RIS-Aided Fluid Antenna Array-Mounted UAV Networks

TL;DR

This paper proposes a novel RIS-assisted UAV network with fluid antennas on UAVs, optimizing multiple parameters to maximize downlink rate and demonstrate superior performance over existing methods.

Contribution

It introduces a joint optimization framework for RIS, UAV deployment, and fluid antenna positioning, employing advanced algorithms to enhance network capacity.

Findings

Achieves the highest downlink rate among compared architectures.

Demonstrates the effectiveness of RIS and fluid antennas in UAV networks.

Outperforms benchmarks like zero-forcing and heuristic methods.

Abstract

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.