# RIS-Aided Fluid Antenna Array-Mounted UAV Networks

arXiv: https://arxiv.org/abs/2501.09351 · HTML: https://mlsanity.com/paper/2501.09351/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2025.

## Built on by (most cited first, 3 of 3)

- [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)
- [Parametrized Sharing for Multi-Agent Hybrid DRL for Multiple Multi-Functional RISs-Aided Downlink NOMA Networks](https://mlsanity.com/paper/2601.00538/) (2026)
- [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 paper investigates reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicle (UAV) downlink networks with fluid antennas (FA), where RIS enables non-line-of-sight (NLoS) transmissions. Moreover, the FA is equipped on the UAV offering dynamic antenna position adjustment, enhancing spatial diversity besides UAV deployment. We aim at total downlink rate maximization while ensuring minimum user rate requirement. We consider joint optimization of active UAV beamforming, passive RIS beamforming, UAV deployment and FA position adjustment. To address the complex problem, we propose beamfomring for RIS/UAV and FA-UAV deployment (BRAUD) scheme by employing alternative optimization, successive convex approximation (SCA) and sequential rank-one constraint relaxation (SROCR) method for the decomposed subproblems. Simulation results demonstrate the effectiveness of RIS-FA-UAV, achieving the highest rate among existing architectures without FA/UAV/RIS deployment and without proper beamforming. Moreover, BRAUD achieves the highest rate among benchmarks of drop-rank method, heuristic optimizations and conventional zero-forcing beamforming as well as random method.