Deep Reinforcement Learning-Based Adaptive IRS Control with Limited Feedback Codebooks

TL;DR

This paper introduces a deep reinforcement learning approach for adaptive IRS control with limited feedback, improving data rates in complex, time-varying wireless environments without relying on extensive channel estimation.

Contribution

It proposes a novel deep RL-based control protocol and two adaptive codebook design methods for IRS, addressing practical feedback and channel estimation challenges.

Findings

Significant data rate improvements over baseline methods.

Effective IRS control in multi-path, time-varying channels.

Reduced need for channel estimation and high-rate feedback.

Abstract

Intelligent reflecting surfaces (IRS) consist of configurable meta-atoms, which can alter the wireless propagation environment through design of their reflection coefficients. We consider adaptive IRS control in the practical setting where (i) the IRS reflection coefficients are attained by adjusting tunable elements embedded in the meta-atoms, (ii) the IRS reflection coefficients are affected by the incident angles of the incoming signals, (iii) the IRS is deployed in multi-path, time-varying channels, and (iv) the feedback link from the base station (BS) to the IRS has a low data rate. Conventional optimization-based IRS control protocols, which rely on channel estimation and conveying the optimized variables to the IRS, are not practical in this setting due to the difficulty of channel estimation and the low data rate of the feedback channel. To address these challenges, we develop a novel adaptive codebook-based limited feedback protocol to control the IRS. We propose two solutions for adaptive IRS codebook design: (i) random adjacency (RA), which utilizes correlations across the channel realizations, and (ii) deep neural network policy-based IRS control (DPIC), which is based on a deep reinforcement learning. Numerical evaluations show that the data rate and average data rate over one coherence time are improved substantially by the proposed schemes.