Network Information Theory

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.

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.

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.