Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/29473
Title: Joint MIMO Transceiver and Reflector Design for Reconfigurable Intelligent Surface-Assisted Communication
Authors: Zhao, Y
Xu, J
Xu, W
Wang, K
Ye, X
Yuen, C
You, X
Keywords: reconfigurable intelligent surface (RIS);transceiver optimization;weighted minimum mean squared error (WMMSE);semi-definite relaxation (SDR);successive closed form (SCF);alternating optimization;Karush-Kuhn- Tucker (KKT) point
Issue Date: 11-Jun-2024
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Citation: Zhao, Y. et al. (2024) 'Joint MIMO Transceiver and Reflector Design for Reconfigurable Intelligent Surface-Assisted Communication', IEEE Transactions on Vehicular Technology, 0 (early access), pp. 1 - 15. doi: 10.1109/TVT.2024.3406199.
Abstract: In this paper, we consider a reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output communication system with multiple antennas at both the base station (BS) and the user. We plan to maximize the achievable rate through jointly optimizing the transmit precoding matrix, the receive combining matrix, and the RIS reflection matrix under the constraints of the transmit power at the BS and the unit-modulus reflection at the RIS. Regarding the non-trivial problem form, we initially reformulate it into an considerable problem to make it tractable by utilizing the relationship between the achievable rate and the weighted minimum mean squared error. Next, the transmit precoding matrix, the receive combining matrix, and the RIS reflection matrix are alternately optimized. In particular, the optimal transmit precoding matrix and receive combining matrix are obtained in closed forms. Furthermore, a pair of computationally efficient methods are proposed for the RIS reflection matrix, namely the semi-definite relaxation (SDR) method and the successive closed form (SCF) method. We theoretically prove that both methods are ensured to converge, and the SCF-based algorithm is able to converges to a Karush-Kuhn-Tucker point of the problem.
Description: A preprint version of this article is available at: arXiv:2405.17329v1 [cs.IT], https://arxiv.org/abs/2405.17329 . It may not have been certified by peer review. Please consult the peer reviewed version published by IEEE at https://doi.org/10.1109/TVT.2024.3406199 .
URI: https://bura.brunel.ac.uk/handle/2438/29473
DOI: https://doi.org/10.1109/TVT.2024.3406199
ISSN: 0018-9545
Other Identifiers: ORCiD: Yaqiong Zhao https://orcid.org/0000-0002-0753-617X
ORCiD: Jindan Xu https://orcid.org/0000-0002-4090-6478
ORCiD: Wei Xu https://orcid.org/0000-0001-9341-8382
ORCiD: Kezhi Wang https://orcid.org/0000-0001-8602-0800
ORCiD: Chau Yuen https://orcid.org/0000-0002-9307-2120
ORCiD: Xiaohu You https://orcid.org/0000-0002-0809-8511
Appears in Collections:Dept of Computer Science Research Papers

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