Among the most common issues to be faced in optical satellite communications, weather conditions play a fundamental role for a correct transmission of the information. In the case of heavy rain, hailstorm snow, or even dense clouds, a communication channel between the satellite and a optical ground station (OGS) may suffer significant interference, causing errors in delivering information. Since satellite transmissions cover in general very widespread areas, it usually happens that different zones are characterized by different weather conditions. This property is exploited by the site diversity technique, that tries to limit bad weather effects on the overall availability of the communication channel. When implementing such a site diversity technique, the satellite should be able to switch between the OGSs, by evaluating the rain events probability either through direct measurement campaigns or exploiting statistical models. The setup studied in this work involves a geostationary satellite equipped with two laser communication terminals (LCTs). In order to dynamically decide the OGSs to be pointed by those LCTs, a Deep-Learning based proactive control algorithm for site diversity, performing weather forecasting and consequent preventive LCT switching on the basis of current and past weather conditions has been developed. Simulative results show the ability of our proposed algorithm in achieving the maximum possible link availability, which is bounded by the weather conditions of all the OGSs.

An intelligent ground station selection algorithm in satellite optical communications via deep learning / Wrona, Andrea; De Santis, Emanuele; Delli Priscoli, Francesco; Lavacca, Francesco Giacinto. - (2023), pp. 493-499. (Intervento presentato al convegno 2023 31st Mediterranean Conference on Control and Automation (MED) tenutosi a Limassol; Cyprus) [10.1109/MED59994.2023.10185908].

An intelligent ground station selection algorithm in satellite optical communications via deep learning

Wrona, Andrea;De Santis, Emanuele
;
Delli Priscoli, Francesco;Lavacca, Francesco Giacinto
2023

Abstract

Among the most common issues to be faced in optical satellite communications, weather conditions play a fundamental role for a correct transmission of the information. In the case of heavy rain, hailstorm snow, or even dense clouds, a communication channel between the satellite and a optical ground station (OGS) may suffer significant interference, causing errors in delivering information. Since satellite transmissions cover in general very widespread areas, it usually happens that different zones are characterized by different weather conditions. This property is exploited by the site diversity technique, that tries to limit bad weather effects on the overall availability of the communication channel. When implementing such a site diversity technique, the satellite should be able to switch between the OGSs, by evaluating the rain events probability either through direct measurement campaigns or exploiting statistical models. The setup studied in this work involves a geostationary satellite equipped with two laser communication terminals (LCTs). In order to dynamically decide the OGSs to be pointed by those LCTs, a Deep-Learning based proactive control algorithm for site diversity, performing weather forecasting and consequent preventive LCT switching on the basis of current and past weather conditions has been developed. Simulative results show the ability of our proposed algorithm in achieving the maximum possible link availability, which is bounded by the weather conditions of all the OGSs.
2023
2023 31st Mediterranean Conference on Control and Automation (MED)
GEO satellite; optical communications; deep learning; site diversity; handover
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
An intelligent ground station selection algorithm in satellite optical communications via deep learning / Wrona, Andrea; De Santis, Emanuele; Delli Priscoli, Francesco; Lavacca, Francesco Giacinto. - (2023), pp. 493-499. (Intervento presentato al convegno 2023 31st Mediterranean Conference on Control and Automation (MED) tenutosi a Limassol; Cyprus) [10.1109/MED59994.2023.10185908].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1686000
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