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E-raamat: Automatic Modulation Classification: Principles, Algorithms and Applications

(Brunel University London, UK), (Brunel University London, UK)
  • Formaat: PDF+DRM
  • Ilmumisaeg: 15-Dec-2014
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781118906521
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Automatic Modulation Classification: Principles, Algorithms and ApplicationsSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 15-Dec-2014
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781118906521
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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
About the Authors xi
Preface xiii
List of Abbreviations xv
List of Symbols xix
1 Introduction 1(18)
1.1 Background
1(1)
1.2 Applications of AMC
2(3)
1.2.1 Military Applications
2(1)
1.2.2 Civilian Applications
3(2)
1.3 Field Overview and Book Scope
5(1)
1.4 Modulation and Communication System Basics
6(10)
1.4.1 Analogue Systems and Modulations
6(2)
1.4.2 Digital Systems and Modulations
8(7)
1.4.3 Received Signal with Channel Effects
15(1)
1.5 Conclusion
16(1)
References
16(3)
2 Signal Models for Modulation Classification 19(16)
2.1 Introduction
19(1)
2.2 Signal Model in AWGN Channel
20(5)
2.2.1 Signal Distribution of I-Q Segments
21(2)
2.2.2 Signal Distribution of Signal Phase
23(2)
2.2.3 Signal Distribution of Signal Magnitude
25(1)
2.3 Signal Models in Fading Channel
25(3)
2.4 Signal Models in Non-Gaussian Channel
28(3)
2.4.1 Middleton's Class A Model
28(2)
2.4.2 Symmetric Alpha Stable Model
30(1)
2.4.3 Gaussian Mixture Model
30(1)
2.5 Conclusion
31(1)
References
32(3)
3 Likelihood-based Classifiers 35(14)
3.1 Introduction
35(1)
3.2 Maximum Likelihood Classifiers
36(4)
3.2.1 Likelihood Function in AWGN Channels
36(2)
3.2.2 Likelihood Function in Fading Channels
38(1)
3.2.3 Likelihood Function in Non-Gaussian Noise Channels
39(1)
3.2.4 Maximum Likelihood Classification Decision Making
39(1)
3.3 Likelihood Ratio Test for Unknown Channel Parameters
40(4)
3.3.1 Average Likelihood Ratio Test
40(1)
3.3.2 Generalized Likelihood Ratio Test
41(2)
3.3.3 Hybrid Likelihood Ratio Test
43(1)
3.4 Complexity Reduction
44(1)
3.4.1 Discrete Likelihood Ratio Test and Lookup Table
44(1)
3.4.2 Minimum Distance Likelihood Function
45(1)
3.4.3 Non-Parametric Likelihood Function
45(1)
3.5 Conclusion
45(1)
References
46(3)
4 Distribution Test-based Classifier 49(16)
4.1 Introduction
49(1)
4.2 Kolmogorov-Smirnov Test Classifier
50(7)
4.2.1 The KS Test for Goodness of Fit
51(2)
4.2.2 One-sample KS Test Classifier
53(2)
4.2.3 Two-sample KS Test Classifier
55(1)
4.2.4 Phase Difference Classifier
56(1)
4.3 Cramer-Von Mises Test Classifier
57(1)
4.4 Anderson-Darling Test Classifier
57(1)
4.5 Optimized Distribution Sampling Test Classifier
58(5)
4.5.1 Sampling Location Optimization
59(1)
4.5.2 Distribution Sampling
60(1)
4.5.3 Classification Decision Metrics
61(1)
4.5.4 Modulation Classification Decision Making
62(1)
4.6 Conclusion
63(1)
References
63(2)
5 Modulation Classification Features 65(16)
5.1 Introduction
65(1)
5.2 Signal Spectral-based Features
66(5)
5.2.1 Signal Spectral-based Features
66(3)
5.2.2 Spectral-based Features Specialities
69(1)
5.2.3 Spectral-based Features Decision Making
70(1)
5.2.4 Decision Threshold Optimization
70(1)
5.3 Wavelet Transform-based Features
71(3)
5.4 High-order Statistics-based Features
74(2)
5.4.1 High-order Moment-based Features
74(1)
5.4.2 High-order Cumulant-based Features
75(1)
5.5 Cyclostationary Analysis-based Features
76(3)
5.6 Conclusion
79(1)
References
79(2)
6 Machine Learning for Modulation Classification 81(16)
6.1 Introduction
81(1)
6.2 K-Nearest Neighbour Classifier
81(3)
6.2.1 Reference Feature Space
82(1)
6.2.2 Distance Definition
82(1)
6.2.3 K-Nearest Neighbour Decision
83(1)
