# Deep Learning-based Phase Reconfiguration for Intelligent Reflecting Surfaces

arXiv: https://arxiv.org/abs/2009.13988 · HTML: https://mlsanity.com/paper/2009.13988/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2020.

## Built on by (most cited first, 6 of 6)

- [Federated Distributionally Robust Optimization for Phase Configuration of RISs](https://mlsanity.com/paper/2108.09026/) (2021)
- [Channel Estimation and Hybrid Architectures for RIS-Assisted Communications](https://mlsanity.com/paper/2104.07115/) (2021)
- [Robust Reconfigurable Intelligent Surfaces via Invariant Risk and Causal Representations](https://mlsanity.com/paper/2105.01771/) (2021)
- [Beyond 5G RIS mmWave Systems: Where Communication and Localization Meet](https://mlsanity.com/paper/2109.07729/) (2021)
- [Federated Learning Games for Reconfigurable Intelligent Surfaces via Causal Representations](https://mlsanity.com/paper/2306.01306/) (2023)
- [Sustainable LSTM-Based Precoding for RIS-Aided mmWave MIMO Systems with Implicit CSI](https://mlsanity.com/paper/2509.12658/) (2025)

## Abstract (arXiv)

Intelligent reflecting surfaces (IRSs), consisting of reconfigurable metamaterials, have recently attracted attention as a promising cost-effective technology that can bring new features to wireless communications. These surfaces can be used to partially control the propagation environment and can potentially provide a power gain that is proportional to the square of the number of IRS elements when configured in a proper way. However, the configuration of the local phase matrix at the IRSs can be quite a challenging task since they are purposely designed to not have any active components, therefore, they are not able to process any pilot signal. In addition, a large number of elements at the IRS may create a huge training overhead. In this paper, we present a deep learning (DL) approach for phase reconfiguration at an IRS in order to learn and make use of the local propagation environment. The proposed method uses the received pilot signals reflected through the IRS to train the deep feedforward network. The performance of the proposed approach is evaluated and the numerical results are presented.