Learning-Based Adaptive IRS Control with Limited Feedback Codebooks

TL;DR

This paper introduces a novel adaptive IRS control method using limited feedback codebooks, leveraging deep learning and random adjacency techniques to improve data rates in complex, time-varying wireless environments.

Contribution

It proposes a new adaptive codebook-based feedback protocol and two innovative solutions, RA and DPIC, for IRS control without explicit channel estimation.

Findings

Significant improvement in data rate performance.

Effective IRS control with low-rate feedback.

Robustness in multi-path, time-varying channels.

Abstract

Intelligent reflecting surfaces (IRS) consist of configurable meta-atoms, which can change the wireless propagation environment through design of their reflection coefficients. We consider a practical setting where (i) the IRS reflection coefficients are achieved 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 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 applicable in this setting due to the difficulty of channel estimation and the low feedback rate. Therefore, we develop a novel adaptive codebook-based limited feedback protocol where only a codeword index is transferred to the IRS. We propose two solutions for adaptive codebook design: random adjacency (RA) and deep neural network policy-based IRS control (DPIC), both of which only require the end-to-end compound channels. We further develop several augmented schemes based on the RA and DPIC. Numerical evaluations show that the data rate and average data rate over one coherence time are improved substantially by our schemes.