Consider a communication system in which a single- antenna user equipment exchanges information with a multi- antenna base station via a reconfigurable intelligent surface (RIS) in the presence of spatially correlated channels and electromagnetic interference (EMI). To exploit the attractive advantages of RIS technology, accurate configuration of its reflecting elements is crucial. In this paper, we use statistical knowledge of channels and EMI to optimize the RIS elements for i) accurate channel estimation and ii) reliable data transmission. In both cases, our goal is to determine the RIS coefficients that minimize the mean square error, resulting in the formulation of two non-convex problems that share the same structure. To solve these two problems, we present an alternating optimization approach that reliably converges to a locally optimal solution. The incorporation of the diagonally scaled steepest descent algo- rithm, derived from Newton’s method, ensures fast convergence with manageable complexity. Numerical results demonstrate the effectiveness of the proposed method under various propagation conditions. Notably, it shows significant advantages over existing alternatives that depend on a suboptimal configuration of the RIS and are derived on the basis of different criteria.

MMSE Design of RIS-Aided Communications with Spatially-Correlated Channels and Electromagnetic Interference

WenXuan Long
;
Marco Moretti;Luca Sanguinetti;Rui Chen
2024-01-01

Abstract

Consider a communication system in which a single- antenna user equipment exchanges information with a multi- antenna base station via a reconfigurable intelligent surface (RIS) in the presence of spatially correlated channels and electromagnetic interference (EMI). To exploit the attractive advantages of RIS technology, accurate configuration of its reflecting elements is crucial. In this paper, we use statistical knowledge of channels and EMI to optimize the RIS elements for i) accurate channel estimation and ii) reliable data transmission. In both cases, our goal is to determine the RIS coefficients that minimize the mean square error, resulting in the formulation of two non-convex problems that share the same structure. To solve these two problems, we present an alternating optimization approach that reliably converges to a locally optimal solution. The incorporation of the diagonally scaled steepest descent algo- rithm, derived from Newton’s method, ensures fast convergence with manageable complexity. Numerical results demonstrate the effectiveness of the proposed method under various propagation conditions. Notably, it shows significant advantages over existing alternatives that depend on a suboptimal configuration of the RIS and are derived on the basis of different criteria.
2024
Long, Wenxuan; Moretti, Marco; Abrardo, Andrea; Sanguinetti, Luca; Chen, Rui
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1272847
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