ML modulation classification in presence of unreliable observations

Joint detection and maximum-likelihood (ML) classification of linear modulations based on observations collected over an unknown flat-fading additive Gaussian noise channel is considered. It is assumed that some of the observations are subject to data failures, in which case the receiver acquires on...

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Bibliographic Details
Published in:	Electronics letters Vol. 52; no. 18; pp. 1569 - 1571
Main Author:	Dulek, B
Format:	Journal Article
Language:	English
Published:	The Institution of Engineering and Technology 02.09.2016
Subjects:	channel parameters Channels Classification Classifiers data failures expectation‐maximisation algorithm fading channels Failure flat fading additive Gaussian noise channel Gaussian noise linear modulations Mathematical models maximum likelihood classification maximum likelihood estimation ML modulation classification Modulation Noise Parameters suboptimal classifier unreliable observations Wireless communications unreliable observations modulation maximum likelihood estimation ML modulation classification channel parameters fading channels Gaussian noise data failures flat fading additive Gaussian noise channel linear modulations expectation-maximisation algorithm suboptimal classifier maximum likelihood classification
ISSN:	0013-5194, 1350-911X, 1350-911X
Online Access:	Get full text
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