Deep Learning Aided Packet Routing in Aeronautical Ad-Hoc Networks Relying on Real Flight Data: From Single-Objective to Near-Pareto Multi-Objective Optimization

TL;DR

This paper introduces a deep learning-based routing method for aeronautical ad-hoc networks that leverages real flight data to optimize multiple objectives such as delay, capacity, and lifetime, outperforming existing protocols.

Contribution

It presents a novel DL-assisted routing approach that uses local geographic info and extends to multi-objective optimization in AANETs, based on real flight data.

Findings

Outperforms existing position-based routing in delay, capacity, and lifetime.

Capable of approaching the Pareto front with local info.

Effective in real flight scenarios.

Abstract

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.