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Automatic Modulation Classification
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Automatic Modulation Classification
by Asoke K. Nandi, Zhechen Zhu
Automatic Modulation Classification: Principles, Algorithms and Applications
Cover
Title page
Copyright page
Dedication page
About the Authors
Preface
List of Abbreviations
List of Symbols
1 Introduction
1.1 Background
1.2 Applications of AMC
1.3 Field Overview and Book Scope
1.4 Modulation and Communication System Basics
1.5 Conclusion
References
2 Signal Models for Modulation Classification
2.1 Introduction
2.2 Signal Model in AWGN Channel
2.3 Signal Models in Fading Channel
2.4 Signal Models in Non-Gaussian Channel
2.5 Conclusion
References
3 Likelihood-based Classifiers
3.1 Introduction
3.2 Maximum Likelihood Classifiers
3.3 Likelihood Ratio Test for Unknown Channel Parameters
3.4 Complexity Reduction
3.5 Conclusion
References
4 Distribution Test-based Classifier
4.1 Introduction
4.2 Kolmogorov–Smirnov Test Classifier
4.3 Cramer–Von Mises Test Classifier
4.4 Anderson–Darling Test Classifier
4.5 Optimized Distribution Sampling Test Classifier
4.6 Conclusion
References
5 Modulation Classification Features
5.1 Introduction
5.2 Signal Spectral-based Features
5.3 Wavelet Transform-based Features
5.4 High-order Statistics-based Features
5.5 Cyclostationary Analysis-based Features
5.6 Conclusion
References
6 Machine Learning for Modulation Classification
6.1 Introduction
6.2 K-Nearest Neighbour Classifier
6.3 Support Vector Machine Classifier
6.4 Logistic Regression for Feature Combination
6.5 Artificial Neural Network for Feature Combination
6.6 Genetic Algorithm for Feature Selection
6.7 Genetic Programming for Feature Selection and Combination
6.8 Conclusion
References
7 Blind Modulation Classification
7.1 Introduction
7.2 Expectation Maximization with Likelihood-based Classifier
7.3 Minimum Distance Centroid Estimation and Non-parametric Likelihood Classifier
7.4 Conclusion
References
8 Comparison of Modulation Classifiers
8.1 Introduction
8.2 System Requirements and Applicable Modulations
8.3 Classification Accuracy with Additive Noise
8.4 Classification Accuracy with Limited Signal Length
8.5 Classification Robustness against Phase Offset
8.6 Classification Robustness against Frequency Offset
8.7 Computational Complexity
8.8 Conclusion
References
9 Modulation Classification for Civilian Applications
9.1 Introduction
9.2 Modulation Classification for High-order Modulations
9.3 Modulation Classification for Link-adaptation Systems
9.4 Modulation Classification for MIMO Systems
9.5 Conclusion
References
10 Modulation Classifier Design for Military Applications
10.1 Introduction
10.2 Modulation Classifier with Unknown Modulation Pool
10.3 Modulation Classifier against Low Probability of Detection
10.4 Conclusion
References
Index
End User License Agreement
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CONTENTS
Cover
Title page
Copyright page
Dedication page
About the Authors
Preface
List of Abbreviations
List of Symbols
1 Introduction
1.1 Background
1.2 Applications of AMC
1.3 Field Overview and Book Scope
1.4 Modulation and Communication System Basics
1.5 Conclusion
References
2 Signal Models for Modulation Classification
2.1 Introduction
2.2 Signal Model in AWGN Channel
2.3 Signal Models in Fading Channel
2.4 Signal Models in Non-Gaussian Channel
2.5 Conclusion
References
3 Likelihood-based Classifiers
3.1 Introduction
3.2 Maximum Likelihood Classifiers
3.3 Likelihood Ratio Test for Unknown Channel Parameters
3.4 Complexity Reduction
3.5 Conclusion