6.3 Support Vector Machine Classifier
84(2)
6.4 Logistic Regression for Feature Combination
86(1)
6.5 Artificial Neural Network for Feature Combination
87(2)
6.6 Genetic Algorithm for Feature Selection
89(1)
6.7 Genetic Programming for Feature Selection and Combination
90(4)
6.7.1 Tree-structured Solution
91(1)
6.7.2 Genetic Operators
91(2)
6.7.3 Fitness Evaluation
93(1)
6.8 Conclusion
94(1)
References
94(3)
7 Blind Modulation Classification 97(12)
7.1 Introduction
97(1)
7.2 Expectation Maximization with Likelihood-based Classifier
98(5)
7.2.1 Expectation Maximization Estimator
98(3)
7.2.2 Maximum Likelihood Classifier
101(1)
7.2.3 Minimum Likelihood Distance Classifier
102(1)
7.3 Minimum Distance Centroid Estimation and Non-parametric Likelihood Classifier
103(4)
7.3.1 Minimum Distance Centroid Estimation
103(2)
7.3.2 Non-parametric Likelihood Function
105(2)
7.4 Conclusion
107(1)
References
107(2)
8 Comparison of Modulation Classifiers 109(32)
8.1 Introduction
109(1)
8.2 System Requirements and Applicable Modulations
110(1)
8.3 Classification Accuracy with Additive Noise
110(10)
8.3.1 Benchmarking Classifiers
113(1)
8.3.2 Performance Comparison in AWGN Channel
114(6)
8.4 Classification Accuracy with Limited Signal Length
120(6)
8.5 Classification Robustness against Phase Offset
126(6)
8.6 Classification Robustness against Frequency Offset
132(5)
8.7 Computational Complexity
137(1)
8.8 Conclusion
138(1)
References
139(2)
9 Modulation Classification for Civilian Applications 141(12)
9.1 Introduction
141(1)
9.2 Modulation Classification for High-order Modulations
141(2)
9.3 Modulation Classification for Link-adaptation Systems
143(1)
9.4 Modulation Classification for MIMO Systems
144(6)
9.5 Conclusion
150(1)
References
150(3)
10 Modulation Classifier Design for Military Applications 153(8)
10.1 Introduction
153(1)
10.2 Modulation Classifier with Unknown Modulation Pool
154(3)
10.3 Modulation Classifier against Low Probability of Detection
157(3)
10.3.1 Classification of DSSS Signals
157(1)
10.3.2 Classification of FHSS Signals
158(2)
10.4 Conclusion
160(1)
References
160(1)
Index 161
Zhechen Zhu, Department of Electronic & Computer Engineering, Brunel University London, UK Zhechen Zhu received his B.Eng. degree in the Department of Electrical Engineering and Electronics from the University of Liverpool in 2010.  His undergraduate project was awarded the Farnell Company Prize. He is currently pursuing his PhD degree at Brunel University conducting research on the subject of automatic modulation classification. His research interests include high order statistics, machine learning, statistical signal processing, blind signal processing, and their application in signal estimation and classification.

Asoke K. Nandi, Department of Electronic & Computer Engineering, Brunel University London, UK Prof. Nandi is Chair and Head of the Electronic and Computer Engineering Department at Brunel University London, UK. He leads the Signal Processing and Communications Research Group with interests in the areas of signal processing, machine learning, and communications research. He is a Finland Distinguished Professor at the University of Jyvaskyla, Finland. In 1983 Professor Nandi was a member of the UA1 team at CERN that discovered the three fundamental particles known as W+, W and Z0, providing the evidence for the unification of the electromagnetic and weak forces, which was recognized by the Nobel Committee for Physics in 1984. He has authored or co-authored more than 190 journal papers, and 2 books. The Google Scholar h-index of his publications is 54. In 2010 he received the Glory of Bengal Award for his outstanding achievements in scientific research, and in 2012 was awarded the IEEE Heinrich Hertz Award. Prof. Nandi is a Fellow of the IEEE.
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