RIS 辅助通信中基于深度学习的高效级联信道反馈

Yiming Cui, Jiajia Guo, Chao-Kai Wen, Shi Jin
{"title":"RIS 辅助通信中基于深度学习的高效级联信道反馈","authors":"Yiming Cui, Jiajia Guo, Chao-Kai Wen, Shi Jin","doi":"arxiv-2409.08149","DOIUrl":null,"url":null,"abstract":"In the realm of reconfigurable intelligent surface (RIS)-assisted\ncommunication systems, the connection between a base station (BS) and user\nequipment (UE) is formed by a cascaded channel, merging the BS-RIS and RIS-UE\nchannels. Due to the fixed positioning of the BS and RIS and the mobility of\nUE, these two channels generally exhibit different time-varying\ncharacteristics, which are challenging to identify and exploit for feedback\noverhead reduction, given the separate channel estimation difficulty. To\naddress this challenge, this letter introduces an innovative deep\nlearning-based framework tailored for cascaded channel feedback, ingeniously\ncapturing the intrinsic time variation in the cascaded channel. When an entire\ncascaded channel has been sent to the BS, this framework advocates the feedback\nof an efficient representation of this variation within a subsequent period\nthrough an extraction-compression scheme. This scheme involves RIS unit-grained\nchannel variation extraction, followed by autoencoder-based deep compression to\nenhance compactness. Numerical simulations confirm that this feedback framework\nsignificantly reduces both the feedback and computational burdens.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Efficient Deep Learning-based Cascaded Channel Feedback in RIS-Assisted Communications\",\"authors\":\"Yiming Cui, Jiajia Guo, Chao-Kai Wen, Shi Jin\",\"doi\":\"arxiv-2409.08149\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the realm of reconfigurable intelligent surface (RIS)-assisted\\ncommunication systems, the connection between a base station (BS) and user\\nequipment (UE) is formed by a cascaded channel, merging the BS-RIS and RIS-UE\\nchannels. Due to the fixed positioning of the BS and RIS and the mobility of\\nUE, these two channels generally exhibit different time-varying\\ncharacteristics, which are challenging to identify and exploit for feedback\\noverhead reduction, given the separate channel estimation difficulty. To\\naddress this challenge, this letter introduces an innovative deep\\nlearning-based framework tailored for cascaded channel feedback, ingeniously\\ncapturing the intrinsic time variation in the cascaded channel. When an entire\\ncascaded channel has been sent to the BS, this framework advocates the feedback\\nof an efficient representation of this variation within a subsequent period\\nthrough an extraction-compression scheme. This scheme involves RIS unit-grained\\nchannel variation extraction, followed by autoencoder-based deep compression to\\nenhance compactness. Numerical simulations confirm that this feedback framework\\nsignificantly reduces both the feedback and computational burdens.\",\"PeriodicalId\":501034,\"journal\":{\"name\":\"arXiv - EE - Signal Processing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - EE - Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2409.08149\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - EE - Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.08149","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

在可重构智能表面(RIS)辅助通信系统领域,基站(BS)和用户设备(UE)之间的连接由一个级联信道构成,合并了 BS-RIS 和 RIS-UE 信道。由于 BS 和 RIS 的固定位置以及 UE 的移动性,这两个信道通常表现出不同的时变特性,鉴于单独的信道估计困难,要识别和利用这些特性来减少反馈开销具有挑战性。为解决这一难题,本文介绍了一种基于深度学习的创新框架,该框架专为级联信道反馈量身定制,巧妙地捕捉了级联信道的内在时间变化。当整个级联信道被发送到 BS 时,该框架主张通过提取-压缩方案在随后的时间段内反馈这种变化的有效表示。该方案包括 RIS 单位粒度信道变化提取,然后是基于自动编码器的深度压缩,以提高压缩率。数值模拟证实,这种反馈框架显著减少了反馈和计算负担。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Efficient Deep Learning-based Cascaded Channel Feedback in RIS-Assisted Communications
In the realm of reconfigurable intelligent surface (RIS)-assisted communication systems, the connection between a base station (BS) and user equipment (UE) is formed by a cascaded channel, merging the BS-RIS and RIS-UE channels. Due to the fixed positioning of the BS and RIS and the mobility of UE, these two channels generally exhibit different time-varying characteristics, which are challenging to identify and exploit for feedback overhead reduction, given the separate channel estimation difficulty. To address this challenge, this letter introduces an innovative deep learning-based framework tailored for cascaded channel feedback, ingeniously capturing the intrinsic time variation in the cascaded channel. When an entire cascaded channel has been sent to the BS, this framework advocates the feedback of an efficient representation of this variation within a subsequent period through an extraction-compression scheme. This scheme involves RIS unit-grained channel variation extraction, followed by autoencoder-based deep compression to enhance compactness. Numerical simulations confirm that this feedback framework significantly reduces both the feedback and computational burdens.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信