# Deep Reinforcement Learning for Optimizing RIS-Assisted HD-FD Wireless Systems

arXiv: https://arxiv.org/abs/2110.04859 · HTML: https://mlsanity.com/paper/2110.04859/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2021.

## Built on by (most cited first, 7 of 7)

- [Deep-Learning Channel Estimation for IRS-Assisted Integrated Sensing and Communication System](https://mlsanity.com/paper/2402.09441/) (2024)
- [Deep-Learning-Based Channel Estimation for IRS-Assisted ISAC System](https://mlsanity.com/paper/2402.09439/) (2024)
- [Extreme Learning Machine-based Channel Estimation in IRS-Assisted Multi-User ISAC System](https://mlsanity.com/paper/2402.09440/) (2024)
- [A Heuristic-Integrated DRL Approach for Phase Optimization in Large-Scale RISs](https://mlsanity.com/paper/2505.04401/) (2025)
- [Security-aware Semantic-driven ISAC via Paired Adversarial Residual Networks](https://mlsanity.com/paper/2509.20835/) (2025)
- [Active RIS-aided EH-NOMA Networks: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2304.12184/) (2023)
- [Aerial Multi-Functional RIS in Fluid Antennas-Aided Full-Duplex Networks: A Self-Optimized Hybrid Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2604.14309/) (2026)

## Abstract (arXiv)

This letter investigates the reconfigurable intelligent surface (RIS)-assisted multiple-input single-output (MISO) wireless system, where both half-duplex (HD) and full-duplex (FD) operating modes are considered together, for the first time in the literature. The goal is to maximize the rate by optimizing the RIS phase shifts. A novel deep reinforcement learning (DRL) algorithm is proposed to solve the formulated non-convex optimization problem. The complexity analysis and Monte Carlo simulations illustrate that the proposed DRL algorithm significantly improves the rate compared to the non-optimized scenario in both HD and FD operating modes using a single parameter setting. Besides, it significantly reduces the computational complexity of the downlink HD MISO system and improves the achievable rate with a reduced number of steps per episode compared to the conventional DRL algorithm.