dc.contributor.author | Tang, S | |
dc.contributor.author | Xia, J | |
dc.contributor.author | Fan, L | |
dc.contributor.author | Lei, X | |
dc.contributor.author | Xu, W | |
dc.contributor.author | Nallanathan, A | |
dc.date.accessioned | 2024-07-16T08:41:39Z | |
dc.date.available | 2024-07-16T08:41:39Z | |
dc.date.issued | 2022 | |
dc.identifier.issn | 0018-9545 | |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/98169 | |
dc.description.abstract | Although the frequency-division duplex (FDD) massive multiple-input multiple-output (MIMO) system can offer high spectral and energy efficiency, it requires to feedback the downlink channel state information (CSI) from users to the base station (BS), in order to fulfill the precoding design at the BS. However, the large dimension of CSI matrices in the massive MIMO system makes the CSI feedback very challenging, and it is urgent to compress the feedback CSI. To this end, this paper proposes a novel dilated convolution based CSI feedback network, namely D ilated C hannel R econstruction Net work (DCRNet). Specifically, the dilated convolutions are used to enhance the receptive field (RecF) of the proposed DCRNet without increasing the convolution size. Moreover, advanced encoder and decoder blocks are designed to improve the reconstruction performance and reduce computational complexity as well. Numerical results are presented to show the superiority of the proposed DCRNet over the conventional networks. In particular, compared to the state-of-the-arts (SOTA) networks, the proposed DCRNet can achieve almost the same performance while reduce floating point operations (FLOPs) by about 30%. | en_US |
dc.format.extent | 11216 - 11221 | |
dc.publisher | IEEE | en_US |
dc.relation.ispartof | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY | |
dc.subject | Convolution | en_US |
dc.subject | Decoding | en_US |
dc.subject | Massive MIMO | en_US |
dc.subject | Feature extraction | en_US |
dc.subject | Sparse matrices | en_US |
dc.subject | Delays | en_US |
dc.subject | Precoding | en_US |
dc.subject | CSI feedback | en_US |
dc.subject | deep learning | en_US |
dc.subject | dilated convolutions | en_US |
dc.subject | massive MIMO | en_US |
dc.title | Dilated Convolution Based CSI Feedback Compression for Massive MIMO Systems | en_US |
dc.type | Article | en_US |
dc.rights.holder | © 2022 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.identifier.doi | 10.1109/TVT.2022.3183596 | |
pubs.author-url | https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000870332400076&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=612ae0d773dcbdba3046f6df545e9f6a | en_US |
pubs.issue | 10 | en_US |
pubs.notes | Not known | en_US |
pubs.publication-status | Published | en_US |
pubs.volume | 71 | en_US |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |
rioxxterms.funder.project | b215eee3-195d-4c4f-a85d-169a4331c138 | en_US |