References
4 Distribution Test-based Classifier
4.1 Introduction
4.2 Kolmogorov–Smirnov Test Classifier
4.3 Cramer–Von Mises Test Classifier
4.4 Anderson–Darling Test Classifier
4.5 Optimized Distribution Sampling Test Classifier
4.6 Conclusion
References
5 Modulation Classification Features
5.1 Introduction
5.2 Signal Spectral-based Features
5.3 Wavelet Transform-based Features
5.4 High-order Statistics-based Features
5.5 Cyclostationary Analysis-based Features
5.6 Conclusion
References
6 Machine Learning for Modulation Classification
6.1 Introduction
6.2 K-Nearest Neighbour Classifier
6.3 Support Vector Machine Classifier
6.4 Logistic Regression for Feature Combination
6.5 Artificial Neural Network for Feature Combination
6.6 Genetic Algorithm for Feature Selection
6.7 Genetic Programming for Feature Selection and Combination
6.8 Conclusion
References
7 Blind Modulation Classification
7.1 Introduction
7.2 Expectation Maximization with Likelihood-based Classifier
7.3 Minimum Distance Centroid Estimation and Non-parametric Likelihood Classifier
7.4 Conclusion
References
8 Comparison of Modulation Classifiers
8.1 Introduction
8.2 System Requirements and Applicable Modulations
8.3 Classification Accuracy with Additive Noise
8.4 Classification Accuracy with Limited Signal Length
8.5 Classification Robustness against Phase Offset
8.6 Classification Robustness against Frequency Offset
8.7 Computational Complexity
8.8 Conclusion
References
9 Modulation Classification for Civilian Applications
9.1 Introduction
9.2 Modulation Classification for High-order Modulations
9.3 Modulation Classification for Link-adaptation Systems
9.4 Modulation Classification for MIMO Systems
9.5 Conclusion
References
10 Modulation Classifier Design for Military Applications
10.1 Introduction
10.2 Modulation Classifier with Unknown Modulation Pool
10.3 Modulation Classifier against Low Probability of Detection
10.4 Conclusion
References
Index
End User License Agreement
List of Tables
Chapter 05
Table 5.1 Decision tree for modulations classification using spectral-based features
Chapter 08
Table 8.1 System requirements and applicable modulation for LB classifiers
Table 8.2 System requirements and applicable modulation for distribution test-based classifiers
Table 8.3 System requirements and applicable modulation for FB classifiers
Table 8.4 System requirements and applicable modulation for machine learning classifiers
Table 8.5 System requirements and applicable modulation for blind modulation classifiers
Table 8.6 Number of different operations needed for each classifier
List of Illustrations
Chapter 01
Figure 1.1 Military signal intelligence system.
Figure 1.2 AMC in link adaptation system.
Figure 1.3 Analogue communication system.
Figure 1.4 (a) Carrier signal, (b) source signal and (c) AM signal.
Figure 1.5 (a) Carrier signal, (b) source signal and (c) FM signal.
Figure 1.6 (a) Carrier signal, (b) source signal and (c) PM signal.
Figure 1.7 Digital communication system.
Figure 1.8 (a) Carrier signal, (b) source signal and (c) ASK signal.
Figure 1.9 (a) Carrier signal, (b) source signal and (c) FSK signal.
Figure 1.10 (a) Carrier signal (b) source signal and (c) PSK signal.
Figure 1.11 Constellation plots of 2-PAM, QPSK, 8-PSK and 16-QAM.
Chapter 02
Figure 2.1 Constellation of 4-QAM signal in AWGN with SNR = 10 dB.
Figure 2.2 PDF of 4-QAM signals in AWGN channel with SNR = 10 dB.
Figure 2.3 PDF of 4-QAM signals I-Q segments in AWGN channel with SNR = 10 dB.
Figure 2.4 Constellation of 4-QAM signal in slow fading channel.
Figure 2.5 Constellation of 4-QAM signal with frequency offset.
