Anticipating Optical Availability in Hybrid RF/FSO Links Using RF Beacons and Deep Learning

TL;DR

This paper presents a deep learning-based system that uses RF beacons to predict FSO link degradation in satellite networks, enabling proactive rerouting to maintain high data rates amid atmospheric disturbances.

Contribution

It introduces a novel RF beacon-based predictive framework combined with supervised learning for anticipating FSO link availability in satellite constellations.

Findings

Achieved 86% prediction accuracy with 16 RF beacons.

Demonstrated effective proactive rerouting in simulated satellite networks.

Analyzed trade-offs between prediction horizon and accuracy.

Abstract

Radio frequency (RF) communications offer reliable but low data rates and energy-inefficient satellite links, while free-space optical (FSO) promises high bandwidth but struggles with disturbances imposed by atmospheric effects. A hybrid RF/FSO architecture aims to achieve optimal reliability along with high data rates for space communications. Accurate prediction of dynamic ground-to-satellite FSO link availability is critical for routing decisions in low-earth orbit constellations. In this paper, we propose a system leveraging ubiquitous RF links to proactively forecast FSO link degradation prior to signal drops below threshold levels. This enables pre-calculation of rerouting to maximally maintain high data rate FSO links throughout the duration of weather effects. We implement a supervised learning model to anticipate FSO attenuation based on the analysis of RF patterns. Through the simulation of a dense lower earth orbit (LEO) satellite constellation, we demonstrate the efficacy of our approach in a simulated satellite network, highlighting the balance between predictive accuracy and prediction duration. An emulated cloud attenuation model is proposed which provides insight into the temporal profiles of RF signals and their correlation to FSO channel dynamics. Our investigation sheds light on the trade-offs between prediction horizon and accuracy arising from RF beacon proximity, achieving a prediction accuracy of 86\% with 16 RF beacons.