[en] 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.
