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We consider a downlink multi-user scenario and investigate the use of reconfigurable intelligent surfaces (RISs) to maximize the dirty-paper-coding (DPC) sum rate of the RIS-assisted broadcast channel. Different from prior works, which maximize the rate achievable by linear precoders, we assume a capacity-achieving DPC scheme is employed at the transmitter and optimize the transmit covariances and RIS reflection coefficients to directly maximize the sum capacity of the broadcast channel. We propose an optimization algorithm that iteratively alternates between optimizing the transmit covariances using convex optimization and the RIS reflection coefficients using Riemannian manifold optimization. Our results show that the proposed technique can be used to effectively improve the sum capacity in a variety of scenarios compared to benchmark schemes.


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Maximizing dirty-paper coding rate of RIS-assisted multi-user MIMO broadcast channels

Show Author's information Mohamed A. Elmossallamy1Radwa Sultan3( )Karim G. Seddik4Geoffery Ye Li5Zhu Han2
Qualcomm Inc., Boxborough, MA 01719, USA
Electrical and Computer Engineering Department and the Computer Science Department, University of Houston, Houston, TX 77004, USA
Electrical and Computer Engineering Department, Manhattan College, Riverdale, NY 10471, USA
Electronics and Communications Engineering Department, American University in Cairo, New Cairo 11835, Egypt
Department of Electrical and Electronic Engineering, Imperial College London, London, SW7 2AZ, UK

Abstract

We consider a downlink multi-user scenario and investigate the use of reconfigurable intelligent surfaces (RISs) to maximize the dirty-paper-coding (DPC) sum rate of the RIS-assisted broadcast channel. Different from prior works, which maximize the rate achievable by linear precoders, we assume a capacity-achieving DPC scheme is employed at the transmitter and optimize the transmit covariances and RIS reflection coefficients to directly maximize the sum capacity of the broadcast channel. We propose an optimization algorithm that iteratively alternates between optimizing the transmit covariances using convex optimization and the RIS reflection coefficients using Riemannian manifold optimization. Our results show that the proposed technique can be used to effectively improve the sum capacity in a variety of scenarios compared to benchmark schemes.

Keywords: broadcast channels, dirty-paper coding, multiple-input-multiple-output (MIMO), reconfigurable intelligent surfaces

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Published: 30 March 2022
Issue date: March 2022

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This work was partially supported by the National Science Foundation (Nos. CNS-2107216 and CNS-2128368).

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This work is available under the CC BY-NC-ND 3.0 IGO license: https://creativecommons.org/licenses/by-nc-nd/3.0/igo/

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