  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
