Automatic modulation classification using interacting multiple model - Kalman filter for channel estimation
dc.contributor.author | Abdul Salam, Ahmed O. | |
dc.contributor.author | Sheriff, Ray E. | |
dc.contributor.author | Hu, Yim Fun | |
dc.contributor.author | Al-Araji, S.R. | |
dc.contributor.author | Mezher, K. | |
dc.date.accessioned | 2019-07-26T21:05:43Z | |
dc.date.accessioned | 2019-08-07T16:11:53Z | |
dc.date.available | 2019-07-26T21:05:43Z | |
dc.date.available | 2019-08-07T16:11:53Z | |
dc.date.issued | 2019-09 | |
dc.identifier.citation | Abdul 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. | |
dc.identifier.uri | http://hdl.handle.net/10454/17197 | |
dc.description | Yes | |
dc.description.abstract | A 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. | |
dc.language.iso | en | en |
dc.publisher | Institute 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. | |
dc.subject | Automatic modulation classification | |
dc.subject | Kalman filter | |
dc.subject | Interacting multiple model | |
dc.subject | Channel estimation | |
dc.title | Automatic modulation classification using interacting multiple model - Kalman filter for channel estimation | |
dc.status.refereed | Yes | |
dc.date.application | 2019-07-23 | |
dc.type | Article | |
dc.type.version | Accepted manuscript | |
dc.identifier.doi | https://doi.org/10.1109/TVT.2019.2930469 | |
dc.rights.license | Unspecified | |
dc.date.updated | 2019-07-26T20:05:57Z | |
refterms.dateFOA | 2019-08-07T16:12:18Z | |
dc.openaccess.status | openAccess |