Deep Reinforcement Learning-Based Joint Routing and Capacity Optimization in an Aerial and Terrestrial Hybrid Wireless Network
As the airspace is experiencing an increasing number of low-altitude aircraft, the concept of spectrum sharing between aerial and terrestrial users emerges as a compelling solution to improve the spectrum utilization efficiency.In this paper, we Sofa Chaise consider a new Aerial and Terrestrial Hybrid Network (ATHN) comprising aerial vehicles (AVs)