This book differs from the many other textbooks on information and coding theory in that it provides a plentitude of examples, presented in the form of solved problems. Whereas other books may offer a more rigorous mathematical treatment, this book puts a focus on worked examples. Each chapter is divided into two sections: first a brief theoretical overview, and then a larger problem section. The latter is sometimes three or four times longer than the former.

After a very short introduction, the first two chapters deal with information sources and source encoding, that is, data compression; this important topic is often omitted because it is so very different from error-correction coding. A longer chapter is dedicated to information channels, discrete and continuous ones, with and without memory. Chapter 5 covers block codes, especially linear block codes. Chapter 6 presents a subclass of linear block codes called cyclic codes, best known for error detection as cyclic redundancy codes, for example, in mobile communication. Convolutional codes for channel coding are analyzed in chapter 7. The authors illustrate majority logic decoding, sequential decoding, and the Viterbi algorithm. Convolutional codes are used, for example, in satellite communication, but also for data storage on magnetic discs. Chapter 8 addresses trellis decoding of linear block codes and turbo decoding. The authors show how from a transformed trellis generator matrix the corresponding parity-check matrix can be obtained. The final chapter deals with low-density parity check (LDPC) codes, also called Gallager codes, in honor of Robert G. Gallager, who developed the concept in 1960. The Tanner interpretation of LDPC codes using bipartite graphs is also noteworthy.

Newer topics like polar codes, spatially coupled LDPC codes, fountain codes, and nonbinary LDPC are not mentioned in this book.

The otherwise excellent presentation of the material is clouded by the many grammatical errors that an attentive editor should have caught.