A Review on AI-Driven Aerial Access Networks: Challenges and Open Research Issues

Aerial access networks (AANs) consisting of low altitude platforms (LAPs) and high altitude platforms (HAPs) have been considered as emerging wireless networking technologies to enhance both the capacity and coverage of future wireless networks, especially in remote and hard to reach areas with lack of terrestrial base stations. However, the limited onboard resources and high dynamicity of the network make challenging to optimally manage both the communication and computation resources for an efficient aerial networking infrastructure. On the other hand, artificial intelligence (AI), especially reinforcement learning- and deep reinforcement learning-based networking, are attracting significant attention to capture the network dynamicity and long-term resource management performance, recently. Thus, in this paper, we first provide a taxonomy of AI-driven aerial access networks and then, present a review and discussion on the state-of-the-art researches on AI-driven AANs from the communication and computation perspective. Moreover, we identify existing research challenges and provide future research direction for further investigations.