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dc.contributor.authorAbdul Salam, Ahmed O.
dc.contributor.authorSheriff, Ray E.
dc.contributor.authorHu, Yim Fun
dc.contributor.authorAl-Araji, S.R.
dc.contributor.authorMezher, K.
dc.date.accessioned2019-07-26T21:05:43Z
dc.date.accessioned2019-08-07T16:11:53Z
dc.date.available2019-07-26T21:05:43Z
dc.date.available2019-08-07T16:11:53Z
dc.date.issued2019-09
dc.identifier.citationAbdul Salam AO, Sheriff RE, Hu YF et al (2019) Automatic modulation classification using interacting multiple model - Kalman filter for channel estimation. IEEE Transactions on Vehicular Technology. 68(9): 8928-8939.en_US
dc.identifier.urihttp://hdl.handle.net/10454/17197
dc.descriptionYesen_US
dc.description.abstractA rigorous model for automatic modulation classification (AMC) in cognitive radio (CR) systems is proposed in this paper. This is achieved by exploiting the Kalman filter (KF) integrated with an adaptive interacting multiple model (IMM) for resilient estimation of the channel state information (CSI). A novel approach is proposed, in adding up the squareroot singular values (SRSV) of the decomposed channel using the singular value decompositions (SVD) algorithm. This new scheme, termed Frobenius eigenmode transmission (FET), is chiefly intended to maintain the total power of all individual effective eigenmodes, as opposed to keeping only the dominant one. The analysis is applied over multiple-input multiple-output (MIMO) antennas in combination with a Rayleigh fading channel using a quasi likelihood ratio test (QLRT) algorithm for AMC. The expectation-maximization (EM) is employed for recursive computation of the underlying estimation and classification algorithms. Novel simulations demonstrate the advantages of the combined IMM-KF structure when compared to the perfectly known channel and maximum likelihood estimate (MLE), in terms of achieving the targeted optimal performance with the desirable benefit of less computational complexity loads.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronic Engineers
dc.rights© 2019 IEEE. Reproduced in accordance with the publisher's self-archiving policy. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.subjectAutomatic modulation classificationen_US
dc.subjectKalman filteren_US
dc.subjectInteracting multiple modelen_US
dc.subjectChannel estimationen_US
dc.titleAutomatic modulation classification using interacting multiple model - Kalman filter for channel estimationen_US
dc.status.refereedYesen_US
dc.date.application2019-07-23
dc.typeArticleen_US
dc.type.versionAccepted manuscripten_US
dc.identifier.doihttps://doi.org/10.1109/TVT.2019.2930469
dc.date.updated2019-07-26T20:05:57Z
refterms.dateFOA2019-08-07T16:12:18Z


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