Chapter 03
Figure 3.1 Maximum likelihood classifier in fading channel with AWGN noise.
Chapter 04
Figure 4.1 Cumulative distribution probability of 4-QAM, 16-QAM and 64-QAM modulation signals in AWGN channel.
Chapter 05
Figure 5.1 Decision tree for modulations classification using spectral-based features.
Chapter 06
Figure 6.1 Two-class feature space with linear support vector machine.
Figure 6.2 Multilayer perceptron neural network for AMC feature combination.
Figure 6.3 Crossover in genetic algorithm.
Figure 6.4 Mutation in genetic algorithm.
Figure 6.5 Tree-structured individual of genetic programming.
Figure 6.6 Parent trees selected for crossover operation.
Figure 6.7 Child trees produced by crossover from trees in Figure 6.6.
Figure 6.8 Parent tree selected for mutation and a randomly generated branch.
Chapter 07
Figure 7.1 EM estimation and ML classifier.
Figure 7.2 Centroid estimation and NPLF classifier.
Chapter 08
Figure 8.1 Symbol mapping for different modulations on I-Q plane.
Figure 8.2 Classification accuracy of the ML classifier in AWGN channel.
Figure 8.3 Classification accuracy of the KS test classifier in AWGN channel.
Figure 8.4 Classification accuracy of the moment-based KNN classifier in AWGN channel.
Figure 8.5 Classification accuracy of the cumulant-based KNN classifier in AWGN channel.
Figure 8.6 Classification accuracy of the GP-KNN classifier in AWGN channel.
Figure 8.7 Classification accuracy of the EM-ML classifier in AWGN channel.
Figure 8.8 Average classification accuracy of all classifiers in AWGN channel.
Figure 8.9 Classification accuracy of the ML classifier with different signal length.
Figure 8.10 Classification accuracy of the KS test classifier with different signal length.
Figure 8.11 Classification accuracy of the moment-based classifier with different signal length.
Figure 8.12 Classification accuracy of the cumulant-based classifier with different signal length.
Figure 8.13 Classification accuracy of the GP-KNN classifier with different signal length.
Figure 8.14 Classification accuracy of the EM-ML classifier with different signal length.
Figure 8.15 Average classification accuracy of all classifiers with different signal length.
Figure 8.16 Classification accuracy of the ML classifier with phase offset.
Figure 8.17 Classification accuracy of the KS test classifier with phase offset.
Figure 8.18 Classification accuracy of the moment-based KNN classifier with phase offset.
Figure 8.19 Classification accuracy of the cumulant-based KNN classifier with phase offset.
Figure 8.20 Classification accuracy of the GP-KNN classifier with phase offset.
Figure 8.21 Classification accuracy of the EM-ML classifier with phase offset.
Figure 8.22 Average classification accuracy of all classifiers with phase offset.
Figure 8.23 Classification accuracy of the ML classifier with frequency offset.
Figure 8.24 Classification accuracy of the KS test classifier with frequency offset.
Figure 8.25 Classification accuracy of the moment-based KNN classifier with frequency offset.
Figure 8.26 Classification accuracy of the cumulant-based KNN classifier with frequency offset.
Figure 8.27 Classification accuracy of the GP-KNN classifier with frequency offset.
Figure 8.28 Classification accuracy of the EM-ML classifier with frequency offset.
Figure 8.29 Average classification accuracy of each classifier with frequency offset.
Chapter 09
Figure 9.1 AMC in wired system with high-order modulation.
Figure 9.2 AMC in wired system with link adaptation.
Figure 9.3 MIMO channel.
Figure 9.4 AMC in MIMO system using EM-ML classifier.
Chapter 10
Figure 10.1 Classification of unknown modulation pool.
Figure 10.2 Classification of DSSS signal.
Figure 10.3 Classification of FHSS signal.
Guide
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Table of Contents
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