Statistical Modeling of Scenario-Based Indoor WBAN Channels

• Published in: IEEE transactions on antennas and propagation Vol. 72; no. 8; pp. 6549 - 6560
• Main Authors: Youssef, Badre, Roblin, Christophe, Sibille, Alain
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Anechoic chambers; Antennas; Body area networks; Classification; Computational efficiency; Computational modeling; Construction industry; Electromagnetism; Engineering Sciences; experimental design (ED); Indoor environments; indoor on-body channel; Radio links; Ray tracing; ray tracing (RT); Reflection; Statistical analysis; statistical modeling; Statistical models; Surface waves; wireless body area network (WBAN) channel; Wireless communication; Wireless communication; Surface waves; ray tracing (RT); wireless body area network (WBAN) channel; Computational modeling; Body area networks; Radio links; statistical modeling; Reflection; experimental design (ED); Antennas; indoor on-body channel
• ISSN: 0018-926X; 1558-2221; 1558-2221
• DOI: 10.1109/TAP.2024.3421369

This article presents a parametric statistical path loss (PL) model for wireless body area network (WBAN) communications in the context of a scenario-based approach for indoor environments. One of the specificities of WBANs is their numerous sources of variability (subject motion and morphology, antennas, local environment, and so on). We focus here on the influence of the environment, in the case of empty rooms. The model, developed for the first ultra wide band (UWB) sub-band (<inline-formula> <tex-math notation="LaTeX">B = </tex-math></inline-formula> [3.1, 4.8] GHz), takes into account the sizes of the rooms (assumed to be parallelepipedic and empty) and the wall characteristics (via an average reflectivity coefficient). They also involve an elaborate categorization of environments. The following methodology was implemented, to avoid time-consuming and complex experimental campaigns while still having a relatively representative and sufficient number of statistical samples: first, a simplified ray tracing (RT) code enabled a large number of different rooms to be sampled at moderate computational cost; second, part of these simulations was supported by anechoic chamber measurements; and third, the simulations were carried out using elaborate experimental designs (EDs), based on a categorization of environments and a fairly comprehensive study of building industry data. The parametric PL models obtained significantly reduce their variance.