Ray Tracing Prediction of Path Loss for 5G Wireless Communications within Hospital Patient Room

Ghaddar, M.; Mabrouk, I. Ben; Garcia-Pardo, J.M.; Lienard, M.; Degauque, P.

Abstract

Millimeter (mm)-waves band has become a promising option for fifth-generation (5G) near-infinite data rate and ultra-low latency communications. Despite all recent achievements, engineers are still facing technical challenges for the assessment of indoor propagation mechanisms at extremely short wavelengths. In this paper, a mathematical-based deterministic Ray-Tracing Model (RTM) has been derived for the prediction of Path Loss (PL) within a hospital patient room while ensuring users freedom of mobility. Directional, and omnidirectional radiation patterns have been considered at both transmitting and receiving ends of the channel, respectively. For the RTM validation, broadband propagation measurements have been conducted under Line-Of-Sight (LOS) condition. The predicted and measured channel responses are found to be in a good match for all R X positions throughout the room. Furthermore, both predicted and measured path loss exponents (n) are found to be about 1.55. The predicted n agrees with the values reported in the literature.

Citation

Ghaddar, M., Mabrouk, I. B., Garcia-Pardo, J., Lienard, M., & Degauque, P. (2022, July). Ray Tracing Prediction of Path Loss for 5G Wireless Communications within Hospital Patient Room. Presented at 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI), Denver, Colorado, USA

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI)
• Start Date: Jul 10, 2022
• End Date: Jul 15, 2022
• Online Publication Date: Sep 21, 2022
• Publication Date: 2022
• Deposit Date: May 26, 2023
• Publisher: Institute of Electrical and Electronics Engineers
• Pages: 760-761
• Series ISSN: 1947-1491
• DOI: https://doi.org/10.1109/ap-s/usnc-ursi47032.2022.9886049
• Public URL: https://durham-repository.worktribe.com/output/1133976