By Zhechen Zhu

Automatic Modulation class (AMC) has been a key expertise in lots of army, protection, and civilian telecommunication functions for many years. In army and safeguard purposes, modulation usually serves as one other point of encryption; in glossy civilian purposes, a number of modulation kinds may be hired through a sign transmitter to manage the knowledge fee and hyperlink reliability.

This booklet deals entire documentation of AMC types, algorithms and implementations for winning modulation acceptance. It presents a useful theoretical and numerical comparability of AMC algorithms, in addition to suggestions on state of the art category designs with particular army and civilian functions in mind.

Key Features:

  • Provides an incredible choice of AMC algorithms in 5 significant different types, from likelihood-based classifiers and distribution-test-based classifiers to feature-based classifiers, computer studying assisted classifiers and blind modulation classifiers
  • Lists certain implementation for every set of rules in line with a unified theoretical historical past and a finished theoretical and numerical functionality comparison
  • Gives transparent counsel for the layout of particular computerized modulation classifiers for various functional functions in either civilian and army conversation systems
  • Includes a MATLAB toolbox on a spouse web site supplying the implementation of a range of tools mentioned within the book

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Extra resources for Automatic Modulation Classification: Principles, Algorithms and Applications

Sample text

1988) Cyclic Spectral Analysis for Signal Detection and Modulation Recognition. Military Communications Conference,San Diego, CA, USA, 23–26 October 1988, pp. 419–424. J. and Chua, S. (1998) Adaptive coded modulation for fading channels. IEEE Transactions on Communications, 46 (5), 595–602. A. and Popescu, D. (2009) On the likelihood-based approach to modulation classification. IEEE Transactions on Wireless Communications, 8 (12), 5884–5892. , Hamouda, W. et al. (2012) Blind digital modulation identification for spatially-correlated MIMO systems.

2010) Fast and robust modulation classification via KolmogorovSmirnov test. IEEE Transactions on Communications, 58 (8), 2324–2332. Wei, W. M. (2000) Maximum-likelihood classification for digital amplitude-phase modulations. IEEE Transactions on Communications, 48 (2), 189–193. W. K. (2014) Genetic algorithm optimized distribution sampling test for M-QAMmodulation classification. Signal Processing, 94, 264–277. 1 Introduction Likelihood-based (LB) modulation classifiers are by far the most popular modulation classification approaches.

Military Communications Conference,San Diego, CA, USA, 23–26 October 1988, pp. 419–424. J. and Chua, S. (1998) Adaptive coded modulation for fading channels. IEEE Transactions on Communications, 46 (5), 595–602. A. and Popescu, D. (2009) On the likelihood-based approach to modulation classification. IEEE Transactions on Wireless Communications, 8 (12), 5884–5892. , Hamouda, W. et al. (2012) Blind digital modulation identification for spatially-correlated MIMO systems. IEEE Transactions on Wireless Communications, 11 (2), 683–693.

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