# Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2002.10072 · HTML: https://mlsanity.com/paper/2002.10072/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2020.

## Built on by (most cited first, 30 of 39)

- [Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications](https://mlsanity.com/paper/2111.12444/) (2021)
- [Generative AI Agents with Large Language Model for Satellite Networks via a Mixture of Experts Transmission](https://mlsanity.com/paper/2404.09134/) (2024)
- [Deep-Reinforcement-Learning-Based AoI-Aware Resource Allocation for RIS-Aided IoV Networks](https://mlsanity.com/paper/2406.11245/) (2024)
- [Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2205.03793/) (2022)
- [Multi-hop RIS-Empowered Terahertz Communications: A DRL-based Hybrid Beamforming Design](https://mlsanity.com/paper/2101.09137/) (2021)
- [A Robust Deep Learning-Based Beamforming Design for RIS-assisted Multiuser MISO Communications with Practical Constraints](https://mlsanity.com/paper/2111.06555/) (2021)
- [Decentralized Federated Reinforcement Learning for User-Centric Dynamic TFDD Control](https://mlsanity.com/paper/2211.02296/) (2022)
- [Digital Twin-Aided Learning for Managing Reconfigurable Intelligent Surface-Assisted, Uplink, User-Centric Cell-Free Systems](https://mlsanity.com/paper/2302.05073/) (2023)
- [Ultra-Reliable Indoor Millimeter Wave Communications using Multiple Artificial Intelligence-Powered Intelligent Surfaces](https://mlsanity.com/paper/2104.00075/) (2021)
- [On the Robustness of Deep Reinforcement Learning in IRS-Aided Wireless Communications Systems](https://mlsanity.com/paper/2107.08293/) (2021)
- [RIS-assisted UAV Communications for IoT with Wireless Power Transfer Using Deep Reinforcement Learning](https://mlsanity.com/paper/2108.02889/) (2021)
- [Reconfigurable Intelligent Surface Empowered Over-the-Air Federated Edge Learning](https://mlsanity.com/paper/2109.02353/) (2021)
- [Deep Learning for Multi-User MIMO Systems: Joint Design of Pilot, Limited Feedback, and Precoding](https://mlsanity.com/paper/2209.10332/) (2022)
- [DNN-Based Precoding in RIS-Aided mmWave MIMO Systems With Practical Phase Shift](https://mlsanity.com/paper/2507.02824/) (2025)
- [A Multi-Modal Foundational Model for Wireless Communication and Sensing](https://mlsanity.com/paper/2602.04016/) (2026)
- [Deep Reinforcement Learning Powered IRS-Assisted Downlink NOMA](https://mlsanity.com/paper/2104.01414/) (2021)
- [Energy-Efficient Design for a NOMA assisted STAR-RIS Network with Deep Reinforcement Learning](https://mlsanity.com/paper/2111.15464/) (2021)
- [Optimization for Master-UAV-powered Auxiliary-Aerial-IRS-assisted IoT Networks: An Option-based Multi-agent Hierarchical Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2112.10630/) (2021)
- [Collaborative Intelligent Reflecting Surface Networks with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2203.14152/) (2022)
- [Deep Reinforcement Learning Based on Location-Aware Imitation Environment for RIS-Aided mmWave MIMO Systems](https://mlsanity.com/paper/2205.08788/) (2022)
- [Deep Reinforcement Learning Based Joint Downlink Beamforming and RIS Configuration in RIS-aided MU-MISO Systems Under Hardware Impairments and Imperfect CSI](https://mlsanity.com/paper/2211.09702/) (2022)
- [A Hierarchical DRL Approach for Resource Optimization in Multi-RIS Multi-Operator Networks](https://mlsanity.com/paper/2410.12320/) (2024)
- [RIS-Assisted Downlink Pinching-Antenna Systems: GNN-Enabled Optimization Approaches](https://mlsanity.com/paper/2511.20305/) (2025)
- [Deep Reinforcement Learning for Intelligent Reflecting Surface-assisted D2D Communications](https://mlsanity.com/paper/2108.02892/) (2021)
- [Covert Communications via Adversarial Machine Learning and Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2112.11414/) (2021)
- [Phase Shift Design in RIS Empowered Wireless Networks: From Optimization to AI-Based Methods](https://mlsanity.com/paper/2204.13372/) (2022)
- [Deep Reinforcement Learning for IRS Phase Shift Design in Spatiotemporally Correlated Environments](https://mlsanity.com/paper/2211.09726/) (2022)
- [Energy Efficiency Maximization in IRS-Aided Cell-Free Massive MIMO System](https://mlsanity.com/paper/2212.12744/) (2022)
- [A Novel Deep Reinforcement Learning-based Approach for Enhancing Spectral Efficiency of IRS-assisted Wireless Systems](https://mlsanity.com/paper/2302.14706/) (2023)
- [Active RIS-aided EH-NOMA Networks: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2304.12184/) (2023)

## Abstract (arXiv)

Recently, the reconfigurable intelligent surface (RIS), benefited from the breakthrough on the fabrication of programmable meta-material, has been speculated as one of the key enabling technologies for the future six generation (6G) wireless communication systems scaled up beyond massive multiple input multiple output (Massive-MIMO) technology to achieve smart radio environments. Employed as reflecting arrays, RIS is able to assist MIMO transmissions without the need of radio frequency chains resulting in considerable reduction in power consumption. In this paper, we investigate the joint design of transmit beamforming matrix at the base station and the phase shift matrix at the RIS, by leveraging recent advances in deep reinforcement learning (DRL). We first develop a DRL based algorithm, in which the joint design is obtained through trial-and-error interactions with the environment by observing predefined rewards, in the context of continuous state and action. Unlike the most reported works utilizing the alternating optimization techniques to alternatively obtain the transmit beamforming and phase shifts, the proposed DRL based algorithm obtains the joint design simultaneously as the output of the DRL neural network. Simulation results show that the proposed algorithm is not only able to learn from the environment and gradually improve its behavior, but also obtains the comparable performance compared with two state-of-the-art benchmarks. It is also observed that, appropriate neural network parameter settings will improve significantly the performance and convergence rate of the proposed algorithm.