# Downlink Rate Maximization with Reconfigurable Intelligent Surface Assisted Full-Duplex Transmissions

arXiv: https://arxiv.org/abs/2311.03579 · HTML: https://mlsanity.com/paper/2311.03579/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2023.

## Built on by (most cited first, 2 of 2)

- [Multi-Functional RIS-Enabled in SAGIN for IoT: A Hybrid Deep Reinforcement Learning Approach with Compressed Twin-Models](https://mlsanity.com/paper/2507.16204/) (2025)
- [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)

Reconfigurable intelligent surfaces (RIS) as an effective technique for intelligently manipulating channel paths through reflection to serve desired users. Full-duplex (FD) systems, enabling simultaneous transmission and reception from a base station (BS), offer the theoretical advantage of doubled spectrum efficiency. However, the presence of strong self-interference (SI) in FD systems significantly degrades performance, which can be mitigated by leveraging the capabilities of RIS. In this work, we consider joint BS and RIS beamforming for maximizing the downlink (DL) transmission rate while guaranteeing uplink (UL) rate requirement. We propose an FD-RIS beamforming (FRIS) scheme by adopting penalty convex-concave programming. Simulation results demonstrate the UL/DL rate improvements achieved by considering various levels of imperfect CSI. The proposed FRIS scheme validates their effectiveness across different RIS deployments and RIS/BS configurations. FRIS has achieved the highest rate compared to the other approximation method, conventional beamforming techniques, HD systems, and deployment without RIS.