ML-based PBCH symbol detection and equalization for 5G Non-Terrestrial Networks

Inés Larráyoz-Arrigote; Marcele O. K. Mendonca; Alejandro Gonzalez-Garrido; Jevgenij Krivochiza; Sumit Kumar; Jorge Querol; Joel Grotz; Stefano Andrenacci; Symeon Chatzinotas

arXiv · 2309.14923

ML-based PBCH symbol detection and equalization for 5G Non-Terrestrial Networks

Inés Larráyoz-Arrigote·Marcele O. K. Mendonca·Alejandro Gonzalez-Garrido·Jevgenij Krivochiza·Sumit Kumar·Jorge Querol·Joel Grotz·Stefano Andrenacci·Symeon Chatzinotas

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Abstract

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.

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Inés Larráyoz-Arrigote, Marcele O. K. Mendonca, Alejandro Gonzalez-Garrido, Jevgenij Krivochiza, Sumit Kumar, Jorge Querol, Joel Grotz, Stefano Andrenacci, Symeon Chatzinotas. 2023-09-26. ML-based PBCH symbol detection and equalization for 5G Non-Terrestrial Networks. https://arxiv.org/abs/2309.14923

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