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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 monograph 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
Sparse Regression Codes an essential resource in understanding
these new techniques that will have a significant impact on such
systems in the years to come.
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