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

arXiv: https://arxiv.org/abs/2205.03636 · HTML: https://mlsanity.com/paper/2205.03636/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 17 references that are in the map.
- First posted on arXiv 7 May 2022.

## Builds on (most cited first, 7 of 7)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Towards Smart and Reconfigurable Environment: Intelligent Reflecting Surface Aided Wireless Network](https://mlsanity.com/paper/1905.00152/) (2019)
- [Intelligent Reflecting Surface: Practical Phase Shift Model and Beamforming Optimization](https://mlsanity.com/paper/1907.06002/) (2019)
- [RIS-Aided Wireless Communications: Prototyping, Adaptive Beamforming, and Indoor/Outdoor Field Trials](https://mlsanity.com/paper/2103.00534/) (2021)
- [Prospective Multiple Antenna Technologies for Beyond 5G](https://mlsanity.com/paper/1910.00092/) (2019)
- [Learning-Based Adaptive IRS Control with Limited Feedback Codebooks](https://mlsanity.com/paper/2112.01874/) (2021)
- [Angle-Dependent Phase Shifter Model for Reconfigurable Intelligent Surfaces: Does the Angle-Reciprocity Hold?](https://mlsanity.com/paper/2005.13529/) (2020)

## Abstract (arXiv)

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.