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Informationen zum Autor 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. Klappentext The first complete and unified coverage of both classical and recent results, including numerous worked examples and over 250 exercises. Zusammenfassung Providing a coherent and accessible presentation of the field! this is the first complete and unified coverage of both classical and recent results. Featuring a wealth of illustrations! worked examples! bibliographic notes and over 250 end-of-chapter problems! this book is ideal for students! researchers and practitioners alike. Inhaltsverzeichnis 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....
