# Deep Contextual Bandits for Orchestrating Multi-User MISO Systems with Multiple RISs

arXiv: https://arxiv.org/abs/2202.08194 · HTML: https://mlsanity.com/paper/2202.08194/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Feb 2022.

## Built on by (most cited first, 4 of 4)

- [Joint Active RIS Configuration and User Power Control for Localization: A Neuroevolution-Based Approach](https://mlsanity.com/paper/2510.13819/) (2025)
- [Lyapunov-Driven Deep Reinforcement Learning for Edge Inference Empowered by Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2305.10931/) (2023)
- [Fairness-Driven Optimization of RIS-Augmented 5G Networks for Seamless 3D UAV Connectivity Using DRL Algorithms](https://mlsanity.com/paper/2312.09420/) (2023)
- [Active Sensing for RIS-Aided Tracking and Power Control: A Hybrid Neuroevolution and Supervised Learning Approach](https://mlsanity.com/paper/2607.00056/) (2026)

## Abstract (arXiv)

The emergent technology of Reconfigurable Intelligent Surfaces (RISs) has the potential to transform wireless environments into controllable systems, through programmable propagation of information-bearing signals. Techniques stemming from the field of Deep Reinforcement Learning (DRL) have recently gained popularity in maximizing the sum-rate performance in multi-user communication systems empowered by RISs. Such approaches are commonly based on Markov Decision Processes (MDPs). In this paper, we instead investigate the sum-rate design problem under the scope of the Multi-Armed Bandits (MAB) setting, which is a relaxation of the MDP framework. Nevertheless, in many cases, the MAB formulation is more appropriate to the channel and system models under the assumptions typically made in the RIS literature. To this end, we propose a simpler DRL approach for orchestrating multiple metasurfaces in RIS-empowered multi-user Multiple-Input Single-Output (MISO) systems, which we numerically show to perform equally well with a state-of-the-art MDP-based approach, while being less demanding computationally.