Performance Analysis of Linear Codes under Maximum-Likelihood Decoding: A Tutorial focuses on the performance evaluation of linear codes under optimal maximum-likelihood (ML) decoding. Though the ML decoding algorithm is prohibitively complex for most practical codes, their performance analysis under ML decoding allows to predict their performance without resorting to computer simulations. Performance Analysis of Linear Codes under Maximum-Likelihood Decoding: A Tutorial is a comprehensive introduction to this important topic for students, practitioners and researchers working in communications and information theory.

Most coding schemes used in today's communication systems are designed for memoryless channels. These codes break down when they are transmitted over channels with memory, which is in fact what real-world channels look like since errors often occur in bursts. Therefore, these systems employ interleaving to spread the errors so that the channel looks more or less memoryless (for the decoder) at the cost of added delay and complexity. In addition, they fail to exploit the memory of the channel which increases the capacity for a wide class of channels. On the other hand, most channels with memory do not have simple and mathematically tractable models, making the design of suitable channel codes more challenging and possibly not practical. Recently, a new model has been proposed known as the queue-based channel (QBC) which is simple enough for mathematical analysis and complex enough for modeling wireless fading channels. In this work, we examine the performance of linear block codes when transmitted over this channel. We break down our focus into two parts. First, we investigate the maximum likelihood decoding of binary linear block codes over the QBC. Since it is well known that for binary symmetric memoryless channels, maximum likelihood decoding reduces to minimum Hamming distance decoding, our objective here is to explore whether there exists a similar relation between these two decoding schemes when the channel does have memory. We give a partial answer for the case of perfect and quasi perfect codes. Next, we study Reed-Solomon (RS) codes and analyze their performance when transmitted over the QBC under the assumption of bounded distance decoding. In particular, we examine the two interleaving strategies encountered when dealing with non-binary codes over a binary input channel; namely, symbol interleaving and bit interleaving. We compare these two interleaving schemes analytically and show that symbol interleaving always outperforms bit interleaving. Non-interleaved Reed-Solomon codes are also covered. We derive some useful expressions pertaining to the calculation of the probability of codeword error. The performance of non-interleaved RS codes are compared to that of interleaved ones for the simplest scenario of the QBC which is the additive (first-order) Markov noise channel with non-negative noise correlation.

Having trouble deciding which coding scheme to employ, how to design a new scheme, or how to improve an existing system? This summary of the state-of-the-art in iterative coding makes this decision more straightforward. With emphasis on the underlying theory, techniques to analyse and design practical iterative coding systems are presented. Using Gallager's original ensemble of LDPC codes, the basic concepts are extended for several general codes, including the practically important class of turbo codes. The simplicity of the binary erasure channel is exploited to develop analytical techniques and intuition, which are then applied to general channel models. A chapter on factor graphs helps to unify the important topics of information theory, coding and communication theory. Covering the most recent advances, this text is ideal for graduate students in electrical engineering and computer science, and practitioners. Additional resources, including instructor's solutions and figures, available online: www.cambridge.org/9780521852296.

5G Physical Layer: Principles, Models and Technology Components explains fundamental physical layer design principles, models and components for the 5G new radio access technology – 5G New Radio (NR). The physical layer models include radio wave propagation and hardware impairments for the full range of frequencies considered for the 5G NR (up to 100 GHz). The physical layer technologies include flexible multi-carrier waveforms, advanced multi-antenna solutions, and channel coding schemes for a wide range of services, deployments, and frequencies envisioned for 5G and beyond. A MATLAB-based link level simulator is included to explore various design options. 5G Physical Layer is very suitable for wireless system designers and researchers: basic understanding of communication theory and signal processing is assumed, but familiarity with 4G and 5G standards is not required. With this book the reader will learn: The fundamentals of the 5G NR physical layer (waveform, modulation, numerology, channel codes, and multi-antenna schemes). Why certain PHY technologies have been adopted for the 5G NR. The fundamental physical limitations imposed by radio wave propagation and hardware impairments. How the fundamental 5G NR physical layer functionalities (e.g., parameters/methods/schemes) should be realized. The content includes: A global view of 5G development – concept, standardization, spectrum allocation, use cases and requirements, trials, and future commercial deployments. The fundamentals behind the 5G NR physical layer specification in 3GPP. Radio wave propagation and channel modeling for 5G and beyond. Modeling of hardware impairments for future base stations and devices. Flexible multi-carrier waveforms, multi-antenna solutions, and channel coding schemes for 5G and beyond. A simulator including hardware impairments, radio propagation, and various waveforms. Ali Zaidi is a strategic product manager at Ericsson, Sweden. Fredrik Athley is a senior researcher at Ericsson, Sweden. Jonas Medbo and Ulf Gustavsson are senior specialists at Ericsson, Sweden. Xiaoming Chen is a professor at Xi’an Jiaotong University, China. Giuseppe Durisi is a professor at Chalmers University of Technology, Sweden, and a guest researcher at Ericsson, Sweden.