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|Title:||Cost-efficient RIS-aided channel estimation via rank-one matrix factorization||Authors:||Zhang, Wei
Tay, Wee Peng
|Keywords:||Engineering::Electrical and electronic engineering::Wireless communication systems||Issue Date:||2021||Source:||Zhang, W. & Tay, W. P. (2021). Cost-efficient RIS-aided channel estimation via rank-one matrix factorization. IEEE Wireless Communications Letters, 10(11), 2562-2566. https://dx.doi.org/10.1109/LWC.2021.3107547||Project:||A19D6a0053||Journal:||IEEE Wireless Communications Letters||Abstract:||A reconfigurable intelligent surface (RIS) consists of massive meta elements, which can improve the performance of future wireless communication systems. Existing RIS-aided channel estimation methods try to estimate the cascaded channel directly, incurring high computational and training overhead especially when the number of elements of RIS is extremely large. In this paper, we propose a cost-efficient channel estimation method via rank-one matrix factorization (MF). Specifically, if the RIS is employed near base station (BS), it is found that the RIS-aided channel can be factorized into a product of low-dimensional matrices. To estimate these factorized matrices, we propose alternating minimization and gradient descent approaches to obtain the near optimal solutions. Compared to directly estimating the cascaded channel, the proposed MF method reduces training overhead substantially. Finally, the numerical simulations show the effectiveness of the proposed MF method.||URI:||https://hdl.handle.net/10356/152711||ISSN:||2162-2337||DOI:||10.1109/LWC.2021.3107547||Rights:||© 2021 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. The published version is available at: https://doi.org/10.1109/LWC.2021.3107547.||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Journal Articles|
Updated on May 24, 2022
Updated on May 24, 2022
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