A Survey of Deep Learning Architectures for Intelligent Reflecting Surfaces

TL;DR

This survey reviews how deep learning techniques are applied to optimize intelligent reflecting surfaces in 6G wireless systems, focusing on signal detection, channel estimation, and beamforming.

Contribution

It provides a comprehensive overview of DL architectures used for IRS-assisted wireless systems, highlighting recent advances and challenges in the field.

Findings

DL improves IRS signal detection accuracy

DL enables efficient channel estimation

DL facilitates adaptive beamforming in IRS systems

Abstract

Intelligent reflecting surfaces (IRSs) have recently received significant attention for 6G wireless communications as they enable the control of the wireless propagation environment. The use of IRS also provides reducing the hardware complexity, physical size, weight as well as cost of conventional large antenna arrays. However, deployment of the IRS entails dealing with multiple channel links between the base station (BS) and the users. Further, the BS and IRS beamformers require a joint design, wherein the IRS elements must be rapidly reconfigured. Data-driven techniques, such as deep learning (DL), are critical in addressing these challenges. The lower computation time and model-free nature of DL make it robust against data imperfections and environmental changes. At the physical layer, DL has been shown to be effective for IRS signal detection, channel estimation, and active/passive beamforming using architectures such as supervised, unsupervised, and reinforcement learning. This article provides a synopsis of these techniques for designing DL-based IRS-assisted wireless systems.