Joint Optimization Algorithm for UAV Trajectory and Beamforming for NOMA‐Based Integrated Sensing and Communication (ISAC) Systems

ABSTRACT Non‐orthogonal multiple access (NOMA) combined with integrated sensing and communication (ISAC) presents a promising solution to 6G spectrum scarcity. However, non‐line‐of‐sight channels caused by obstacles significantly degrade performance. Unmanned aerial vehicles (UAVs) can establish line‐of‐sight links, thereby improving communication rates. Existing research primarily focuses on stationary targets, while studies on moving targets rely on single‐UAV sensing, leading to substantial estimation errors in dynamic environments. To address this, we propose a UAV‐enabled NOMA‐ISAC network that employs an extended Kalman filter for accurate target tracking. We aim to jointly optimize the UAV trajectory and beamforming vector using a block coordinate descent framework, where the subproblems are efficiently solved via successive convex approximation and semidefinite programming. Numerical results demonstrate that the proposed scheme achieves an 18.47% higher sum rate compared to benchmark schemes.