By Zhechen Zhu
Automatic Modulation class (AMC) has been a key know-how in lots of army, safeguard, and civilian telecommunication purposes for many years. In army and protection functions, modulation usually serves as one other point of encryption; in smooth civilian functions, a number of modulation kinds could be hired by means of a sign transmitter to manage the information cost and hyperlink reliability.
This e-book bargains entire documentation of AMC versions, algorithms and implementations for profitable modulation attractiveness. It offers a useful theoretical and numerical comparability of AMC algorithms, in addition to counsel on state of the art category designs with particular army and civilian functions in mind.
- Provides an enormous number of AMC algorithms in 5 significant different types, from likelihood-based classifiers and distribution-test-based classifiers to feature-based classifiers, desktop studying assisted classifiers and blind modulation classifiers
- Lists specific implementation for every set of rules according to a unified theoretical history and a accomplished theoretical and numerical functionality comparison
- Gives transparent suggestions for the layout of particular computerized modulation classifiers for various sensible purposes in either civilian and army communique systems
- Includes a MATLAB toolbox on a significant other site delivering the implementation of a range of tools mentioned within the book
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Additional resources for Automatic Modulation Classification: Principles, Algorithms and Applications
And Polydoros, A. (1995) Likelihood methods for MPSK modulation classification. IEEE Transactions on Communications, 43 (2), 1493–1504. Middleton, D. (1999) Non-Gaussian noise models in signal processing for telecommunications: new methods and results for class A and class B noise models. IEEE Transactions on Information Theory, 45 (4), 1129–1149. L. and Shao, M. , New York. Shi, Q. and Karasawa, Y. (2012) Automatic modulation identification based on the probability density function of signal phase.
As the GMM will be used as the primary model for impulsive noise, therefore we derive the PDFs of received signals in the non-Gaussian channel with a GMM noise model. Assume the GMM uses K components where the probability λk and variance σ 2k for each component are either known or estimated. 32), respectively. 5 Conclusion In this chapter, we establish the signal models required for constructing modulation classifiers in the following chapters. The signal models are also needed in the computer-aided simulations for the generation of signals for classification.
Fading, especially deep fading, drastically changes the property of the transmitted signal and imposes a tough challenge on the robustness of a modulation classifier. Though early literature on modulation classifier focused on the validation of algorithms in AWGN channel, the current standard requires the robustness in fading channel as an important trait. In this chapter, a unified model of a fading channel is presented with flexible representation of different fading scenarios. It is worth noting that AWGN noise will also be considered in the fading channel as to approach a more realistic real world channel condition.