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    A unified practical approach to modulation classification in cognitive radio using likelihood-based techniques

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    Publication date
    2015
    Author
    Salam, A.O.A.
    Sheriff, Ray E.
    Al-Araji, S.R.
    Mezher, K.
    Nasir, Q.
    Keyword
    Likelihood algorithms; Cognitive radio; Automatic modulation classification
    Peer-Reviewed
    Yes
    
    Metadata
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    Abstract
    he automatic classification of digital modulated signals has been subject to extensive studies over the last decade, with numerous scholarly articles and research studies published. This paper provides an insightful guidance and discussion on the most practical approaches of automatic modulation classification (AMC) in cognitive radio (CR) using likelihood based (LB) statistical tests. It also suggests a novel idea of storing the known constellation sets on the receiver side using a look-up table (LUT) to detect the transmitted replica. Relevant performance measures with simulated comparisons in flat fading additive white Gaussian noise (AWGN) channels are examined. Namely, the average likelihood ratio test (ALRT), generalized LRT (GLRT) and hybrid LRT (HLRT) are particularly illustrated using linearly phase-modulated signals such as M-ary phase shift keying (MPSK) and quadrature amplitude modulation (MQAM). When the unknown signal constellation is estimated using the maximum likelihood (ML) method, results indicate that the HLRT performs well and near optimal in most situations without extra computational burden.
    URI
    http://hdl.handle.net/10454/9216
    Version
    No full-text available in the repository
    Citation
    Salam AOA, Sheriff RE, Al-Araji SR et al (2015) A unified practical approach to modulation classification in cognitive radio using likelihood-based techniques. In: Proceedings of the IEEE 28th Canadian Conference on Electrical and Computer Engineering (CCECE). 3-6 May 2015, Halifax, Canada: 1024-1029.
    Link to publisher’s version
    http://dx.doi.org/10.1109/CCECE.2015.7129415
    Type
    Conference Paper
    Collections
    Engineering and Informatics Publications

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