A Deep Learning Based Modeling of Reconfigurable Intelligent Surface Assisted Wireless Communications for Phase Shift Configuration

Abstract

Reconfigurable Intelligent Surface (RIS) has emerged as a promising technology in wireless networks to achieve high spectrum and energy efficiency. RIS typically comprises a large number of low-cost nearly passive elements that can smartly interact with the impinging electromagnetic waves for performance enhancement. However, optimally configuring massive number of RIS elements remains a challenge. In this article, we present a machine learning (ML) based modeling approach that learns the interactions between the phase shifts of the RIS elements and receiver (Rx) location attributes and uses them to predict the achievable rate directly without using channel state information (CSI) or received pilots. Once learned, our model can be used to predict optimal RIS configuration for any new receiver locations in the same wireless network. We leverage deep learning (DL) techniques to build our model and study its performance and robustness. Simulation results demonstrate that the proposed DL model can recommend near-optimal RIS configurations for test receiver locations which achieved close to an upper bound performance that assumes perfect channel knowledge. Our DL model was trained using less than 2% of the total receiver locations. This promising result represents great potential in developing a practical solution for the optimal phase shifts of RIS elements without requesting CSI from the wireless network infrastructure.