How Generative Models Improve LOS Estimation in 6G Non-Terrestrial Networks

by   Saira Bano, et al.

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


Measuring the quality of Synthetic data for use in competitions

Machine learning has the potential to assist many communities in using t...

Synthetic Data – A Privacy Mirage

Synthetic datasets drawn from generative models have been advertised as ...

FedSyn: Synthetic Data Generation using Federated Learning

As Deep Learning algorithms continue to evolve and become more sophistic...

Understanding how Differentially Private Generative Models Spend their Privacy Budget

Generative models trained with Differential Privacy (DP) are increasingl...

Synthetic data, real errors: how (not) to publish and use synthetic data

Generating synthetic data through generative models is gaining interest ...

Challenges in creative generative models for music: a divergence maximization perspective

The development of generative Machine Learning (ML) models in creative p...

Please sign up or login with your details

Forgot password? Click here to reset