2023/09/26 by Inés Larráyoz-Arrigote, Marcele O. K. Mendonça, Larráyoz-Arrigote, Inés +15
Engineering · #Advanced Wireless Communication Techniques #FOS: Electrical engineering #IoT Networks and Protocols #Signal Processing (eess.SP) #Telecommunications and Broadcasting Technologies #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2309.14923
openalex publication_date 2023/09/26 · openalex created_date 2023/09/29 · openalex updated_date 2026/07/28
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.