Demand-Driven Task Scheduling and Resource Allocation in Space-Air-Ground Integrated Network: A Deep Reinforcement Learning Approach

Space-air-ground integrated network (SAGIN) can support a wide variety of task scenarios as a next-generation network. In this dynamic network, tasks with different characteristics and demands are usually simultaneously generated, which requires an efficient task scheduling method to meet diverse task demands, reduce system cost, and improve resource utilization. In this paper, we propose a scalable task scheduling and resource allocation solution suitable for various task scenarios in SAGIN. Specifically, we construct a generic task scheduling framework that considers diverse task types and demands in SAGIN. With the objective of minimizing the total system cost, we propose a novel joint task scheduling and resource allocation algorithm based on deep reinforcement learning to obtain the optimal solution. Extensive simulations are conducted in a civil aviation scenario to evaluate the performance of the proposed algorithm. Numerical results demonstrate that our proposed algorithm can efficiently reduce system cost and task drop rate compared with three benchmark schemes, and the improvement of resource allocation to task scheduling is verified through analysis.