Interference-Constrained Integrated Sensing, Communication, and Compution for Clustered UAV Networks: Hierarchical Alternating Optimization for the Delay-Value Trade-Off

To address the issues of spectrum scarcity, inefficient resource scheduling, imbalanced target assignment, and susceptibility to interference in cluster-based unmanned aerial vehicle (UAV) networks for collaborative task execution, we propose an anti-interference integrated sensing, communication, and computation (ISCC) algorithm for UAVs. With the joint optimization of time delay and sensing information value as the objective, we explicitly characterize the impact of interference on link capacity and sensing quality, and formulate the system decisions as a joint optimization problem encompassing the allocation of sensing targets, data offloading subcarriers, computation data offloading ratio, and computing resource. To solve the problem efficiently, we design a hierarchical alternating optimization (HAO) framework. This framework decouples continuous and discrete variables in layers and iteratively solves them alternately. Simulation results show that the proposed HAO-based anti-interference ISCC algorithm converges rapidly under various interference levels and achieves a better balance between network time delay and sensing information value compared to multiple baselines, providing a viable path for the practical deployment of anti-interference UAV swarms.