William Turin

Performance Analysis and Modeling of Digital Transmission Systems

• Gebundenes Buch

Produktbeschreibung

This book describes mathematical methods for analyzing digital transmission system performance. In contrast with publications that use an idealistic model of channels with independent errors, this book shows how to evaluate performance characteristics of information transmission systems in real communication channels with bursts of noise. The book shows how to apply hidden Markov Models (HMMs) to model and analyze performance of communications systems (including error correction codes and communication protocols) in channels with memory. This edition includes a new chapter describing the theory and applications of continuous state HMMs. Methods developed in the book have broad applications in queuing theory, speech and image recognition, signature verification, control theory, artificial intelligence, biology, fraud detection, and finance. The attached CD-ROM contains numerous MATLAB® programs implementing the theory described in the book. With a rich assortment of chapter-ending problems and illustrations, the book and CD-ROM are perfect tools for the study of HMM methods or for use as a classroom text.

Produktdetails

• Information Technology: Transmission, Processing, and Storage
• Verlag: Springer Netherlands
• Seitenzahl: 441
• Englisch
• Abmessung: 254mm
• Gewicht: 1109g
• ISBN-13: 9780306481918
• ISBN-10: 030648191X
• Artikelnr.: 14055590

Inhaltsangabe

Preface.- Notation.- Introduction.- Error Source Models.- Matrix Probabilities.- Model Parameter Estimation.- Performance of Forward Error-Correction Systems.- Performance Analysis of Communication Protocols.- Continuous Time HMM.- Continous State HMM.- Appendix 1.- Appendix 2.- Appendix 3.- Appendix 4.- Appendix 5.- Appendix 6.- Appendix 7.-Index

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Preface.- Notation.- 1. Error Source Models.- 1.1 Description of Error Sources by Hidden Markov Models.- 1.2 Binary Symmetric Stationary Channel.- 1.3 Error Source Description by Matrix Processes.- 1.4 Error Source Description by Semi-Markov Processes.- 1.5 Some Particular Error Source Models.- 1.6 Conclusion.- References.- 2. Matrix Probabilities.- 2.1 Matrix Probabilities and Their Properties.- 2.2 Matrix Transforms.- 2.3 Matrix Distributions.- 2.4 Markov Functions.- 2.5 Monte Carlo Method.- 2.6 Computing Scalar Probabilities.- 2.7 Conclusion.- References.- 3. Model Parameter Estimation.- 3.1 The Em Algorithm.- 3.2 Baum-Welch Algorithm.- 3.3 Markov Renewal Process.- 3.4 Matrix-Geometric Distribution Parameter Estimation.- 3.5 Matrix Process Parameter Estimation.- 3.6 Hmm Parameter Estimation.- 3.7 Monte Carlo Method of Model Building.- 3.8 Error Source Model in Several Channels.- 3.9 Conclusion.- References.- 4. Performance of Forward Error-Correction Systems.- 4.1 Basic Characteristics of One-Way Systems.- 4.2 Elements of Error-Correcting Coding.- 4.3 Maximum A Posteriori Decoding.- 4.4 Block Code Performance Characterization.- 4.5 Convolutional Code Performance.- 4.6 Computer Simulation.- 4.7 Zero-Redundancy Codes.- 4.8 Conclusion.- References.- 5. Performance Analysis of Communication Protocol.- 5.1 Basic Characteristics of Two-Way Systems.- 5.2 Return-Channel Messages.- 5.3 Synchronization.- 5.4 Arq Performance Characteristics.- 5.5 Delay-Constained Systems.- 5.6 Conclusion.- References.- 6. Continuous Time Hmm.- 6.1 Continuous and Discrete Time Hmm.- 6.2 Fitting Continuous Time Hmm.- 6.3 Conclusion.- References.- 7. Continuous State Hmm.- 7.1 Continuous and Discrete State Hmm.- 7.2 Operator Probability.- 7.3 Filtering, Prediction, and Smoothing.- 7.4 Linear Systems.- 7.5 Autoregressive Moving Average Processes.- 7.6 Parameter Estimation.- 7.7 Arma Channel Modeling.- 7.8 Conclusion.- References.- Appendix 1.- 1.1 Matrix Processes.- 1.2 Markov Lumpable Chains.- 1.3 Semi-Markov Lumpable Chains.- References.- Appendix 2.- 2.1 Asymptotic Expansion of Matrix Probabilities.- 2.2 Chernoff Bounds.- 2.3 Block Graphs.- References.- Appendix 3.- 3.1 Statistical Inference.- 3.2 Markov Chain Model Building.- 3.3 Semi-Markov Process Hypothesis Testing.- 3.4 Matrix Process Parameter Estimation.- References.- Appendix 4.- 4.1 Sums With Binomial Coefficients.- 4.2 Maximum-Distance-Separable Code Weight Generating Function.- 4.3 Union Bounds on Viterbi Algorithm Performance.- References.- Appendix 5.- 5.1 Matrices.- References.- Appendix 6.- 6.1 Markov Chains and Graphs.- References.- Appendix 7.- 7.1 Markov Processes.- 7.2 Gauss-Markov Processes.- References.