AI 中文总结
研究双流天线辅助无人机多用户多输入多输出下行通信网络，通过联合优化无人机轨迹、收发天线位置和波束成形最大化平均可达速率，采用交替优化算法求解，性能优于传统和现有基线。
AI 中文摘要
流体天线（FA）辅助无人机网络利用FA位置适应性和灵活波束成形克服固定位置天线（FPA）在动态无人机信道和多用户（MU）干扰中的局限性。本文研究双流FA辅助无人机网络用于MU多输入多输出（MIMO）下行通信，旨在通过联合优化无人机轨迹、发射/接收FA位置和波束成形来最大化平均可达速率。所提出的问题是高度耦合且非凸的。因此，针对分解后的子问题开发了一种基于交替优化（AO）的高效算法，得到一个次优解。数值结果表明，与传统基于FPA的和现有的基于FA的基线相比，分别有120%和110%的显著性能提升。
英文摘要
Fluid Antennas (FAs)-assisted Unmanned Aerial Vehicle (UAV) networks leverage the FA position adaptivity and flexible beamforming to overcome the limitations of Fixed-Positioned Antennas (FPAs) in dynamic UAV channels and Multi-User (MU) interference. This letter investigates a dual FA-assisted UAV network for MU-Multiple-Input-Multiple-Output (MIMO) downlink communications, aiming to maximize the average achievable rate through the joint optimization of UAV trajectory, the transmit/receive FA positions, and beamforming. The formulated problem is highly coupled and non-convex. Accordingly, an efficient Alternating Optimization (AO)-based algorithm is developed for decomposed subproblems, yielding a suboptimal solution. Numerical results demonstrate significant performance gains of 120% and 110% over conventional FPA-based and existing FA-based baselines, respectively.
