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Automatic Modulation Classification: Principles, Algorithms and Applications

Book Description

Automatic Modulation Classification (AMC) has been a key technology in many military, security, and civilian telecommunication applications for decades. In military and security applications, modulation often serves as another level of encryption; in modern civilian applications, multiple modulation types can be employed by a signal transmitter to control the data rate and link reliability.

This book offers comprehensive documentation of AMC models, algorithms and implementations for successful modulation recognition. It provides an invaluable theoretical and numerical comparison of AMC algorithms, as well as guidance on state-of-the-art classification designs with specific military and civilian applications in mind.

Key Features:

  • Provides an important collection of AMC algorithms in five major categories, from likelihood-based classifiers and distribution-test-based classifiers to feature-based classifiers, machine learning assisted classifiers and blind modulation classifiers

  • Lists detailed implementation for each algorithm based on a unified theoretical background and a comprehensive theoretical and numerical performance comparison

  • Gives clear guidance for the design of specific automatic modulation classifiers for different practical applications in both civilian and military communication systems

  • Includes a MATLAB toolbox on a companion website offering the implementation of a selection of methods discussed in the book

  • Table of Contents

    1. Cover
    2. Title page
    3. Copyright page
    4. Dedication page
    5. About the Authors
    6. Preface
    7. List of Abbreviations
    8. List of Symbols
    9. 1 Introduction
      1. 1.1 Background
      2. 1.2 Applications of AMC
      3. 1.3 Field Overview and Book Scope
      4. 1.4 Modulation and Communication System Basics
      5. 1.5 Conclusion
      6. References
    10. 2 Signal Models for Modulation Classification
      1. 2.1 Introduction
      2. 2.2 Signal Model in AWGN Channel
      3. 2.3 Signal Models in Fading Channel
      4. 2.4 Signal Models in Non-Gaussian Channel
      5. 2.5 Conclusion
      6. References
    11. 3 Likelihood-based Classifiers
      1. 3.1 Introduction
      2. 3.2 Maximum Likelihood Classifiers
      3. 3.3 Likelihood Ratio Test for Unknown Channel Parameters
      4. 3.4 Complexity Reduction
      5. 3.5 Conclusion
      6. References
    12. 4 Distribution Test-based Classifier
      1. 4.1 Introduction
      2. 4.2 Kolmogorov–Smirnov Test Classifier
      3. 4.3 Cramer–Von Mises Test Classifier
      4. 4.4 Anderson–Darling Test Classifier
      5. 4.5 Optimized Distribution Sampling Test Classifier
      6. 4.6 Conclusion
      7. References
    13. 5 Modulation Classification Features
      1. 5.1 Introduction
      2. 5.2 Signal Spectral-based Features
      3. 5.3 Wavelet Transform-based Features
      4. 5.4 High-order Statistics-based Features
      5. 5.5 Cyclostationary Analysis-based Features
      6. 5.6 Conclusion
      7. References
    14. 6 Machine Learning for Modulation Classification
      1. 6.1 Introduction
      2. 6.2 K-Nearest Neighbour Classifier
      3. 6.3 Support Vector Machine Classifier
      4. 6.4 Logistic Regression for Feature Combination
      5. 6.5 Artificial Neural Network for Feature Combination
      6. 6.6 Genetic Algorithm for Feature Selection
      7. 6.7 Genetic Programming for Feature Selection and Combination
      8. 6.8 Conclusion
      9. References
    15. 7 Blind Modulation Classification
      1. 7.1 Introduction
      2. 7.2 Expectation Maximization with Likelihood-based Classifier
      3. 7.3 Minimum Distance Centroid Estimation and Non-parametric Likelihood Classifier
      4. 7.4 Conclusion
      5. References
    16. 8 Comparison of Modulation Classifiers
      1. 8.1 Introduction
      2. 8.2 System Requirements and Applicable Modulations
      3. 8.3 Classification Accuracy with Additive Noise
      4. 8.4 Classification Accuracy with Limited Signal Length
      5. 8.5 Classification Robustness against Phase Offset
      6. 8.6 Classification Robustness against Frequency Offset
      7. 8.7 Computational Complexity
      8. 8.8 Conclusion
      9. References
    17. 9 Modulation Classification for Civilian Applications
      1. 9.1 Introduction
      2. 9.2 Modulation Classification for High-order Modulations
      3. 9.3 Modulation Classification for Link-adaptation Systems
      4. 9.4 Modulation Classification for MIMO Systems
      5. 9.5 Conclusion
      6. References
    18. 10 Modulation Classifier Design for Military Applications
      1. 10.1 Introduction
      2. 10.2 Modulation Classifier with Unknown Modulation Pool
      3. 10.3 Modulation Classifier against Low Probability of Detection
      4. 10.4 Conclusion
      5. References
    19. Index
    20. End User License Agreement