Koopman-based Prediction of Connectivity for Flying Ad Hoc Networks

TL;DR

This paper introduces Koopman operator-based data-driven methods to predict connectivity in highly dynamic flying ad hoc networks, improving communication reliability by modeling UAV trajectories and predicting outages.

Contribution

It presents novel Koopman-based centralized and distributed approaches for modeling UAV dynamics and predicting network connectivity in FANETs, addressing challenges of dynamic topology.

Findings

Koopman approaches accurately predict connectivity and outages.

Methods improve scheduling and reliability in UAV networks.

Demonstrated effectiveness in surveillance UAV scenarios.

Abstract

The application of machine learning (ML) to communication systems is expected to play a pivotal role in future artificial intelligence (AI)-based next-generation wireless networks. While most existing works focus on ML techniques for static wireless environments, they often face limitations when applied to highly dynamic environments, such as flying ad hoc networks (FANETs). This paper explores the use of data-driven Koopman approaches to address these challenges. Specifically, we investigate how these approaches can model UAV trajectory dynamics within FANETs, enabling more accurate predictions and improved network performance. By leveraging Koopman operator theory, we propose two possible approaches -- centralized and distributed -- to efficiently address the challenges posed by the constantly changing topology of FANETs. To demonstrate this, we consider a FANET performing surveillance with UAVs following pre-determined trajectories and predict signal-to-interference-plus-noise ratios (SINRs) to ensure reliable communication between UAVs. Our results show that these approaches can accurately predict connectivity and isolation events that lead to modelled communication outages. This capability could help UAVs schedule their transmissions based on these predictions.