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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Vydáno v:	Electronics letters Ročník 52; číslo 18; s. 1569 - 1571
Hlavní autor:	Dulek, B
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	The Institution of Engineering and Technology 02.09.2016
Témata:	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
On-line přístup:	Získat plný text
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Shrnutí:	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 only noise. Expectation–maximisation algorithm is employed to compute the ML estimates of the unknown channel parameters, which are then substituted into the corresponding likelihood expressions to perform hypothesis testing. Numerical simulations indicate that a suboptimal classifier, which is ignorant to data failures, exhibits extremely poor performance in the presence of high failure rates. On the other hand, the proposed classifier demonstrates comparable performance with that of the clairvoyant classifier which is assumed to have a priori knowledge of the channel parameters and data failures.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0013-5194 1350-911X 1350-911X
DOI:	10.1049/el.2016.1611