A statistical model for the shadowing induced by human bodies in the proximity of a mmWaves radio link

Cassioli, D. and Rendevski, N. (2014) A statistical model for the shadowing induced by human bodies in the proximity of a mmWaves radio link. In: 2014 IEEE International Conference on Communications Workshops (ICC), 10-14 June 2014, Sydney, NSW, Australia.

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5G technology is a broad concept that describes the envisaged disruptive evolution of communication technology in the near future, with a dramatic increase in the network data-rate and capacity to support a variety of innovative services. The exploitation of the new spectrum available at mmWaves represents a key enabler for 5G. mmWaves are expected to revolutionize the indoor wireless connectivity providing a very large capacity at very high data rates. It is well known that mmWaves radio links are strongly influenced by human bodies and this issue is very relevant in indoor environments. Several models are available for ray-tracing investigations and line-of-sight blockage, whereas statistical models enabling tractable analytical studies and simulations of mmWaves wireless systems, accounting for people in the link's proximity, are still lacking. In this paper, measurements of 60 GHz channel impulse responses in static but “evolutionary” office scenarios that involve one, two and three individuals are presented. Regression fits are applied to the experimental responses to obtain an accurate characterization of human-induced shadowing events in both proximity and blockage situations. Tractable statistical models are provided for different scenarios and for eight carrier frequencies spanning the bands from 54 to 59 GHz and from 61 to 66 GHz.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: 5G, 60 GHz, mmWaves, Shadowing
Subjects: Scientific Fields (Frascati) > Natural sciences > Computer and information sciences
Scientific Fields (Frascati) > Engineering and Technology > Electrical engineering, electronic engineering,information engineering
Divisions: Faculty of Information and Communication Technologies
Depositing User: Prof. d-r. Nikola Rendevski
Date Deposited: 22 Mar 2020 16:24
Last Modified: 22 Mar 2020 16:24
URI: https://eprints.uklo.edu.mk/id/eprint/4262

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