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

TL;DR

This paper explores the application of machine learning techniques to improve symbol detection and equalization in 5G Non-Terrestrial Networks, demonstrating promising results with real and synthetic data in satellite-based 5G systems.

Contribution

It introduces ML models trained on real and synthetic data for PBCH detection and equalization in 5G-NTN, addressing a novel application in satellite-based 5G networks.

Findings

ML models perform well under various SNR conditions

Models show adaptability to real-world satellite data

Enhanced symbol detection and channel equalization capabilities

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.