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Performance Analysis and Modeling of Digital Transmission Systems

  • Book
  • © 2004

Overview

Authors:
  1. William Turin

    1. AT&T Labs—Research, Florham Park, New Jersey, USA

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Table of contents (8 chapters)

  1. Front Matter

    Pages i-xxi
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  2. Introduction

    • William Turin
    Pages 1-7

  3. Error Source Models

    • William Turin
    Pages 9-59

  4. Matrix Probabilities

    • William Turin
    Pages 61-99

  5. Model Parameter Estimation

    • William Turin
    Pages 101-174

  6. Performance of Forward Error-Correction Systems

    • William Turin
    Pages 175-243

  7. Performance Analysis of Communication Protocols

    • William Turin
    Pages 245-277

  8. Continuous Time HMM

    • William Turin
    Pages 279-293

  9. Continuous State HMM

    • William Turin
    Pages 295-340

  10. Back Matter

    Pages 341-441
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Keywords

About this book

This book is an expanded third edition of the book Performance Analysis of Digital Transmission Systems, originally published in 1990. Second edition of the book titled Digital Transmission Systems: Performance Analysis and Modeling was published in 1998. The book is intended for those who design communication systems and networks. A computer network designer is interested in selecting communication channels, error protection schemes, and link control protocols. To do this efficiently, one needs a mathematical model that accurately predicts system behavior. Two basic problems arise in mathematical modeling: the problem of identifying a system and the problem of applying a model to the system analysis. System identification consists of selecting a class of mathematical objects to describe fundamental properties of the system behavior. We use a specific class of hidden Markov models (HMMs) to model communication systems. This model was introduced by C. E. Shannon more than 50 years ago as a Noisy Discrete Channel with a finite number of states. The model is described by a finite number of matrices whose elements are estimated on the basis of experimental data. We develop several methods of model identification and show their relationship to other methods of data analysis, such as spectral methods, autoregressive moving average CARMA) approximations, and rational transfer function approximations.

Authors and Affiliations

  • AT&T Labs—Research, Florham Park, New Jersey, USA

    William Turin

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