Low-Latency Content Delivery in High-Speed Railway Communication Systems Enabled by Space-Terrestrial Integrated Networks

Considering the mobility of high-speed railway (HSR) trains, we propose an interruption-avoiding transmission paradigm for content delivery in HSR communication systems, enabled by a space-terrestrial integrated network (STIN) that supports both out-carriage and in-carriage communications. Each content delivery task must be fully transmitted from a satellite or terrestrial base station to the corresponding carriage femto-cell access point (FAP) before handoff occurs. To minimize the maximum transmission latency, we formulate the Content delivery task Scheduling and Resource Allocation problem for both Out-carriage and In-carriage communications (CSRA-OI) over a long-term scheduling period. To solve the CSRA-OI problem, we decompose it into two interrelated sub-problems, i.e., a transmission rate control and in-carriage bandwidth allocation (RCIBA) sub-problem, and a content delivery task scheduling and out-carriage resource management (CSORM) sub-problem. We show that the optimal solution to the RCIBA sub-problem can be obtained by solving a series of linear programming problems. To solve the CSORM sub-problem, we propose a multi-agent deep reinforcement learning (DRL)-based framework assisted by a graph attention network (GAT). We present a novel training scheme for the CSORM framework, where the policy networks are updated rapidly in a distributed manner while the critic network and GAT are updated jointly at a slower rate. Extensive experiments validate the advantages of our approach.