With the advent of 5G and the anticipated arrival of 6G, there has been a growing research interest in combining mobile networks with Non-Terrestrial Network platforms such as low earth orbit satellites and Geosynchronous Equatorial Orbit satellites to provide broader coverage for a wide range of applications. However, integrating these platforms is challenging because Line-Of-Sight (LOS) estimation is required for both inter satellite and satellite-to-terrestrial segment links. Machine Learning (ML) techniques have shown promise in channel modeling and LOS estimation, but they require large datasets for model training, which can be difficult to obtain. In addition, network operators may be reluctant to disclose their network data due to privacy concerns. Therefore, alternative data collection techniques are needed. In this paper, a framework is proposed that uses generative models to generate synthetic data for LOS estimation in non-terrestrial 6G networks. Specifically, the authors show that generative models can be trained with a small available dataset to generate large datasets that can be used to train ML models for LOS estimation. Furthermore, since the generated synthetic data does not contain identifying information of the original dataset, it can be made publicly available without violating privacy

翻译：随着5G的到来和6G的预期发展，将移动网络与低地球轨道卫星、地球同步轨道卫星等非地面网络平台相结合以提供更广泛覆盖的研究兴趣日益增长。然而，整合这些平台面临挑战，因为星际链路和星地链路都需要进行视距估计。机器学习技术在信道建模和视距估计方面展现出潜力，但模型训练需要大量数据集，而这类数据往往难以获取。此外，网络运营商可能因隐私顾虑不愿公开其网络数据。因此，需要替代性的数据采集技术。本文提出一种利用生成式模型为非地面6G网络视距估计生成合成数据的框架。具体而言，作者证明生成式模型可利用少量可用数据集训练，生成可用于训练视距估计机器学习模型的大规模数据集。此外，由于生成的合成数据不包含原始数据集的标识信息，因此可在不违反隐私规定的情况下公开共享。