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

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 signi...

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Bibliographic Details
Published in:arXiv.org 2023-09
Main Authors: Larráyoz-Arrigote, Inés, Marcele O K Mendonca, Gonzalez-Garrido, Alejandro, Krivochiza, Jevgenij, Kumar, Sumit, Querol, Jorge, Grotz, Joel, Andrenacci, Stefano, Chatzinotas, Symeon
Format: Article
Language:English
Subjects:
Equalization
Machine learning
Signal to noise ratio
Wireless communications
Online Access:Get full text
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Summary: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.
ISSN:2331-8422