Raytracing Digital Trees at Millimeter-Wave: Calibration Against 60 GHz Channel Measurements in Summer and Winter

Published

December 19, 2023

Author(s)

Chiehping Lai, Jelena Senic, Camillo Gentile, Nada T. Golmie

Abstract

Accurate channel propagation modeling of foliage is critical to the design and deployment of wireless networks, given its pervasive nature in rural, suburban, and urban environments. Its blockage effects can be particularly devastating at millimeter-wave (mmWave) frequencies, where the size of leaves and branches is comparable to the wavelength of the transmitted signal. While theoretical models are firmly based on electromagnetic principles, reliability can be attained only through calibration of the models against measurements. In the few works that do so, foliage is represented as simple canonical shapes (cylinders, discs, etc.) and calibration is performed against measurements with foliage as part of entire outdoor environments. The controlled approach that we propose in this paper, rather, is based on measurement of single specimens of trees, for precision characterization. To sustain this precision at mmWave, the trees are represented digitally as meshes of faceted leaves and branches that are specific the live trees measured. Predictions from a raytracing model applied to digital twins of seven live trees in summer and in winter are calibrated against measurements collected with a double-directional 60 GHz channel sounder. The tree-specific predictions, which can then be incorporated as part of an entire outdoor environment, are shown to match the measurements very well.

Citation

IEEE Access Journal

Pub Type

Journals

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Keywords

5G, mmWave, foliage, penetration loss, propagation, simulation, tuning, vegetation, wireless networks

Citation

Lai, C. , Senic, J. , Gentile, C. and Golmie, N. (2023), Raytracing Digital Trees at Millimeter-Wave: Calibration Against 60 GHz Channel Measurements in Summer and Winter, IEEE Access Journal, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=936108 (Accessed April 27, 2024)