Citation

Tai, Ian Hwa Pien and Lim, Heng Siong and Diong, Kah Seng and Alaghbari, Khaled Ab. Aziz (2023) Effects of Crowd Density on Radio Propagation at 24 GHz in a Pedestrian Tunnel for 5G Communications. IEEE Access, 11. pp. 40240-40248. ISSN 2169-3536

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Abstract

T In this paper, we report the results of radio propagation characterisation in a pedestrian tunnel with different crowd densities at 24 GHz using commercial ray-tracing software called Wireless InSite. The 3D empty tunnel and human body models we created using computer-aided design software and imported into Wireless InSite. The tunnel model is based on a pedestrian tunnel connecting Suria and KLCC, which is located in the heart of Kuala Lumpur. Five three-dimensional (3D) human body models with different levels of detail were developed and tested. The crowd densities investigated were 0, 0.05, 0.1, 0.15 and 0.2 people/m2 which correspond to 0, 25, 50, 75, and 100 people, respectively, in the study area. The results show that the path loss exponent, log-normal shadowing’s standard deviation, and fluctuation in received power increase as the number of people increases. When the crowd density is above 0.1 people/m2 , the path loss exponent of the large-scale path loss model is higher than that of the empty tunnel. The results of this study are also useful for understanding the effects of human crowds on millimetre wave propagation in indoor tunnel-like environments such as hallways, enclosed corridors, mines, and transportation tunnels. The findings contribute to increasing the effectiveness of network planning and deployment for 5G communication, especially in pedestrian tunnels.

Item Type:	Article
Uncontrolled Keywords:	5G communications , human crowd shadowing , millimetre wave , pedestrian tunnel radio propagation , ray-tracing
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	02 Jun 2023 01:29
Last Modified:	02 Jun 2023 01:29
URII:	http://shdl.mmu.edu.my/id/eprint/11450

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