# A DRL Approach for RIS-Assisted Full-Duplex UL and DL Transmission: Beamforming, Phase Shift and Power Optimization

arXiv: https://arxiv.org/abs/2212.13854 · HTML: https://mlsanity.com/paper/2212.13854/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Dec 2022.

## Built on by (most cited first, 5 of 5)

- [Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models](https://mlsanity.com/paper/2502.07211/) (2025)
- [Joint Link Adaptation and Device Scheduling Approach for URLLC Industrial IoT Network: A DRL-based Method with Bayesian Optimization](https://mlsanity.com/paper/2512.23493/) (2025)
- [DRL-AdaPart: DRL-Driven Adaptive STAR-RIS Partitioning for Fair and Efficient Resource Utilization](https://mlsanity.com/paper/2407.06868/) (2024)
- [Geometry Aware Meta-Learning Neural Network for Joint Phase and Precoder Optimization in RIS](https://mlsanity.com/paper/2409.11270/) (2024)
- [Multi-User Beamforming with Deep Reinforcement Learning in Sensing-Aided Communication](https://mlsanity.com/paper/2505.05956/) (2025)

## Abstract (arXiv)

We propose a deep reinforcement learning (DRL) approach for a full-duplex (FD) transmission that predicts the phase shifts of the reconfigurable intelligent surface (RIS), base station (BS) active beamformers, and the transmit powers to maximize the weighted sum rate of uplink and downlink users. Existing methods require channel state information (CSI) and residual self-interference (SI) knowledge to calculate exact active beamformers or the DRL rewards, which typically fail without CSI or residual SI. Especially for time-varying channels, estimating and signaling CSI to the DRL agent is required at each time step and is costly. We propose a two-stage DRL framework with minimal signaling overhead to address this. The first stage uses the least squares method to initiate learning by partially canceling the residual SI. The second stage uses DRL to achieve performance comparable to existing CSI-based methods without requiring the CSI or the exact residual SI. Further, the proposed DRL framework for quantized RIS phase shifts reduces the signaling from BS to the RISs using 32 times fewer bits than the continuous version. The quantized methods reduce action space, resulting in faster convergence and 7.1% and 22.28% better UL and DL rates, respectively than the continuous method.