Zamir R. Lattice Coding for Signals and Networks. A Structured Coding Approach to Quantization, Modulation, and Multiuser Information Theory

Cambridge University Press, 2014. — 460 p.Digital communication and information theory talk about the same problem from very different aspects. Lattice codes provide a framework to tell their mutual story. They suggest a common view of source and channel coding, and new tools for the analysis of information network problems.
This book makes the language of quantization and modulation more accessible to the hard core information theorist. For him or her, lattices serve as a bridge from the high dimension of Shannon’s theory to that of digital communication techniques. At the same time, lattices provide a useful tool for the communication engineer, whose scope is usually limited to the low – sometimes even one or two – dimensions of practical modulation schemes (e.g., QAM or OFDM). She or he can see, through the lattice framework, how signals and noise interact as the dimension increases, for example, when modulation is combined with coding. Surprisingly for both disciplines, the generalization of the lattice framework to Gaussian networks is not only very natural, but in some cases is more powerful than the traditional techniques.
This book is beneficial to the Gaussian-oriented information theorist, who wishes to become familiar with network information theory from a constructive viewpoint (as opposed to the more abstract random-coding/random-binning approach). And it is a useful tool for the communication practitioner in the industry, who prefers a geometric and signal-processing oriented viewpoint of information theory in general, and multiuser problems in particular. The algebraic coding theorist can celebrate the variety of new applications for lattice codes found in the book. The control theorist, who wishes to add communication constraints into the system, will find the linear-additive model of dithered lattice quantization useful. Other readers, like those having a background in signal processing or computer networks, can find potential challenges in the relations to linear estimation and network coding.Lattices
Figures of merit
Dithering and estimation
Entropy-coded quantization
Infinite constellation for modulation
Asymptotic goodness
Nested lattices
Lattice shaping
Side-information problems
Modulo-lattice modulation
Gaussian networks
Error exponents