# Deep Reinforcement Learning Based Intelligent Reflecting Surface for Secure Wireless Communications

arXiv: https://arxiv.org/abs/2002.12271 · HTML: https://mlsanity.com/paper/2002.12271/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2020.

## Built on by (most cited first, 22 of 22)

- [Regret Analysis of Policy Gradient Algorithm for Infinite Horizon Average Reward Markov Decision Processes](https://mlsanity.com/paper/2309.01922/) (2023)
- [Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2205.03793/) (2022)
- [Global Convergence for Average Reward Constrained MDPs with Primal-Dual Actor Critic Algorithm](https://mlsanity.com/paper/2505.15138/) (2025)
- [Cooperative Multi-Agent Reinforcement Learning Based Distributed Dynamic Spectrum Access in Cognitive Radio Networks](https://mlsanity.com/paper/2106.09274/) (2021)
- [A Survey on Trustworthy Edge Intelligence: From Security and Reliability To Transparency and Sustainability](https://mlsanity.com/paper/2310.17944/) (2023)
- [Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semantics](https://mlsanity.com/paper/2301.00912/) (2023)
- [A Robust Deep Learning-Based Beamforming Design for RIS-assisted Multiuser MISO Communications with Practical Constraints](https://mlsanity.com/paper/2111.06555/) (2021)
- [RIS-assisted UAV Communications for IoT with Wireless Power Transfer Using Deep Reinforcement Learning](https://mlsanity.com/paper/2108.02889/) (2021)
- [Video-VoT-R1: An efficient video inference model integrating image packing and AoE architecture](https://mlsanity.com/paper/2503.15807/) (2025)
- [Deep Reinforcement Learning Powered IRS-Assisted Downlink NOMA](https://mlsanity.com/paper/2104.01414/) (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)
- [Hybrid-Task Meta-Learning: A GNN Approach for Scalable and Transferable Bandwidth Allocation](https://mlsanity.com/paper/2401.10253/) (2023)
- [A Hierarchical DRL Approach for Resource Optimization in Multi-RIS Multi-Operator Networks](https://mlsanity.com/paper/2410.12320/) (2024)
- [Phase Shift Design in RIS Empowered Wireless Networks: From Optimization to AI-Based Methods](https://mlsanity.com/paper/2204.13372/) (2022)
- [Machine Learning for Metasurfaces Design and Their Applications](https://mlsanity.com/paper/2211.01296/) (2022)
- [A Novel Deep Reinforcement Learning-based Approach for Enhancing Spectral Efficiency of IRS-assisted Wireless Systems](https://mlsanity.com/paper/2302.14706/) (2023)
- [Progression Cognition Reinforcement Learning with Prioritized Experience for Multi-Vehicle Pursuit](https://mlsanity.com/paper/2306.05016/) (2023)
- [Science-Informed Design of Deep Learning With Applications to Wireless Systems: A Tutorial](https://mlsanity.com/paper/2407.07742/) (2024)
- [Quantum Machine Learning for Secure Cooperative Multi-Layer Edge AI with Proportional Fairness](https://mlsanity.com/paper/2507.15145/) (2025)
- [A 3GPP-Compliant Benchmark Dataset for RIS-Aided Beyond 5G Networks](https://mlsanity.com/paper/2609.39058/) (2026)

## Abstract (arXiv)

In this paper, we study an intelligent reflecting surface (IRS)-aided wireless secure communication system for physical layer security, where an IRS is deployed to adjust its surface reflecting elements to guarantee secure communication of multiple legitimate users in the presence of multiple eavesdroppers. Aiming to improve the system secrecy rate, a design problem for jointly optimizing the base station (BS)'s beamforming and the IRS's reflecting beamforming is formulated given the different quality of service (QoS) requirements and time-varying channel condition. As the system is highly dynamic and complex, and it is challenging to address the non-convex optimization problem, a novel deep reinforcement learning (DRL)-based secure beamforming approach is firstly proposed to achieve the optimal beamforming policy against eavesdroppers in dynamic environments. Furthermore, post-decision state (PDS) and prioritized experience replay (PER) schemes are utilized to enhance the learning efficiency and secrecy performance. Specifically, PDS is capable of tracing the environment dynamic characteristics and adjust the beamforming policy accordingly. Simulation results demonstrate that the proposed deep PDS-PER learning-based secure beamforming approach can significantly improve the system secrecy rate and QoS satisfaction probability in IRS-aided secure communication systems.