Indoor Signal Focusing with Deep Learning Designed Reconfigurable Intelligent Surfaces

TL;DR

This paper introduces a deep learning approach to efficiently configure Reconfigurable Intelligent Surfaces in indoor environments, enabling focused signal reflection and improved throughput without complex wired controls.

Contribution

It presents a novel DNN-based method for online RIS configuration using coordinate fingerprints, reducing complexity and enhancing indoor wireless signal focusing.

Findings

DNN effectively maps user location to RIS configuration.

Simulation shows increased throughput at target locations.

Method simplifies RIS control in indoor environments.

Abstract

Reconfigurable Intelligent Surfaces (RISs) comprised of tunable unit elements have been recently considered in indoor communication environments for focusing signal reflections to intended user locations. However, the current proofs of concept require complex operations for the RIS configuration, which are mainly realized via wired control connections. In this paper, we present a deep learning method for efficient online wireless configuration of RISs when deployed in indoor communication environments. According to the proposed method, a database of coordinate fingerprints is implemented during an offline training phase. This fingerprinting database is used to train the weights and bias of a properly designed Deep Neural Network (DNN), whose role is to unveil the mapping between the measured coordinate information at a user location and the configuration of the RIS's unit cells that maximizes this user's received signal strength. During the online phase of the presented method, the trained DNN is fed with the measured position information at the target user to output the optimal phase configurations of the RIS for signal power focusing on this intended location. Our realistic simulation results using ray tracing on a three dimensional indoor environment demonstrate that the proposed DNN-based configuration method exhibits its merits for all considered cases, and effectively increases the achievable throughput at the target user location.