Machine Learning and Direct Device to Satellite Communication

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Machine Learning techniques will improve the service performance and management NTN (satellite to earth) connectivity. Non-terrestrial networks (NTNs) have been gaining importance in the last years due to their technological improvements and the integration in the 3GPP standards. Direct satellite-to-device connectivity will have more than 25 million subscribers by the end of 2023 due to the investments of several important companies, such as Apple, Globalstar, SpaceX/Starlink and T-Mobile,Inmarsat, Iridium and Samsung.

Simulations proved that by using ML, the average cost per device can be reduced by 50–60% with respect to a random or even offloading strategy.

Finding the optimal path between two or more nodes of a network can be challenging if highly varying network conditions, such as overloaded routers, malfunctions or network outages, have to be considered. Traditional routing algorithms have to deal with a heavy computation load to assure good end-to-end transmission performance. Moreover, obtaining good local performance does not imply that the global transmission performance improves, too. In NTNs, the routing problems it is even more difficult due to the heterogeneous nature of the network. Researchers proposed a supervised learning approach, which improves the routing performance in terms of signaling overhead, throughput and per hop delay compared to the traditional Open Shortest Path First (OSPF) protocol. Simulation results proved that the ML approach outperforms OSPF in the three metrics. More specifically, this technique lowered the signaling overhead by 70%, increasing the throughput of 2% and reducing the hop-delay of 90%.