Basic Concepts in Information Theory and Coding is an outgrowth of a one- semester introductory course that has been taught at the University of Southern California since the mid-1960s. Lecture notes from that course have evolved in response to student reaction, new technological and theoretical develop- ments, and the insights of faculty members who have taught the course (in- cluding the three of us). In presenting this material, we have made it accessible to a broad audience by limiting prerequisites to basic calculus and the ele- mentary concepts of discrete probability theory. To keep the material suitable for a one-semester course, we have limited its scope to discrete information theory and a general discussion of coding theory without detailed treatment of algorithms for encoding and decoding for various specific code classes. Readers will find that this book offers an unusually thorough treatment of noiseless self-synchronizing codes, as well as the advantage of problem sections that have been honed by reactions and interactions of several gen- erations of bright students, while Agent 00111 provides a context for the discussion of abstract concepts.
Unifying information theory and digital communication through the language of lattice codes, this book provides a detailed overview for students, researchers and industry practitioners. It covers classical work by leading researchers in the field of lattice codes and complementary work on dithered quantization and infinite constellations, and then introduces the more recent results on 'algebraic binning' for side-information problems, and linear/lattice codes for networks. It shows how high dimensional lattice codes can close the gap to the optimal information theoretic solution, including the characterisation of error exponents. The solutions presented are based on lattice codes, and are therefore close to practical implementations, with many advanced setups and techniques, such as shaping, entropy-coding, side-information and multi-terminal systems. Moreover, some of the network setups shown demonstrate how lattice codes are potentially more efficient than traditional random-coding solutions, for instance when generalising the framework to Gaussian networks.
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