Reconfigurable Intelligent Surface-Aided Indoor Communication With Neural Beam Alignment

Abstract

Reconfigurable intelligent surface (RIS) is a promising technology for realizing cost-and-energy efficient wideband communication in future wireless communications. We construct a prototype of the RIS communication which realizes a new radiofrequency (RF) chain-free architecture. This experimental prototype using RIS is tested at 5.8 GHz and achieves a 20 MHz data transmission efficiently. We also propose a deep neural network-based beam alignment for the RIS communication, which validated by experiment tests with the RIS system consisting of 16×16 meta-material reflecting elements in indoor environment. The provided results show that the proposed method can achieve beam alignment in experiment tests. It improves the performance of the RIS communication with reduced overhead.