Movable Antenna-Enabled UAV-ISAC: Joint Antenna Positioning, Beamforming, and Trajectory Optimization

Abstract

Movable antenna (MA) enables adaptive reconfiguration of array geometry by dynamically adjusting antenna element positions, thereby providing new spatial degrees of freedom for uncrewed aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC) systems. However, since the UAV trajectory changes the time-varying link distances and spatial directions, it is inherently coupled with MA position optimization. Most existing studies separately design antenna positioning, beamforming, and UAV trajectory, which limits the achievable average sum rate under sensing constraints. To address this issue, this paper proposes an MA-enabled UAV-ISAC system, where MA element positions are dynamically adjusted to enhance channel reconfigurability and spatial coordination capability. Under the sensing performance constraint, transmit power constraint, and UAV propulsion energy budget constraint, a non-convex joint optimization problem is formulated to maximize the average sum rate of the system by jointly optimizing MA positions, beamforming, and UAV trajectory. To solve this problem, an alternating optimization algorithm is developed by integrating particle swarm optimization, successive convex approximation, and semidefinite relaxation. Simulation results demonstrate that the proposed scheme significantly outperforms benchmark schemes with fixed-position antennas, random-position antennas, and fixed trajectory in terms of average sum rate, thereby verifying its effectiveness in enhancing the communication-sensing synergy of UAV-ISAC systems.