Abstract
The innovation proposed herein leverages machine learning to predict Non-Line-of-Sight (NLOS) conditions in a 5G Non-Terrestrial Network (NTN), significantly enhancing communication reliability in such networks. By dynamically encoding these predictions into user equipment (UE) Route Selection Policies (URSPs), the innovation proposed herein optimizes data transfers, ensuring efficient use of satellite coverage and improving overall network performance with minimal changes to the existing 5G core infrastructure.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Xavier, Alwin; Grobel, Wojciech; Mitrovic, Snezana; and Saini, Vinay, "ENHANCING 5G NON-TERRESTRIAL NETWORK (NTN) COMMUNICATIONS WITH MACHINE LEARNING-DRIVEN NON-LINE-OF-SIGHT (NLOS) PREDICTION AND MITIGATION", Technical Disclosure Commons, (July 22, 2025)
https://www.tdcommons.org/dpubs_series/8382