Machine-Learning-Based LOS Detection for 5G Signals with Applications in Airport Environments.

ABSTRACT: The operational costs of the advanced Air Traffic Management (ATM) solutions are often prohibitive in low- and medium-sized airports. Therefore, new and complementary solutions are currently under research in order to take advantage of existing infrastructure and offer low-cost alternatives. The 5G signals are particularly attractive in an ATM context due to their promising potential in wireless positioning and sensing via Time-of-Arrival (ToA) and Angle-of-Arrival (AoA) algorithms. However, ToA and AoA methods are known to be highly sensitive to the presence of multipath and Non-Line-of-Sight (NLOS) scenarios. Yet, LOS detection in the context of 5G signals has been poorly addressed in the literature so far, to the best of the Authors' knowledge. This paper focuses on LOS/NLOS detection m

SUBMITTER: Jayawardana PADN 

PROVIDER: S-EPMC9920163 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature