Fundamentals of Convolutional Coding

Fundamentals of Convolutional Coding, Second Edition, regarded as a bible of convolutional coding brings you a clear and comprehensive discussion of the basic principles of this field. This edition has been expanded to reflect the developments in modern coding theory, including new chapters on low–density parity–check convolutional codes and turbo codes. Since these types of codes are now appearing in industry standards, application engineers and scientists will also find this book essential to obtaining a basic understanding of the theory behind these new techniques. Written by two leading authorities in coding and information theory, it is unmatched in the field for its accessible analysis of the structural properties of convolutional encoders. Other essentials covered include:        Viterbi, BCJR, BEAST, list, and sequential decoding        Low–density parity–check (LDPC) codes and iterative decoding        Turbo codes and iterative coding        An extensive set of practice problems The authors draw on their own research and more than 40 years of teaching experience to present the fundamentals needed to understand the codes used in a variety of applications today. The book can be used as a textbook for graduate–level electrical engineering students. Rolf Johannesson is Professor Emeritus of Information Theory at Lund University, Sweden, and a Fellow of the IEEE. He was awarded the honor of Professor, honoris causa, from the Institute for Information Transmission Problems, Russian Academy of Sciences, and elected member of the Royal Swedish Academy of Engineering Sciences. Dr. Johannesson´s research interests include information theory, coding theory, and cryptography. Kamil Sh. Zigangirov is Professor Emeritus of Telecommunication Theory at Lund University, Sweden, and a Fellow of the IEEE. He is widely published in the areas of information theory, coding theory, mathematical statistics, and detection theory. Dr. Zigangirov is the inventor of the stack algorithm for sequential decoding and the co–inventor of the LDPC convolutional codes.