# DRL-Based Sidelobe Suppression for Multi-focus Reconfigurable Intelligent Surface

arXiv: https://arxiv.org/abs/2312.15565 · HTML: https://mlsanity.com/paper/2312.15565/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Dec 2023.

## Built on by (most cited first, 1 of 1)

- [A Heuristic-Integrated DRL Approach for Phase Optimization in Large-Scale RISs](https://mlsanity.com/paper/2505.04401/) (2025)

## Abstract (arXiv)

Reconfigurable intelligent surface (RIS) technology is receiving significant attention as a key enabling technology for 6G communications, with much attention given to coverage infill and wireless power transfer. However, relatively little attention has been paid to the radiation pattern fidelity, for example, sidelobe suppression. When considering multi-user coverage infill, direct beam pattern synthesis using superposition can result in undesirable sidelobe levels. To address this issue, this paper introduces and applies deep reinforcement learning (DRL) as a means to optimize the far-field pattern, offering a 4dB reduction in the unwanted sidelobe levels, thereby improving energy efficiency and decreasing the co-channel interference levels.