Assessment of the Representativeness of Numerical Subjects for the WBAN Indoor Channel Modeling

• Published in: 2025 19th European Conference on Antennas and Propagation (EuCAP) pp. 1 - 5
• Main Authors: Youssef, Badre, Roblin, Christophe
• Format: Conference Proceeding
• Language: English
• Published: European Association on Antennas and Propagation 30.03.2025
• Subjects: Antennas; Body area networks; Electromagnetics; Europe; indoor on-body channel; Morphology; Numerical models; Phantoms; Simulation; surrounded antenna; Water resources; WBAN channel modeling; Wireless communication
• DOI: 10.23919/EuCAP63536.2025.10999677

Wireless Body Area Networks peculiarities can be summed up in two points: firstly, the high number of sources of variability, i.e. the subject, the antennas, the frequency, the environment, etc.; secondly the strong electromagnetic disturbance of the human body (due to its composition, around 70% of water). Concerning the subject, there are two aspects that can be considered: the morphology and the dynamic. Here, we are interested only on the first one. When this source of variability is considered in the channel modeling, the problem is the difficulty of obtaining a representative statistical sample. One solution would be to use numerical subject models, which would give us a much wider range of subjects than is currently proposed in the literature. Then the purpose of this article is to assess the representativeness of this approach in the context of WBAN channel modeling, by comparing key channel parameters (the mean Path Loss of the on-body cluster PL^{\mathrm{O}\mathrm{n}} and the surrounded antenna pattern) obtained from simulations results for different numerical subjects and experimentations performed with a whole body phantom. In this study, the scenario based approach is preferred and simulations and experimentations are performed in the 1 st UWB band ([3.1, 4.8] GHz). The results obtained are satisfactory, and validate the use of this approach in view of the assumptions exposed for modeling the WBAN channel.