RIS-Assisted Coverage Optimization for 5G-R Channel in Station Scenario Based on ML and RT

Abstract

As one of the most important radio communication scenarios in vertical industry, the high-speed railway (HSR) station is facing the challenge of coverage optimization due to its complex structure. Regarding the wireless network planning and optimization of HSR stations as a part of the customized network, this paper makes an analysis on the 5G-R channel in the HSR station scenario. Channel characteristics, including path loss, power ratio (PR), and angular spread (AS), are extracted on the basis of ray tracing (RT). Multipath components can also be distinguished based on RT. In order to achieve a better performance, the reconfigurable intelligent surface (RIS) technology is adopted to the network deployment. Moreover, machine learning (ML) is used to locate the best direction of the beam. The analysis results show that the received power in RIS-assisted channels is significantly promoted. Our research can provide a deep understanding to the 5G-R channel in station scenario and a well reference for the design and optimization of the customized network.