NOMA-based joint optimization of trajectory and resources for UAV-enable integrated sensing and communication system

This study examines the difficulties of target selection, beamforming, and the combined optimization of UAV trajectory and speed inside the integrated sensing and communication (ISAC) architecture in order to address the shortage of wireless spectrum resources and the interference among multi-user signals. By leveraging non-orthogonal multiple access (NOMA) technology, the overall system throughput is enhanced. Given the non-convex nature of the problem, this research employs the block coordinate descent (BCD) method to decompose the intricate problem into three manageable subproblems. Utilizing relaxation variables, first-order Taylor approximation, and successive convex approximation (SCA), the method reduces computational complexity and improves efficiency. Alternating iterations of these subproblems yield an approximately optimal solution to the original problem. The suggested algorithm's effectiveness is confirmed by simulation results, which show how it may greatly increase maximum average throughput and exhibit strong convergence.