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

TL;DR

This paper proposes a framework using generative models to create synthetic data for LOS estimation in 6G non-terrestrial networks, enabling effective ML training with limited data and preserving privacy.

Contribution

It introduces a novel approach of leveraging generative models to produce synthetic datasets for LOS estimation, reducing data requirements and privacy concerns.

Findings

Generative models can produce large, realistic datasets from small initial samples.

Synthetic data improves LOS estimation accuracy in 6G non-terrestrial networks.

The approach maintains privacy by avoiding exposure of original network data.

Abstract

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