Book description
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
Table of contents
- Cover
- Title page
- Copyright page
- Dedication page
- About the Authors
- Preface
- List of Abbreviations
- List of Symbols
- 1 Introduction
- 2 Signal Models for Modulation Classification
- 3 Likelihood-based Classifiers
- 4 Distribution Test-based Classifier
- 5 Modulation Classification Features
-
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
-
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
- 10 Modulation Classifier Design for Military Applications
- Index
- End User License Agreement
Product information
- Title: Automatic Modulation Classification: Principles, Algorithms and Applications
- Author(s):
- Release date: February 2015
- Publisher(s): Wiley
- ISBN: 9781118906491
You might also like
book
Modulation and Coding Techniques in Wireless Communications
The high level of technical detail included in standards specifications can make it difficult to find …
book
Bandwidth-Efficient Digital Modulation with Application to Deep-Space Communications
An important look at bandwidth-efficient modulations with applications to today's Space program Based on research and …
book
Transmission Lines in Digital and Analog Electronic Systems: Signal Integrity and Crosstalk
In the last 30 years there have been dramatic changes in electrical technology--yet the length of …
book
Mathematical Foundations for Signal Processing, Communications, and Networking
Mathematical Foundations for Signal Processing, Communications, and Networking describes mathematical concepts and results important in the …