Rapid CNN-Assisted Iterative RIS Element Configuration

TL;DR

This paper introduces a CNN-based method to quickly optimize RIS element configurations in wireless systems, significantly reducing the number of steps needed with minimal performance loss.

Contribution

The paper proposes a novel CNN-assisted iterative algorithm that accelerates RIS configuration without extensive feedback, improving efficiency over traditional methods.

Findings

Reduces configuration steps from 3200 to 160 for a 40x40 RIS.

Achieves rapid configuration with only slight performance degradation.

Demonstrates effectiveness through simulation results.

Abstract

Reconfigurable Intelligent Surfaces (RISs) are becoming one of the fundamental building blocks of next-generation wireless communication systems. To that end, RIS phase configuration optimization is an important issue, where finding the most suitable configuration becomes a challenging and resource-consuming task, especially as the number of RIS elements increases. Since exhaustive search is not practical, iterative algorithms are utilized to determine the RIS configuration by sequentially considering all RIS elements, where the best-performing phase shift configuration is obtained for each element. However, each configuration attempt requires receiver performance feedback, leading to higher delay and signaling overhead. Thus, in this paper, a convolutional neural network (CNN) based solution is formulated to rapidly find the phase configurations of the RIS elements. The simulation results for a RIS with $40\times40$ elements imply that the proposed algorithm reduces the number of steps dramatically e.g., from 3200 to 160 for the particular setup. Furthermore, such improvement in complexity is achieved with a slight degradation in performance.