Making Intelligent Reflecting Surfaces More Intelligent: A Roadmap Through Reservoir Computing

TL;DR

This paper proposes a neural network-based reservoir computing framework for intelligent reflecting surfaces in wireless communications, leveraging RF impairments and channel randomness to improve system robustness and performance.

Contribution

It introduces a novel RC-based signal processing approach for IRS systems that models RF impairments and channel effects as part of the reservoir dynamics.

Findings

Reservoir computing effectively models RF impairments and channel randomness.

The framework enhances IRS performance by exploiting nonlinearity and chaos.

Practical issues like channel estimation and beamforming are addressed.

Abstract

This article introduces a neural network-based signal processing framework for intelligent reflecting surface (IRS) aided wireless communications systems. By modeling radio-frequency (RF) impairments inside the "meta-atoms" of IRS (including nonlinearity and memory effects), we present an approach that generalizes the entire IRS-aided system as a reservoir computing (RC) system, an efficient recurrent neural network (RNN) operating in a state near the "edge of chaos". This framework enables us to take advantage of the nonlinearity of this "fabricated" wireless environment to overcome link degradation due to model mismatch. Accordingly, the randomness of the wireless channel and RF imperfections are naturally embedded into the RC framework, enabling the internal RC dynamics lying on the edge of chaos. Furthermore, several practical issues, such as channel state information acquisition, passive beamforming design, and physical layer reference signal design, are discussed.