# Machine Learning Techniques for Non-Terrestrial Networks
## 1 Introduction  
Non-terrestrial networks (NTNs) have been gaining importance in the last years due to their technological improvements and the integration in the 3 GPP standards. According to [1], 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, just to cite a few, to which will be added Inmarsat, Iridium and Samsung. Traditionally NTNs are used for a limited set of applications, such as TV broadcasting and communication support during disaster relief. Moreover, the natural service of global communications easily implemented by NTNs has been used to a limited extent worldwide, usually replaced by terrestrial communications systems. Previously, NTN communications were considered just a connection between a terrestrial terminal (both fixed or mobile) with a satellite, usually in medium-Earth orbit (MEO) as for iridium or in geostationary-Earth orbit (GEO). The link distance and the difficulties in providing indoor coverage have limited the applicability of the satellite and the NTN service diffusion. Recent technological advances, such as chip-set integration, terrestrial and in-orbit equipment improvement and mobility management strategies as well as the cost reduction in the equipment and in launch, have favored NTN interest for the research and business community. In addition to MEO and GEO satellites, low-Earth orbit (LEO) satellites, high-altitude platform stations (HAPSs) and unmanned aerial vehicles (UAVs) are under investigation to be integrated in the NTNs. Then, novel applications and services related to _service continuity_, _throughput enhancement_ and _ubiquitous connectivity_ can now be provided, worldwide. Nevertheless, some intrinsic limitations of NTN elements remain due to their latency and indoor coverage. According to the authors, the following aspects are fundamental for the NTN diffusion and to exploit their potentials:  
* The integration of terrestrial networks (TNs) and NTNs; deploying two (or more) separated networks will increase the service availability. * The adoption of a unique terminal for both NTN and TN communications; people connect to TNs most, whilst they rarely connect to NTNs. In case it is necessary to switch between the terrestrial equipment and the NTN enabled equipment, this will discourage the NTN diffusion. * Overcoming of the NTN limits in terms of throughput availability and delays. Even with narrow-beams, very high-altitude satellites (i.e., MEO and GEO) cover a large area limiting the available throughput for a single user. Moreover, large distances (from 8000 km to 36,000 km) causes inadequate user experience, not only for video and real-time applications, such as gaming, but also for voice service, experiencing latencies from about 200 ms up to 800 ms (including processing) [2]. These aspects are being overcome from one side by the adoption of the new radio (NR) radio interface, which envisages the integration of the NTN in the 3 GPP standards, and from the other side, the use of dedicated non-terrestrial elements (NTEs), such as UAV, HAPS and LEO satellites on novel orbits, which are more suitable for the current near real-time applications. Considering the characteristics of the NTEs, it is difficult to dynamically set the optimal connection, also taking into consideration the required service in terms for example of required latency and throughput. For example, the NTN system should anticipate the switch from one NTE to another according to the mobility of the user equipment (UE) or to properly steer the antenna beam. To this aim, machine learning (ML) can be helpful [3]. As will be detailed in the following sections, ML is a key enabler for NTN networks. In fact, traditional terrestrial 5G approaches could be inadequate for an architecture, which includes ground, air and space elements.