Re-envisioning Space-Air-Ground Integrated Networks: Reinforcement Learning for Link Optimization

摘要

To provide ubiquitous connectivity and achieve high reliability in the under-served and under-connected areas, the integration of aerial and space communication infrastructures into terrestrial networks is envisioned as a promising solution. In this regard, unmanned aerial vehicles (UAVs), in the role of aerial base stations (BSs), have been recommended to solve the coverage problem. In order to effectively leverage the advantages of UAVs deployment, UAV trajectory and resource management are required to effectively adapt to the network conditions. However, providing backhaul connectivity for terrestrial and aerial BSs deployed in these areas is another tremendous challenge. In this paper, we aim at jointly optimizing backhaul link and access link in a space-air-ground integrated network. We consider low Earth orbit (LEO) satellites as an effective backhaul solution. For access links, we manage the radio resource among UAVs and small cell BSs, and optimize the trajectories of UAVs in the network. To solve the problem in a distributed manner, we utilize the tools of reinforcement learning, and propose two approaches based on the multi-armed bandit and satisfaction algorithms. Simulation results show that the proposed approaches yield significant performance compared to the benchmark algorithms.