Learning-Based Joint Resource Slicing and Scheduling in Space-Terrestrial Integrated Vehicular Networks

In this paper, we investigate the resource slicing and scheduling problem in the space-terrestrial integrated vehicular networks to support both delay-sensitive services (DSSs) and delay-tolerant services (DTSs). Resource slicing and scheduling are to allocate spectrum resources to different slices and determine user association and bandwidth allocation for individual vehicles. To accommodate the dynamic network conditions, we first formulate a joint resource slicing and scheduling (JRSS) problem to minimize the long-term system cost, including the DSS requirement violation cost, DTS delay cost, and slice reconfiguration cost. Since resource slicing and scheduling decisions are interdependent with different timescales, we decompose the JRSS problem into a large-timescale resource slicing subproblem and a small-timescale resource scheduling subproblem. We propose a two-layered reinforcement learning (RL)-based JRSS scheme to find the solutions to the subproblems. In the resource slicing layer, spectrum resources are pre-allocated to different slices via a proximal policy optimization-based RL algorithm. In the resource scheduling layer, spectrum resources in each slice are scheduled to individual vehicles based on dynamic network conditions and service requirements via matching-based algorithms. We conduct extensive trace-driven experiments to demonstrate that the proposed scheme can effectively reduce the system cost while satisfying service quality requirements.