Automatic Modulation Classification

Principles, Algorithms and Applications

Zhechen Zhu, Asoke K. Nandi

• Publisher: Wiley
• Year: 2014
• Print ISBN: 9781118906491
• eBook ISBN: 9781118906514
• Edition: 1
• Topic: Technology & Engineering
• Subtopic: Signals & Signal Processing
• Index: Technology & Engineering

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

1
Introduction

1.1 Background

Automatic modulation classification (AMC) was first motivated by its application in military scenarios where electronic warfare, surveillance and threat analysis requires the recognition of signal modulations in order to identify adversary transmitting units, to prepare jamming signals, and to recover the intercepted signal. The term ‘automatic’ is used as opposed to the initial implementation of manual modulation classification where signals are processed by engineers with the aid of signal observation and processing equipment. Most modulation classifiers developed in the past 20 years are implemented through electronic processors. During the 1980s and 1990s there were considerable numbers of researchers in the field of signal processing and communications who dedicated their work to the problem of automatic modulation classification. This led to the publication of the first well received book on the subject by Azzouz and Nandi (1996). The interest in AMC for military purposes is sustained to this very day.

The beginning of twenty-first century saw a large number of innovations in communications technology. Among them are few that made essential contributions to the staggering increase of transmission throughput in various communication systems. Link adaptation (LA), also known as adaptive modulation and coding (AM&C), creates an adaptive modulation scheme where a pool of multiple modulations are employed by the same system (Goldsmith and Chua, 1998). It enables the optimization of the transmission reliability and data rate through the adaptive selection of modulation schemes according to channel conditions. While the transmitter has the freedom to choose how the signals are modulated, the receiver must have the knowledge of the modulation type to demodulation the signal so that the transmission can be successful. An easy way to achieve that is to include the modulation information in each signal frame so that the receivers would be notified about the change in modulation scheme, and react accordingly. However, this strategy affects the spectrum efficiency due to the extra modulation information in each signal frame. In the current situation where the wireless spectrum is extremely limited and valuable, the aforementioned strategy is simply not efficient enough. For this reason, AMC becomes an attractive solution to the problem. Thanks to the development in microprocessors, receivers nowadays are much enabled in terms of their computational power. Thus, the signal processing required by AMC algorithms becomes feasible. By automatically identifying the modulation type of the received signal, the receiver does not need to be notified about the modulation type and the demodulation can still be successfully achieved. In the end, spectrum efficiency is improved as no modulation information is needed in the transmitted signal frame. AMC has become an integral part of intelligent radio systems, including cognitive radio and software-defined radio.

Over the years, there have been many terms used to describe the same problem: modulation recognition, automatic modulation recognition, modulation identification, modulation classification, and automatic modulation classification. There are other names for the problem, such as PSK (phase-shift keying modulation) classification and M-QAM (M-ary quadrature amplitude modulation) classification that have a more specific target but which still operate under the same principle of automatic modulation classification. In this book, we have decided to use automatic modulation classification and AMC as a consistent reference to the same problem.

1.2 Applications of AMC

Having discussed the possible use of AMC in both military and civilian scenarios, in thi...

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
• 7 Blind Modulation Classification
• 8 Comparison of Modulation Classifiers
• 9 Modulation Classification for Civilian Applications
• 10 Modulation Classifier Design for Military Applications
• Index
• End User License Agreement