RIS-Aided Fluid Antenna Array-Mounted UAV Networks

2025-01-16

IEEE Wireless Communications Letters

AI Summary

This paper addresses the downlink transmission optimization in a reconfigurable intelligent surface (RIS)-assisted fluid antenna-enabled unmanned aerial vehicle (FA-UAV) network, aiming to maximize the total downlink rate while satisfying user-specific minimum-rate constraints. The problem jointly optimizes four tightly coupled dimensions: UAV’s 3D placement, active beamforming at the UAV, passive phase-shift control at the RIS, and dynamic positioning of the fluid antenna elements. Method: We propose BRAUD—a joint optimization framework leveraging alternating optimization, successive convex approximation (SCA), and the sequential rank-one constraint relaxation (SROCR) technique. Contribution/Results: To the best of our knowledge, this is the first work integrating RIS, fluid antennas, and UAV-based aerial platforms—overcoming limitations of conventional fixed antennas and static deployments, thereby significantly enhancing spatial degrees of freedom and non-line-of-sight (NLoS) link performance. Simulation results demonstrate up to a 42% improvement in total system throughput over benchmark schemes, validating the superiority and effectiveness of the proposed architecture and algorithm.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Optimization
• Unmanned Aerial Vehicle (UAV) Networks
• Reconfigurable Intelligent Surfaces (RIS)

Innovation

• Drone with Fluidic Antenna
• Reconfigurable Intelligent Surface (RIS)
• Network Performance Optimization