Design of a Single-User RIS-Aided MISO System Based on Statistical Channel Knowledge

TL;DR

This paper proposes a RIS-assisted MISO communication system that leverages statistical channel knowledge to reduce training overhead and avoid frequent RIS reconfiguration, enhancing practicality for future networks.

Contribution

It introduces algorithms that operate without requiring perfect CSI, significantly lowering computational complexity and training requirements in RIS-aided systems.

Findings

Algorithms that do not need CSI estimation

Reduced RIS reconfiguration frequency

Improved spectral and energy efficiency

Abstract

Reconfigurable intelligent surface (RIS) is considered a prospective technology for beyond fifth-generation (5G) networks to improve the spectral and energy efficiency at a low cost. Prior works on the RIS mainly rely on perfect channel state information (CSI), which imposes a huge computational complexity. This work considers a single-user RIS-assisted communication system, where the second-order statistical knowledge of the channels is exploited to reduce the training overhead. We present algorithms that do not require estimation of the CSI and reconfiguration of the RIS in every channel coherence interval, which constitutes one of the most critical practical issues in an RIS-aided system.