Assessment of satellite contacts using predictive algorithms for autonomous satellite networks

Upcoming Low Earth Orbit Satellite Networks will provide low-latency and high downlink capacity necessary for future broadband communications and Earth Observation missions. This architecture was proposed at the beginning of the 90's, although it has just recently re-gained popularity thanks to the so-called Mega-Constellations. This network is composed of satellites that have Inter-Satellite Links (ISL) to communicate between them. Due to the satellite motion, an ISL is a temporal contact between two satellites characterized by a lifetime in which the communication remains feasible. The determination of a route between distant satellites is a challenging problem in this context. However, the satellite follows a well-known deterministic orbit trajectory, being feasible the prediction of its position by propagating a trajectory model over time. The Contact Graph Routing protocol uses this feature to determine the evolution of the routes by pre-computing on-ground a planning of the satellite contacts. This centralized ground-dependent solution cannot be directly applied in the Internet of Satellites paradigm, which proposes the autonomous deployment of heterogeneous satellite networks without pre-assuming any specific satellite system architecture. Following this concept, the present work proposes a distributed algorithm by which a satellite predicts neighbor contacts, and generates a global contact plan without trajectory propagation. To achieve this solution, an ISL has been modeled as a 'close approach' between two satellites, which is characterized by their relative motion. The present work details the predictive algorithm, and evaluates its performance in two scenarios with a hybrid satellite constellation and a mega-constellation.
This work was supported in part by the CommSensLab Excellence Research Unit Maria de Maeztu (MINECO) under Grant MDM-2016-0600, in part by the Spanish Ministerio MICINN and EU ERDF Project (Sensing With Pioneering Opportunistic Techniques) under Grant RTI2018-099008-B-C21, in part by the AGAUR—Generalitat de Catalunya (FEDER) under Grant FI-DGR 2015, and in part by the Secretaria d’Universitats i Recerca del Departament d’Empresa i Coneixement de la Generalitat de Catalunya under Grant 2017 SGR 376.