Automatic modulation classification : principles, algorithms, and applications

Automatic modulation classification : principles, algorithms, and applications / Zhechen Zhu and Asoke K. Nand.

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...

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Bibliographic Details
Main Author: Zhu, Zhechen
Other Authors: Nandi, Asoke Kumar
Format: eBook
Language:English
Published: Hoboken : John Wiley & Sons Inc., 2015.
Subjects:
Modulation (Electronics)
TECHNOLOGY & ENGINEERING > Mechanical.
Online Access:Click for online access
Table of Contents:
  • Title Page; Copyright Page; Contents; About the Authors; Preface; List of Abbreviations; List of Symbols; Chapter 1 Introduction; 1.1 Background; 1.2 Applications of AMC; 1.2.1 Military Applications; 1.2.2 Civilian Applications; 1.3 Field Overview and Book Scope; 1.4 Modulation and Communication System Basics; 1.4.1 Analogue Systems and Modulations; 1.4.2 Digital Systems and Modulations; 1.4.3 Received Signal with Channel Effects; 1.5 Conclusion; References; Chapter 2 Signal Models for Modulation Classification; 2.1 Introduction; 2.2 Signal Model inAWGNChannel.
  • 2.2.1 Signal Distribution of I-Q Segments2.2.2 Signal Distribution of Signal Phase; 2.2.3 Signal Distribution of Signal Magnitude; 2.3 Signal Models in Fading Channel; 2.4 Signal Models in Non-Gaussian Channel; 2.4.1 Middleton ́s Class A Model; 2.4.2 Symmetric Alpha Stable Model; 2.4.3 Gaussian Mixture Model; 2.5 Conclusion; References; Chapter 3 Likelihood-based Classifiers; 3.1 Introduction; 3.2 Maximum Likelihood Classifiers; 3.2.1 Likelihood Function inAWGNChannels; 3.2.2 Likelihood Function in Fading Channels; 3.2.3 Likelihood Function in Non-Gaussian Noise Channels.
  • 3.2.4 Maximum Likelihood Classification Decision Making3.3 Likelihood Ratio Test for Unknown Channel Parameters; 3.3.1 Average Likelihood Ratio Test; 3.3.2 Generalized Likelihood Ratio Test; 3.3.3 Hybrid Likelihood Ratio Test; 3.4 Complexity Reduction; 3.4.1 Discrete Likelihood Ratio Test and Lookup Table; 3.4.2 Minimum Distance Likelihood Function; 3.4.3 Non-Parametric Likelihood Function; 3.5 Conclusion; References; Chapter 4 Distribution Test-based Classifier; 4.1 Introduction; 4.2 Kolmogorov-Smirnov Test Classifier; 4.2.1 The KS Test for Goodness of Fit.
  • 4.2.2 One-sample KS Test Classifier4.2.3 Two-sample KS Test Classifier; 4.2.4 Phase Difference Classifier; 4.3 Cramer-Von Mises Test Classifier; 4.4 Anderson-Darling Test Classifier; 4.5 Optimized Distribution Sampling Test Classifier; 4.5.1 Sampling Location Optimization; 4.5.2 Distribution Sampling; 4.5.3 Classification Decision Metrics; 4.5.4 Modulation Classification Decision Making; 4.6 Conclusion; References; Chapter 5 Modulation Classification Features; 5.1 Introduction; 5.2 Signal Spectral-based Features; 5.2.1 Signal Spectral-based Features; 5.2.2 Spectral-based Features Specialities.
  • 5.2.3 Spectral-based Features Decision Making5.2.4 Decision Threshold Optimization; 5.3 Wavelet Transform-based Features; 5.4 High-order Statistics-based Features; 5.4.1 High-order Moment-based Features; 5.4.2 High-order Cumulant-based Features; 5.5 Cyclostationary Analysis-based Features; 5.6 Conclusion; References; Chapter 6 Machine Learning for Modulation Classification; 6.1 Introduction; 6.2 K-Nearest Neighbour Classifier; 6.2.1 Reference Feature Space; 6.2.2 Distance Definition; 6.2.3 K-Nearest Neighbour Decision; 6.3 Support Vector Machine Classifier.

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