Signal Processing

Download Automatic Modulation Classification: Principles, Algorithms by Zhechen Zhu PDF

By Zhechen Zhu

Automatic Modulation type (AMC) has been a key know-how in lots of army, safety, and civilian telecommunication functions for many years. In army and safeguard functions, modulation frequently serves as one other point of encryption; in smooth civilian purposes, a number of modulation forms will be hired by means of a sign transmitter to regulate the information expense and hyperlink reliability.

This e-book bargains complete documentation of AMC versions, algorithms and implementations for winning modulation reputation. It offers a useful theoretical and numerical comparability of AMC algorithms, in addition to information on state of the art type designs with particular army and civilian purposes in mind.

Key Features:

  • Provides a big selection of AMC algorithms in 5 significant different types, from likelihood-based classifiers and distribution-test-based classifiers to feature-based classifiers, computing device studying assisted classifiers and blind modulation classifiers
  • Lists specific implementation for every set of rules in line with a unified theoretical heritage and a complete theoretical and numerical functionality comparison
  • Gives transparent tips for the layout of particular automated modulation classifiers for various sensible purposes in either civilian and armed forces conversation systems
  • Includes a MATLAB toolbox on a spouse site delivering 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

Example text

E. K. (1996) Automatic Modulation Recognition of Communication Signals, Kluwer, Boston. R. (1956) Methods of solving noise problems. Proceedings of the IRE, 44, 609–638. G. M. (2011) Maximum-likelihood classification of digital amplitude-phase modulated signals in flat fading non-Gaussian channels. IEEE Transactions on Communications, 59 (8), 2051–2056. G. M. (2013) Classification of digital amplitude-phase modulated signals in time-correlated non-Gaussian channels. IEEE Transactions on Communications, 61 (6), 2408–2419.

531–536. Swami, A. M. (2000) Hierarchical digital modulation classification using cumulants. IEEE Transactions on Communications, 48 (3), 416–429. , Pawełczak, P. and Cˇ abric, D. (2011) Computationally efficient modulation level classification based on probability distribution distance functions. IEEE Communications Letters, 15 (5), 476–478. S. (1984) Threshold detection in narrow-band non-Gaussian noise. IEEE Transactions on Communications, C (2), 134–139. Wang, F. and Wang, X. (2010) Fast and robust modulation classification via KolmogorovSmirnov test.

The unique advantage of the magnitude likelihood appears when a phase or frequency offset is observed in the transmission channel. As the PDF of signal magnitude in AWGN or fading channel only consists of the magnitude of the transmitted symbols, any rotational shift would not alter the resulting probability evaluation. 10). 2 Likelihood Function in Fading Channels Though the likelihood functions in AWGN channels are the primary subject of research, it would be interesting to derive their variant in fading channels.

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