Computation-Aware Beam Hopping for Airborne Sensing in Space-Air-Ground Integrated Networks

Space-air-ground integrated networks (SAGIN) combine the wide-area coverage of satellites with airborne platforms acting as relays, enabling efficient data delivery for large-scale Internet of Things (IoT) sensing applications. To further enhance transmission efficiency, this paper incorporates airborne onboard processing to reduce communication workloads and proposes an intelligent beam hopping strategy tailored to spatially uneven traffic demands. Specifically, we design a multi-agent deep reinforcement learning (MADRL)-based beam hopping framework, where satellite agents coordinate beam scheduling while considering spatial service heterogeneity and the diverse computational capacities of airborne platforms. To reduce the complexity introduced by individual task requirements, we integrate a summarized statistical profile of the computation tasks into the agent's observation space, including average and maximum computation efficiencies across tasks. Simulation results demonstrate that the proposed scheme significantly accelerates convergence and reduces the data backlog by up to 80%, especially under scenarios with considerable heterogeneity in service demands and airborne platform computational capabilities.