  Data packet routing in aeronautical ad-hoc networks (AANETs) is challenging
due to their high-dynamic topology. In this paper, we invoke deep learning (DL)
to assist routing in AANETs. We set out from the single objective of minimizing
the end-to-end (E2E) delay. Specifically, a deep neural network (DNN) is
conceived for mapping the local geographic information observed by the
forwarding node into the information required for determining the optimal next
hop. The DNN is trained by exploiting the regular mobility pattern of
commercial passenger airplanes from historical flight data. After training, the
DNN is stored by each airplane for assisting their routing decisions during
flight relying solely on local geographic information. Furthermore, we extend
the DL-aided routing algorithm to a multi-objective scenario, where we aim for
simultaneously minimizing the delay, maximizing the path capacity, and
maximizing the path lifetime. Our simulation results based on real flight data
show that the proposed DL-aided routing outperforms existing position-based
routing protocols in terms of its E2E delay, path capacity as well as path
lifetime, and it is capable of approaching the Pareto front that is obtained
using global link information.
