# Inter-Satellite Link Prediction with Supervised Learning: An Application in Polar Orbits
###### Abstract  
Distributed space systems are increasingly valued in the space industry, as they enhance mission performance through collaborative efforts and resource sharing among multiple heterogeneous satellites. Additionally, enabling autonomous and real-time satellite-to-satellite communications through Inter-Satellite Links (ISLs) can further increase the overall performance by allowing cooperation without relying on ground links and extensive coordination efforts among diverse stakeholders. Given the constrained resources available onboard satellites, a crucial element of achieving cost-effective and autonomous cooperation involves minimizing energy wastage resulting from unsuccessful or unnecessary communication. To address this challenge, satellites must anticipate their ISL opportunities or encounters with minimal resource utilization. Building upon prior publications, this work presents further insights into the use of supervised learning to enable satellites to forecast their encounters without relying on orbit propagation. In particular, a more realistic definition of satellite encounters, along with a versatile solution applicable to all polar low-Earth orbit satellites is implemented. Results show that the trained model can anticipate encounters for realistic and unseen data from an available data source with a balance accuracy of around 90% and six times faster when compared with the well-known Simplified General Perturbation 4 orbital model. distributed space systems; non-terrestrial networks; inter-satellite links; low-Earth orbits; sun-synchronous orbits; simplified general perturbations; 6G; supervised learning +
Footnote †: journal: _aerospace_  
0000-0001-5000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-00000-00000-00000-00  
cooperation tasks, as optimal communication routes can change significantly over time. To address these challenges, it is essential to anticipate contact opportunities between satellites, also known as close-approach encounters. Traditional methods for anticipating satellite encounters typically employ deterministic and centralized on-ground orbit propagation techniques [5; 11]. These methods use an orbital model to propagate the satellite's initial states encoded in Two-Line Element (TLE) sets. While on-ground solutions excel at producing accurate contact windows, their centralized design renders them impractical for DSSs comprising heterogeneous satellites, as extensive collaboration among diverse stakeholders would be required. Additionally, the scalability of centralized computations is limited by their strong dependence on Earth-based systems and reliance on a single central unit for all data processing, making them unsuitable for mega-constellations. In response to these challenges, recent studies explore decentralized solutions to grant autonomy to satellites [12]. However, these methods typically rely on orbital propagation, which could impose significant processing costs for satellites with constrained resources. To avoid the need for orbital propagation while maintaining decentralization and autonomy, authors in [PERSON] et al. [13] implemented predictive algorithms to learn and construct the satellite contact windows. Nevertheless, some linearizations are employed in the mathematical formulation, making this approach applicable only to specific scenarios where satellites operate at the same altitude in circular and Keplerian orbits. As an alternative solution, [PERSON] and [PERSON] [14] investigated the use of Graph Neural Networks (GNNs) together with Recurrent Neural Networks (RNNs) as a scalable alternative to model the temporal evolution of large satellite network contacts in an autonomous and cost-efficient manner. Following previous works [15; 16], we present some advances to the proof of concept of using Supervised Learning (SL) to predict encounters between heterogeneous satellites. In particular, the encounter prediction problem is solved for polar LEO, where around 40% of all active satellites are located.