dc.contributor.author | Luo, Qiwu | |
dc.contributor.author | Zhou, Jian | |
dc.contributor.author | Sun, Yichuang | |
dc.contributor.author | Simpson, Oluyomi | |
dc.date.accessioned | 2020-10-24T23:04:10Z | |
dc.date.available | 2020-10-24T23:04:10Z | |
dc.date.issued | 2020-07-27 | |
dc.identifier.citation | Luo , 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.citation | conference | |
dc.identifier.isbn | 9781728131290 | |
dc.identifier.uri | http://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.abstract | Accurately 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.extent | 5 | |
dc.format.extent | 340730 | |
dc.language.iso | eng | |
dc.relation.ispartof | 2020 International Wireless Communications and Mobile Computing, IWCMC 2020 | |
dc.relation.ispartofseries | 2020 International Wireless Communications and Mobile Computing, IWCMC 2020 | |
dc.subject | Wireless communications | |
dc.subject | channel state information (CSI) | |
dc.subject | channel estimation | |
dc.subject | fading channel | |
dc.subject | reservoir computing | |
dc.subject | Electrical and Electronic Engineering | |
dc.title | Jointly optimized echo state network for short-term channel state information prediction of fading channel | en |
dc.contributor.institution | Centre for Engineering Research | |
dc.contributor.institution | Communications and Intelligent Systems | |
dc.contributor.institution | School of Physics, Engineering & Computer Science | |
dc.contributor.institution | Department of Engineering and Technology | |
dc.identifier.url | http://www.scopus.com/inward/record.url?scp=85089676945&partnerID=8YFLogxK | |
rioxxterms.versionofrecord | 10.1109/IWCMC48107.2020.9148072 | |
rioxxterms.type | Other | |
herts.preservation.rarelyaccessed | true | |