Intelligent Spectrum Learning for Wireless Networks with Reconfigurable Intelligent Surfaces

TL;DR

This paper proposes an intelligent spectrum learning method using deep neural networks to improve the signal-to-interference-plus-noise ratio in wireless networks with reconfigurable intelligent surfaces, enhancing communication reliability.

Contribution

It introduces a novel deep learning-based spectrum inference technique and a distributed control algorithm for RIS configuration to mitigate interference effects.

Findings

Deep learning effectively infers interfering signals at RIS.

The proposed method improves SINR in simulated environments.

RIS configuration adapts dynamically for optimal performance.

Abstract

Reconfigurable intelligent surface (RIS) has become a promising technology for enhancing the reliability of wireless communications, which is capable of reflecting the desired signals through appropriate phase shifts. However, the intended signals that impinge upon an RIS are often mixed with interfering signals, which are usually dynamic and unknown. In particular, the received signal-to-interference-plus-noise ratio (SINR) may be degraded by the signals reflected from the RISs that originate from non-intended users. To tackle this issue, we introduce the concept of intelligent spectrum learning (ISL), which uses an appropriately trained convolutional neural network (CNN) at the RIS controller to help the RISs infer the interfering signals directly from the incident signals. By capitalizing on the ISL, a distributed control algorithm is proposed to maximize the received SINR by dynamically configuring the active/inactive binary status of the RIS elements. Simulation results validate the performance improvement offered by deep learning and demonstrate the superiority of the proposed ISL-aided approach.