Ergodic Rate Analysis of Reconfigurable Intelligent Surface-Aided Massive MIMO Systems with ZF Detectors

Zhi, Kangda, Pan, Cunhua, Ren, Hong and Wang, Kezhi (2022) Ergodic Rate Analysis of Reconfigurable Intelligent Surface-Aided Massive MIMO Systems with ZF Detectors. IEEE Communications Letters, 26 (2). pp. 264-268. ISSN 1089-7798

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Pan Ergodic Rate Analysis of Reconfigurable Intelligent Surface-Aided Massive MIMO Systems with ZF Detectors 2021 Accepted.pdf - Accepted Version

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Official URL: https://doi.org/10.1109/LCOMM.2021.3128904

Abstract

This letter investigates the reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) systems with a two-timescale design. First, the zero-forcing (ZF) detector is applied at the base station (BS) based on instantaneous aggregated channel state information (CSI), which is the superposition of the direct channel and the cascaded user-RIS-BS channel. Then, by leveraging the channel statistical property, we derive the closed-form ergodic achievable rate expression. Using a gradient ascent method, we design the RIS passive beamforming relying only on the long-term statistical CSI. We prove that the ergodic rate scales on the order of O(log2 (MN)), where M and N denote the number of BS antennas and RIS elements, respectively. We also prove the striking superiority of the considered RIS-aided system with ZF detectors over the RIS-free systems and RIS-aided systems with maximum-ratio combining (MRC).

Item Type: Article
Additional Information: Funding information: This work was supported in part by the National Key Research and Development Project under Grant 2019YFE0123600, National Natural Science Foundation of China (62101128) and Basic Research Project of Jiangsu Provincial Department of Science and Technology (BK20210205).
Uncontrolled Keywords: Reconfigurable intelligent surface (RIS), intelligent reflecting surface (IRS), statistical CSI, massive MIMO, ZF
Subjects: H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: John Coen
Date Deposited: 11 Feb 2022 11:47
Last Modified: 28 Mar 2022 15:00
URI: http://nrl.northumbria.ac.uk/id/eprint/48443

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