{"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}
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.