ML modulation classification in presence of unreliable observations

• 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
• DOI: 10.1049/el.2016.1611

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.