Satellite Resource Allocation Strategy for the Combined Scenario of Unmanned Terminals and Mobile Users

Aiming at the complex hybrid scenario where Low Earth Orbit (LEO) satellite communication systems simultaneously serve unmanned terminals and terrestrial mobile users, this study proposes a two-stage resource allocation strategy based on the Deep Deterministic Policy Gradient (DDPG) algorithm. The strategy is designed to tackle the problems of uneven traffic distribution and large discrepancies in users’ real-time requirements. First, load balancing is achieved by flexibly adjusting the mapping relationship between users and satellite beams. Then, the Time-Frequency Deep Deterministic Policy Gradient (TF-DDPG) deep reinforcement learning algorithm is adopted, through which the agent autonomously learns via training and dynamically allocates time-frequency resources within a short period, giving priority to guaranteeing the communication demands of unmanned terminals. Simulation results demonstrate that, compared with heuristic algorithms, the proposed strategy realizes millisecond-level response in resource allocation decisions and improves system resource utilization, with an average user satisfaction rate of 73.41%. This method effectively resolves the issue of satellite time-frequency resource allocation in complex hybrid scenarios and provides a practical solution for the efficient resource management of future LEO satellite internet systems.