# Machine Learning Techniques for Non-Terrestrial Networks
## 2 Related Works  
Networks involving satellites and other aerial vehicles have been largely studied in the literature. See these reviews [4; 5; 6] and their references. Most of them focus on the adoption of satellites or aerial vehicles to support cellular communications or internet of things (IoT) connections in some use cases [7]. Usually they provide connectivity just assuming a transceiver is posed on the satellite, thus without considering any base station constraints (e.g., energy, weight and database). In [8; 9], some use cases are proposed for IoT data collection or coverage extension but without considering any standard for communication, thus reducing the impact of the work. Most of them propose strategies for traffic optimization or balancing between terrestrial and satellite networks as in [10] or link performance analysis, such as in mmWave [11] or in optical links [12]. The authors in [13] provide performance analysis for IoT in rural areas using low power wide area (LPWA) technologies. Some testbeds are now available [14]. Interesting results related to aerial link characterization and antenna orientation for IoT sensor data collection are in [15], useful for any coverage dimensioning. Any access procedure was not considered for any proper service delivery. Other works, such as [16; 17], investigate the optimization of the scheduling strategy or NTE relay with respect to energy consumption. Security aspects emerging in a space network subject to eavesdropping are analyzed in [18] in which appropriate secrecy performance metrics are also reported. Further NTN challenges, such as coexistence, terminal requirements and spectrum policies are analyzed in [19].  
The authors in [20] report the preliminary efforts of 3 GPP to integrate satellite within the 5G explaining only the flow segmentation between the NTE based on the role of the NTN infrastructure. In [21], the authors envisage possible evolution steps towards 6G. Differently from available works in literature, this paper reports the NTN architectures that can be integrated in the 3 GPP and proposes new services with the related requirements that can be supported by the NTN integration in the NR. Scenarios and systems that do not consider any communication standard and any standardized wireless system architecture in service provisioning are out of the scope. Protocol stack split between NTEs is also investigated.  
ML has been widely implemented in 5G and 6G networks. Supervised, unsupervised and reinforcement learning techniques are applicable to cellular networks in order to optimize aspects, which are hard to manage with traditional algorithmic approaches. In [22], an extensive review of advantages and disadvantages of popular ML approaches is presented. Refs. [23; 24] also proposed an overview and a survey, respectively, on this study subject. The authors in [25] investigated the adoption of federated learning (FL) in LEO-based satellite communication networks in order to improve the performance and reduce overheads and latencies. However, in this work, only space elements are taken into consideration, while the airborne ones are excluded. Adding intelligence to IoT sensor networks, especially in remote areas, is also an issue under study. In [26], the authors present a review of ML technologies for enhanced NTNs, at every altitude, specifically for IoT scenarios. This paper draws attention to the prediction and decision-making procedures, which are essential to achieve self-adaptation, thus enhancing the considered performance metrics