Network Information Theory
1st Edition
Published on 8. December 2011
Book
Hardback
714 pages
978-1-107-00873-1 (ISBN)
Description
This comprehensive treatment of network information theory and its applications provides the first unified coverage of both classical and recent results. With an approach that balances the introduction of new models and new coding techniques, readers are guided through Shannon's point-to-point information theory, single-hop networks, multihop networks, and extensions to distributed computing, secrecy, wireless communication, and networking. Elementary mathematical tools and techniques are used throughout, requiring only basic knowledge of probability, whilst unified proofs of coding theorems are based on a few simple lemmas, making the text accessible to newcomers. Key topics covered include successive cancellation and superposition coding, MIMO wireless communication, network coding, and cooperative relaying. Also covered are feedback and interactive communication, capacity approximations and scaling laws, and asynchronous and random access channels. This book is ideal for use in the classroom, for self-study, and as a reference for researchers and engineers in industry and academia.
Reviews / Votes
'El Gamal and Kim have produced the most extensive and inclusive text on all aspects of information theory to date. They have collected and organized the fruits of six decades of research demonstrating how Shannon's original seminal theory has been enlarged to solve a multitude of important problems mostly encountered in multiple link communication networks. The authors stress the significance of these results for timely applications such as multi-hop wireless networks. Beyond its value as a textbook for an advanced course on information theory, the attention given to motivating applications makes it useful for practising communication engineers as well.' Andrew Viterbi, University of Southern California and co-founder of Qualcomm, Inc. 'El Gamal and Kim have written a masterpiece. It brings organization and clarity to a large and previously chaotic field. The mathematics is done cleanly and carefully, and the intuition behind the results is brought out with clarity.' Robert G. Gallager, Massachusetts Institute of Technology 'On offer in this text is a superb unified pedagogical treatment including results that heretofore were only available in their original, often arcane, sources. While key regions in network information theory remain terra incognita, future discoveries are bound to owe a debt of gratitude to El Gamal and Kim's comprehensive magnum opus.' Sergio Verdu, Princeton University 'I ... recommend the book to all students, researchers, and practitioners.' Klaus Galensa, Computing Reviews 'The presentation is based on basic knowledge of probability and elementary mathematical tools and techniques, making the book accessible to graduate students and for self-study. But the width of covering (this is the first unified treatment of both classical and recent results) makes the book valuable also to researchers and practitioners.' Jaak Henno, Zentralblatt MATHMore details
Language
English
Place of publication
Cambridge
United Kingdom
Target group
Professional and scholarly
Illustrations
Worked examples or Exercises; 12 Tables, black and white; 231 Line drawings, black and white
Dimensions
Height: 250 mm
Width: 175 mm
Thickness: 42 mm
Weight
1390 gr
ISBN-13
978-1-107-00873-1 (9781107008731)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Abbas El Gamal | Young-Han Kim
Network Information Theory
Book
03/2018
Cambridge University Press
€91.50
Shipment within 15-20 days
Abbas El Gamal | Young-Han Kim
Network Information Theory
E-Book
03/2012
1st Edition
Cambridge University Press
€52.99
Available for download
Abbas El Gamal
Network Information Theory
E-Book
12/2011
Cambridge University Press
€44.49
Available for download
Persons
Abbas El Gamal is the Hitachi America Chaired Professor in the School of Engineering and the Chair of the Department of Electrical Engineering at Stanford University, California. In the field of network information theory, he is best known for his seminal contributions to the relay, broadcast, and interference channels; multiple description coding; coding for noisy networks; and energy-efficient packet scheduling and throughput-delay tradeoffs in wireless networks. He is a Fellow of the Institute of Electrical and Electronics Engineers and the winner of the 2012 Claude E. Shannon Award, the highest honor in the field of information theory. Young-Han Kim is an Associate Professor in the Department of Electrical and Computer Engineering at the University of California, San Diego. His research focuses on information theory and statistical signal processing. He is a recipient of the 2012 Institute of Electrical and Electronics Engineers Information Theory Paper Award and the 2008 National Science Foundation Faculty Early Career Development (CAREER) Award.
Content
1. Introduction; Part I. Preliminaries: 2. Information measures and typicality; 3. Point-to-point information theory; Part II. Single-Hop Networks: 4. Multiple access channels; 5. Degraded broadcast channels; 6. Interference channels; 7. Channels with state; 8. General broadcast channels; 9. Gaussian vector channels; 10. Distributed lossless compression; 11. Lossy compression with side information; 12. Distributed lossy compression; 13. Multiple description coding; 14. Joint source-channel coding; Part III. Multihop Networks: 15. Graphical networks; 16. Relay channels; 17. Interactive channel coding; 18. Discrete memoryless networks; 19. Gaussian networks; 20. Compression over graphical networks; Part IV. Extensions: 21. Communication for computing; 22. Information theoretic secrecy; 23. Wireless fading channels; 24. Networking and information theory; Appendices: A. Convex sets and functions; B. Probability and estimation; C. Cardinality bounding techniques; D. Fourier-Motzkin elimination; E. Convex optimization.