TRAVEL: Deterministic Transmission Scheduling for Satellite-Terrestrial Integrated Networks

In this paper, we investigate a deterministic transmission scheduling problem for satellite-terrestrial integrated networks (STIN), in which satellite networks supplement terrestrial networks endowed with deterministic mechanisms (e.g., cycle specified queuing and forwarding) to improve scheduling success ratio and reduce overall transmission delay (i.e., network revenue) while maintaining users' quality of service (QoS). We propose a fixed-mobile-satellite integrated architecture and formulate the transmission scheduling problem as a two-hierarchical routing and queuing problem. Then, a deep reinforcement learning-based Transient Routing And Varied quEue aLgorithm (TRAVEL) is developed to address the two-hierarchical decision problem. Specifically, according to the intrinsic properties of routing and queuing, we decouple the decision problem into two sub-problems, namely route planning at the macro level and queue selection at the micro level. The proposed TRAVEL can realize efficient decision making to perform the differential transmission scheduling of intricate tasks. Simulation results demonstrate that the TRAVEL delivers robust and effective performance, thereby enhancing network operation revenue in terms of different traffic proportion.