Sparse Regression Codes

ISBN-13: 9781680835809 / Angielski / Miękka / 2019 / 218 str.

Ramji Venkataramanan;Sekhar Tatikonda;Andrew Barron

Opis

Developing increasingly computationally-efficient codes for communication and compression has been the major goal of information and coding theory. There have been significant advances towards this goal in the last couple of decades, with the emergence of turbo codes, sparse-graph codes, and polar codes. The world has seen faster network speeds and greater storage due to many of these developments A new class of codes, Sparse Regression Codes (SPARCs), are a promising class of codes for achieving the Shannon limits of a communication channel. This book presents a unified and comprehensive overview of sparse regression codes, covering theory, algorithms, and practical implementation aspects. Written by the world's recognized experts in the field it describes the use of SPARCs for efficient communication over AWGN channels, for lossy compression and multi-terminal communication. Researchers and students in modern communication and network systems will find this book an essential resource in understanding these new techniques which will have a significant impact on such systems in the years to come.

Spis treści

• 1. Introduction
• Part I. AWGN Channel Coding with SPARCs
• 2. Optimal Decoding
• 3. Computationally Efficient Decoding
• 4. Finite Length Decoding Performance
• 5. Spatially Coupled SPARCs
• Part II. Lossy Compression with SPARCs
• 6. Optimal Encoding
• 7. Computationally Efficient Encoding
• Part III. Multiuser Communication and Compression with SPARCs
• 8. Broadcast and Multiple-access Channels
• 9. Communication and Compression with Side Information
• 10. Open Problems and Further Directions
• Acknowledgements
• References