Energy-Efficient Resource Allocation for Space-Air-Ground Integrated Vehicular Network

Space-air-ground integrated vehicular networks (SAGIVNs) can provide long-distance communication service with wide coverage for ground users such as vehicles. However, scarce spectrum resources and long-distance transmission result in high path loss as well as latency. As relay stations, unmanned aerial vehicles (UAVs) play key roles in improving transmission quality between space and ground, i.e., satellites and vehicles, whose configuration of resources highly influences network performance. In this paper, we focus on joint resource allocation for UAVs in SAGIVNs. Specifically, the joint optimization problem in user association, trajectory design, and power control is addressed. By considering the trade-off between transmission rate and energy consumption in SAGIVNs, we propose an energy-efficient joint resource allocation approach named ERDM. Firstly, vehicles are assigned to UAVs based on their positions. hen, we formulate the optimization problem to maximize energy efficiency, the sum rate with unit power cost, with consideration of service quality constraints. To reduce the complexity of the problem, we employ multi-agent deep reinforcement learning framework to obtain optimal solutions. By incorporating the behavior of other agents, UAVs learn to allocate resources for themselves and update Q-networks using gained experiences in distinctive observations and feedback rewards. Simulations reveal ERDM outperforms other benchmarks by up to 24.72% in energy efficiency under different circumstances.