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

January 1, 2024·
Inés Larráyoz-Arrigote
,
Marcele OK Mendonça
,
Alejandro Gonzalez-Garrido
,
Jevgenij Krivochiza
,
Sumit Kumar
,
Jorge Querol
,
Joel Grotz
,
Symeon Chatzinotas
· 1 min read
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.
Type
Publication
2024 IEEE International Mediterranean Conference on Communications and Networking (MeditCom)
publication

Abstract & Overview

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.

Publication Metadata

  • Authors: Inés Larráyoz-Arrigote, Marcele OK Mendonça, Alejandro Gonzalez-Garrido, Jevgenij Krivochiza, Sumit Kumar, Jorge Querol, Joel Grotz, Symeon Chatzinotas
  • Venue: 2024 IEEE International Mediterranean Conference on Communications (2024)
  • Research Areas: 6G-NTN
  • DOI: 10.1109/meditcom61057.2024.10621238
  • Open Access Repository: ORBiLu – University of Luxembourg
6G-NTN