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Record Nr. |
UNINA9910807452803321 |
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Autore |
Zhu Zhechen |
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Titolo |
Automatic modulation classification : principles, algorithms, and applications / / Zhechen Zhu and Asoke K. Nand |
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Pubbl/distr/stampa |
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Chichester, England : , : Wiley, , 2015 |
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©2015 |
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ISBN |
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1-118-90650-0 |
1-118-90652-7 |
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Descrizione fisica |
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1 online resource (194 p.) |
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Disciplina |
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Soggetti |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references at the end of each chapters and index. |
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Nota di contenuto |
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Title Page; Copyright Page; Contents; About the Authors; Preface; List of Abbreviations; List of Symbols; Chapter 1 Introduction; 1.1 Background; 1.2 Applications ofAMC; 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 |
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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 |
6.4 Logistic Regression for Feature Combination |
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Sommario/riassunto |
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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 algo |
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