By Zhechen Zhu
Automatic Modulation category (AMC) has been a key expertise in lots of army, defense, and civilian telecommunication functions for many years. In army and defense functions, modulation frequently serves as one other point of encryption; in glossy civilian functions, a number of modulation forms may be hired through a sign transmitter to manage the information fee and hyperlink reliability.
This ebook deals complete documentation of AMC types, algorithms and implementations for winning modulation attractiveness. It presents a useful theoretical and numerical comparability of AMC algorithms, in addition to suggestions on state of the art type designs with particular army and civilian purposes in mind.
- Provides a major 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 unique implementation for every set of rules in line with a unified theoretical heritage and a entire theoretical and numerical functionality comparison
- Gives transparent counsel for the layout of particular automated modulation classifiers for various useful purposes in either civilian and armed forces verbal exchange systems
- Includes a MATLAB toolbox on a spouse site providing the implementation of a range of equipment mentioned within the book
Read or Download Automatic Modulation Classification: Principles, Algorithms and Applications PDF
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Extra resources for Automatic Modulation Classification: Principles, Algorithms and Applications
While some unsupervised machine learning algorithms could function without a reference signal model, the optimization of such algorithms still relies on the knowledge of a known signal model. Meanwhile, as the validation of modulation classifiers are often realized by computer-aided simulation, accurate signal modelling provides meaningful scenarios for evaluating the performance of various modulation classifiers. The objective of this chapter is to establish some unified signal models for the development of all modulation classifiers from Chapters 3 to 7, and to provide a level ground for the validation of each modulation classifier in Chapter 8.
The possibility to specify the source distribution, propagation properties and beam patterns. The class A model is defined for the non-Gaussian noises with bandwidth narrower than the receiver bandwidth, while the class B model is defined for the non-Gaussian noises with a wider spectrum than the receiver. In the meantime, the class C model provides a combination of the class A and class B models. In this book, the class A model is adopted. 24), fω ðxÞ = e − AA ∞ X 2 − x2 AAk qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ e 4πσkA 2 k = 0 k!
Noise variance is normally best to be estimated when all the rest of channel parameters are accurately estimated. Panagiotou et al. 23). 1 1 − jr½n −αe 2 2σ max e LGLRT ðrÞ = max Lðrjα, σ, θo Þ = max Θ Θ A 2A M 2πσ 2 n=1 m N Y −jθo A m j 2 ð3:23Þ The complexity is notably further reduced. However, the classifier based on the modified GLRT likelihood function now becomes biased in both low SNR and high SNR scenarios. Assume the modified GLRT likelihood function is used to classify among 4-QAM and 16-QAM signals.