Koopman-based Prediction of Connectivity for Flying Ad Hoc Networks
This addresses the challenge of reliable communication in dynamic UAV networks, but it is incremental as it applies an existing Koopman method to a new domain.
The paper tackles the problem of predicting connectivity in highly dynamic flying ad hoc networks (FANETs) by using data-driven Koopman approaches to model UAV trajectory dynamics, resulting in accurate predictions of connectivity and isolation events that lead to communication outages.
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.