This paper delves into the application of Machine Learning (ML) techniques in the realm of 5G Non-Terrestrial Networks (5G-NTN), particularly focusing on symbol detection and equalization for the Physical Broadcast Channel (PBCH). As 5G-NTN gains prominence within the 3GPP ecosystem, ML offers significant potential to enhance wireless communication performance. To investigate these possibilities, we present ML-based models trained with both synthetic and real data from a real 5G over-the-satellite testbed. Our analysis includes examining the performance of these models under various Signal-to-Noise Ratio (SNR) scenarios and evaluating their effectiveness in symbol enhancement and channel equalization tasks. The results highlight the ML performance in controlled settings and their adaptability to real-world challenges, shedding light on the potential benefits of the application of ML in 5G-NTN.
Cite
@article{arxiv.2309.14923,
title = {ML-based PBCH symbol detection and equalization for 5G Non-Terrestrial Networks},
author = {Inés Larráyoz-Arrigote and Marcele O. K. Mendonca and Alejandro Gonzalez-Garrido and Jevgenij Krivochiza and Sumit Kumar and Jorge Querol and Joel Grotz and Stefano Andrenacci and Symeon Chatzinotas},
journal= {arXiv preprint arXiv:2309.14923},
year = {2023}
}