Joint Optimization of Fluid Antenna Port Position and UAV Trajectory for Age of Information Minimization

This paper investigates the joint optimization problem of antenna port selection and trajectory planning in unmanned aerial vehicle (UAV)-enabled mobile edge computing networks equipped with UAV-mounted fluid antenna systems (FAS), aiming to minimize the average Age of Information (AoI) of the system. FAS can dynamically reconfigure antenna positions within a predefined space, providing additional degrees of freedom for antenna spatial configuration in UAV communication systems. Considering the coupling relationship between UAV position and antenna configuration, a joint optimization model is established to simultaneously determine the UAV's two-dimensional trajectory and the optimal port configuration when the UAV antenna system serves each user. For this non-convex optimization problem, a solution algorithm based on deep reinforcement learning is proposed. Simulation results demonstrate that the proposed FAS+DDPG scheme achieves an AoI of 6.6 at the final time slot, representing a 69.6% improvement over the Trad+DDPG scheme (AoI = 21.7).