DEEP REINFORCEMENT LEARNING-BASED JOINT ROUTING AND CAPACITY OPTIMIZATION IN AN AERIAL AND TERRESTRIAL HYBRID WIRELESS NETWORK

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)

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