Automatic Modulation Classification: Principles, Algorithms and Applications - Hardcover

Über die Autorin bzw. den Autor

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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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

Aus dem Klappentext

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