging field, this is a must-have resource for those working in wireline or wireless networking. *Uses an engineering approach - explains the ideas and practical techniques *Covers mathematical underpinnings, practical algorithms, code selection, security, and network management *Discusses key topics of inter-session (non-multicast) network coding, lossy networks, lossless networks, and subgraph-selection algorithms Starting with basic concepts, models, and theory, then covering a core subset of results with full proofs, Ho and Lun provide an authoritative introduction to network coding that supplies both the background to support research and the practical considerations for designing coded networks. This is an essential resource for graduate students and researchers in electronic and computer engineering and for practitioners in the communications industry.

Unified overview of the theory, applications, challenges and future directions of network coding for graduate students, researchers and practitioners.

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An overview of the theory, applications, challenges and future directions of network coding for graduate students, researchers and practitioners.
Preface ix
Introduction 1(1)
What is network coding?
1(1)
What is network coding good for?
2(6)
Throughput
2(3)
Robustness
5(2)
Complexity
7(1)
Security
8(1)
Network model
8(3)
Outline of book
11(1)
Notes and Further reading
12(1)
Lossless Multicast Network Coding
13(36)
Notational conventions
13(1)
Basic Network model and multicast network coding problem formulation
13(1)
Delay-free scalar linear network coding
14(3)
Solvability and throughput
17(4)
The unicast case
17(1)
The multicast case
18(1)
Multicasting from multiple source nodes
19(1)
Maximum throughput advantage
19(2)
Multicast network code construction
21(6)
Centralized polynomial-time construction
21(2)
Random linear network coding
23(4)
Packet networks
27(3)
Distributed random linear coding for packet networks
28(2)
Networks with cycles and convolutional network coding
30(4)
Algebraic representation of convolutional network coding
31(3)
Correlated source processes
34(3)
Joint Source-network coding
35(2)
Separation of source coding and network coding
37(1)
Notes and further reading
37(2)
Appendix: Random network coding
39(10)
Inter-Session Network Coding
49(16)
Scalar and vector linear network coding
50(1)
Fractional coding problem formulation
51(1)
Insufficiency of linear network coding
52(2)
Information theoretic approaches
54(4)
Multiple unicast networks
58(1)
Constructive approaches
58(5)
Pairwise XOR coding in wireline networks
59(1)
XOR Coding in wireless networks
60(3)
Notes and further reading
63(2)
Network Coding in Lossy Networks
65(21)
Random linear network coding
67(1)
Coding theorems
68(15)
Unicast connections
68(14)
Multicast connections
82(1)
Error exponents for Poisson traffic with i.i.d. losses
83(2)
Notes and further reading
85(1)
Subgraph Selection
86(44)
Flow-based approaches
87(27)
Intra-session coding
87(23)
Computation-constrained coding
110(1)
Inter-session coding
111(3)
Queue-length-based approaches
114(15)
Intra-session network coding for multiple multicast sessions
115(13)
Inter-session coding
128(1)
Notes and Further reading
129(1)
Security Against Adversarial Errors
130(27)
Notational conventions
130(1)
Error correction
131(16)
Error correction bounds for centralized network coding
131(11)
Distributed random network coding and polynomial-complexity error correction
142(5)
Detection of adversarial errors
147(4)
Model and problem formulation
148(2)
Detection probability
150(1)
Notes and further reading
151(1)
Appendix: Proof of results for adversarial error detection
152(5)
Bibliography 157(12)
Index 169
Tracey Ho is an Assistant Professor of Electrical Engineering and Computer Science at the California Institute of Technology. In 2004, she was awarded a Ph.D. in electrical engineering and computer science from MIT, and in 2005, was voted one of the 35 top technology innovators under the age of 35 by the Technology Review magazine. Desmond S. Lun is a Computational Biologist at the Broad Institute of MIT and Harvard and a Research Fellow in Genetics at Harvard Medical School. In 2006, he was awarded his Ph.D. in electrical engineering and computer science from MIT.