Detection Algorithms for Wireless Communications : With Applications to Wired and Storage Systems
Gianluigi Ferrari, Giulio Colavolpe, Riccardo Raheli
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.
Table of Contents:
List of Figures.
List of Tables.
1. Wireless Communication Systems.
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.
2. A General Approach to Statistical Detection for Channels with Memory.
2.2 Statistical Detection Theory.
2.3 Transmission Systems with Memory.
2.4 Overview of Detection Algorithms for Stochastic Channels.
3. Sequence Detection: Algorithms and Applications.
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.
4. Symbol Detection: Algorithms and Applications.
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.
5. Graph-Based Detection: Algorithms and Applications.
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.
Appendix: Discretization by Sampling.
A.2 Continuous-Time Signal Model.
A.3 Discrete-Time Signal Model.
List of Acronyms.