Detection Algorithms for Wireless CommunicationsWith Applications to Wired and Storage Systems
Wireless channels are becoming more and more important, with the future development of wireless ad-hoc networks and the integration of mobile and satellite communications. To this end, algorithmic detection aspects (involved in the physical layer) will become fundamental in the design of a communication system. This book proposes a unified approach to detection for stochastic channels, with particular attention to wireless channels. The core idea is to show that the three main criteria of sequence detection, symbol detection and graph-based detection, can all be described within a general framework. This implies that a detection algorithm based on one criterion can be extended to the other criteria in a systematic manner. Presents a detailed analysis of statistical signal detection for digital signals transmitted over wireless communications Provides a unifying framework for different signal detection algorithms, such as sequence detection, symbol detection and graph-based detection, important for the design of modern digital receivers operating over mobile channels Features the hot topic of graph-based detection Detection Algorithms for Wireless Communications represents a novel contribution with respect to the current literature, with a unique focus on detection algorithms, as such it will prove invaluable to researchers working in academia and industry and in the field of wireless communications, as well as postgraduate students attending advanced courses on mobile communications.
Preface. Acknowledgements. List of Figures. List of Tables. 1. Wireless Communication Systems. 1.1 Introduction. 1.2 Overview of Wireless Communication Systems. 1.3 Wireless Channel Models. 1.4 Demodulation, Detection, and Parameter Estimation. 1.5 Information Theoretic Limits. 1.6 Coding and Modulation. 1.7 Approaching Shannon Limits: Turbo Codes and Low Density Parity Check Codes. 1.8 Space-Time Coding. 1.9 Summary. 1.10 Problems. 2. A General Approach to Statistical Detection for Channels with Memory. 2.1 Introduction. 2.2 Statistical Detection Theory. 2.3 Transmission Systems with Memory. 2.4 Overview of Detection Algorithms for Stochastic Channels. 2.5 Summary. 2.6 Problems. 3. Sequence Detection: Algorithms and Applications. 3.1 Introduction. 3.2 MAP Sequence Detection Principle. 3.3 Viterbi Algorithm. 3.4 Soft-output Viterbi Algorithm. 3.5 Finite-Memory Sequence Detection. 3.6 Estimation-Detection Decomposition. 3.7 Data-Aided Parameter Estimation. 3.8 Joint Detection and Estimation. 3.9 Per-Survivor Processing. 3.9.1 Phase-Uncertain Channel. 3.10 Complexity Reduction Techniques for VA-based Detection Algorithms. 3.11 Applications to Wireless Communications. 3.12 Summary. 3.13 Problems. 4. Symbol Detection: Algorithms and Applications. 4.1 Introduction. 4.2 MAP Symbol Detection Principle. 4.3 Forward-Backward Algorithm. 4.4 Iterative Decoding and Detection. 4.5 Extrinsic Information in Iterative Decoding: a Unified View. 4.6 Finite-Memory Symbol Detection. 4.7 An Alternative Approach to Finite-Memory Symbol Detection. 4.8 State Reduction Techniques for Forward-Backward Algorithms. 4.9 Applications to Wireless Communications. 4.10 Summary. 4.11 Problems. 5. Graph-Based Detection: Algorithms and Applications. 5.1 Introduction. 5.2 Factor Graphs and the Sum-Product Algorithm. 5.3 Finite-Memory Graph-Based Detection. 5.4 Complexity Reduction for Graph-Based Detection Algorithms. 5.5 Strictly Finite Memory: Inter-Symbol Interference Channels. 5.6 Applications to Wireless Communications 5.7 An Alternative Approach to Graph-Based Detection in the Presence of Strong Phase Noise. 5.8 Summary. 5.9 Problems. Appendix: Discretization by Sampling. A.1 Introduction. A.2 Continuous-Time Signal Model. A.3 Discrete-Time Signal Model. References. List of Acronyms. Index.
Wireless communications will play a major role in future communication systems. In fact, the need of wireless access to the Internet will become increasingly important, and novel network communication paradigms, such as ad-hoc wireless networks or integrated mobile/satellite systems, will be developed in the near future. Detection algorithms will play an important role in the design of efficient wireless communication systems as it will be mandatory to adhere to specific constraints in terms of power consumption and detection speed. Provides a unified approach to statistical detection for stochastic channels, with particular emphasis on wireless communications Shows how algorithms for trellis-based sequence detection can be systematically extended to trellis-based and graph-based symbol detection algorithms and vice-versa Contains numerous examples of applications with an extended set of numerical results relative to the algorithms’ performance Describes per-survivor processing, a key concept used to implement adaptive detection techniques Presents a detailed description of graph-based detection Features problems at the end of each chapter * Includes a companion website featuring a solutions manual, electronic versions of the figures and a sample chapter * By featuring detection algorithms which can be applied to wireless communications, as well as wired and storage systems such as those relative to transmissions over inter-symbol interference channels, this book will have far reaching appeal. Researchers and practitioners working in wireless and storage system design, both in academia and in industry, will all find it extremely useful.
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