A Resource Allocation Optimization Method for Multi-UAV Integrated Sensing, Computing, and Communication Towards Disaster Relief

For disaster-relief scenarios where natural disasters damage terrestrial communications and constrain on-site data processing, a multi-unmanned aerial vehicle (UAV) integrated sensing, computing, and communication system is considered and a joint resource allocation optimization model formulated. Under constraints on transmit power, detection reliability, UAV speed, inter-UAV safety distance, computing capability, and UAV-access point (AP) association, the objective is to maximize the weighted computed data and the sensing directional gain by jointly optimizing UAV trajectories, transmit beamforming, and association. The formulated problem is a strongly coupled mixed-integer nonconvex program. To address it, an alternating optimization algorithm is developed to decompose the original problem into three subproblems. Firstly, the UAV-AP association is transformed into a linear program by relaxing binary variables and introducing linear constraints, and is efficiently solved via an interior-point method. Secondly, the beamforming subproblem is handled using semidefinite relaxation, and a rank-one solution is recovered through Gaussian randomization to balance the communication rate and sensing beampattern gain. Finally, the trajectory subproblem linearizes the nonconvex rate expression and safety-distance constraints using first-order Taylor expansion, leading to a sequence of convex approximation problems that can be solved iteratively. Simulation results demonstrate that, under different parameter settings, the proposed method achieves higher and more balanced sensing, computing, and communication performance than other baseline schemes.