Joint communication and sensing optimization for LEO-Multi-UAV SAGIN: Task offloading, resource allocation and UAV trajectory

Abstract

Space-Air-Ground Integrated Network (SAGIN) is a key architecture for achieving wide-area sensing and communication services. However, the connection between Low Earth Orbit (LEO) satellites and ground devices is constrained by satellite mobility and service angles. Unmanned Aerial Vehicles (UAVs), acting as relay and sensing nodes, can effectively bridge this gap. Nevertheless, under their limited onboard resources, the coupled impacts between UAV trajectory planning and the performance of communication and sensing-especially in scenarios where multi-UAV collaboration extends LEO service coverage-have not been fully investigated. To address these challenges, this paper proposes an integrated sensing and computation offloading architecture for SAGIN, where UAVs perform multi-target sensing while cooperating with LEO satellites to provide communication and computational services. We formulate a joint optimization problem that encompasses user offloading decisions, communication-sensing time allocation, UAV trajectory planning, and computing resource allocation, aiming to minimize long-term service latency. This problem is modeled as a mixed-integer nonlinear program (MINLP). To efficiently solve it, we develop a low-complexity Lyapunov-Benders Optimization (LBO) algorithm based on Lyapunov optimization and generalized Benders decomposition, which decomposes the long-term problem into tractable single-slot subproblems. Simulation results validate that the proposed method outperforms existing benchmarks in service latency, demonstrating its effectiveness in dynamic SAGIN environments.