# A DRL-based Reflection Enhancement Method for RIS-assisted Multi-receiver Communications

arXiv: https://arxiv.org/abs/2309.05343 · HTML: https://mlsanity.com/paper/2309.05343/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 11 Sep 2023.

## Builds on (most cited first, 6 of 6)

- [Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learning](https://mlsanity.com/paper/2002.10072/) (2020)
- [RIS-Aided Wireless Communications: Prototyping, Adaptive Beamforming, and Indoor/Outdoor Field Trials](https://mlsanity.com/paper/2103.00534/) (2021)
- [Multi-hop RIS-Empowered Terahertz Communications: A DRL-based Hybrid Beamforming Design](https://mlsanity.com/paper/2101.09137/) (2021)
- [Two-Timescale Channel Estimation for Reconfigurable Intelligent Surface Aided Wireless Communications](https://mlsanity.com/paper/1912.07990/) (2019)
- [Transmit Power Control for Indoor Small Cells: A Method Based on Federated Reinforcement Learning](https://mlsanity.com/paper/2209.13536/) (2022)
- [On the Enabling of Multi-receiver Communications with Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2106.06789/) (2021)

## Abstract (arXiv)

In reconfigurable intelligent surface (RIS)-assisted wireless communication systems, the pointing accuracy and intensity of reflections depend crucially on the 'profile,' representing the amplitude/phase state information of all elements in a RIS array. The superposition of multiple single-reflection profiles enables multi-reflection for distributed users. However, the optimization challenges from periodic element arrangements in single-reflection and multi-reflection profiles are understudied. The combination of periodical single-reflection profiles leads to amplitude/phase counteractions, affecting the performance of each reflection beam. This paper focuses on a dual-reflection optimization scenario and investigates the far-field performance deterioration caused by the misalignment of overlapped profiles. To address this issue, we introduce a novel deep reinforcement learning (DRL)-based optimization method. Comparative experiments against random and exhaustive searches demonstrate that our proposed DRL method outperforms both alternatives, achieving the shortest optimization time. Remarkably, our approach achieves a 1.2 dB gain in the reflection peak gain and a broader beam without any hardware modifications.