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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Summary: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.
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ISSN:0013-5194
1350-911X
1350-911X
DOI:10.1049/el.2016.1611