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dc.contributor.authorLuo, Qiwu
dc.contributor.authorZhou, Jian
dc.contributor.authorSun, Yichuang
dc.contributor.authorSimpson, Oluyomi
dc.date.accessioned2020-10-24T23:04:10Z
dc.date.available2020-10-24T23:04:10Z
dc.date.issued2020-07-27
dc.identifier.citationLuo , Q , Zhou , J , Sun , Y & Simpson , O 2020 , Jointly optimized echo state network for short-term channel state information prediction of fading channel . in 2020 International Wireless Communications and Mobile Computing, IWCMC 2020 . , 9148072 , 2020 International Wireless Communications and Mobile Computing, IWCMC 2020 , pp. 1480-1484 , International conference on Wireless Communications & Mobile Computing , Limassol , Cyprus , 15/06/20 . https://doi.org/10.1109/IWCMC48107.2020.9148072
dc.identifier.citationconference
dc.identifier.isbn9781728131290
dc.identifier.otherPURE: 21120708
dc.identifier.otherPURE UUID: d293ccf1-bcf9-40f7-b7b1-e98274604290
dc.identifier.otherScopus: 85089676945
dc.identifier.urihttp://hdl.handle.net/2299/23327
dc.description© 2020 IEEE. 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.description.abstractAccurately obtaining channel state information (CSI) in wireless systems is significant but challenging. This paper focuses the technique of machine-learning-based channel estimation. In particular, a jointly optimized echo state network (JOESN) is proposed to form a concept of the CSI prediction which is made up of two interacting aspects of output weight regularization and initial parameter optimization. First, in order to enhance noise robustness, a sparse regression based on L2 regularization is employed to finely learn the output weights of ESN. Second, vital reservoir parameters (i.e., global scaling factor, reservoir size, scaling coefficient and sparsity degree) are learned by a linear-weighted particle swarm optimization (LWPSO) for further improve the prediction accuracy and reliability. The experiments about computational complexity and three evaluating metrics are carried out on two chaotic benchmarks and one real-world dataset. The analyzed results indicate that the JOESN performs promisingly on multivariate chaotic time series prediction.en
dc.format.extent5
dc.language.isoeng
dc.relation.ispartof2020 International Wireless Communications and Mobile Computing, IWCMC 2020
dc.relation.ispartofseries2020 International Wireless Communications and Mobile Computing, IWCMC 2020
dc.rightsOpen
dc.subjectWireless communications
dc.subjectchannel state information (CSI)
dc.subjectchannel estimation
dc.subjectfading channel
dc.subjectreservoir computing
dc.subjectElectrical and Electronic Engineering
dc.titleJointly optimized echo state network for short-term channel state information prediction of fading channelen
dc.contributor.institutionCentre for Engineering Research
dc.contributor.institutionCommunications and Intelligent Systems
dc.contributor.institutionSchool of Physics, Engineering & Computer Science
dc.contributor.institutionDepartment of Engineering and Technology
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85089676945&partnerID=8YFLogxK
dc.relation.schoolSchool of Physics, Engineering & Computer Science
dc.description.versiontypeFinal Accepted Version
dcterms.dateAccepted2020-07-27
rioxxterms.versionAM
rioxxterms.versionofrecordhttps://doi.org/10.1109/IWCMC48107.2020.9148072
rioxxterms.licenseref.uriOther
rioxxterms.typeOther
herts.preservation.rarelyaccessedtrue
herts.rights.accesstypeOpen


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