Hybrid Modeling of Capacitive Human Body Communication Systems to Capture Channel Variability

The purpose of this contribution is to estimate the path-loss of capacitive human body communication (cHBC) systems under varying channel conditions.

Accurate modeling of cHBC channel characteristics is critical for system design, and yet, channel characteristics are a complex function of environment, electrode geometries, and posture that are difficult to model. Although full-field electromagnetic simulations can accurately capture such complexity, they are slow to run, making it challenging to assess channel statistics quickly. This paper presents a hybrid model that combines the strengths of full-field electromagnetic simulations, measurements, and circuit model analysis to enable rapid, yet accurate, assessments of cHBC channel characteristics across all the aforementioned variations, with a focus on posture variations as the most common source of channel variation.

The proposed model is investigated using a combination of analytical and simulation-based approaches. Experiments with miniaturized battery-operated devices are also conducted to validate the model's results. Simulation results show agreement with measurements across channel variations, with the worst-case mean error magnitude $< $ 2.40 dB.

Channel variations in cHBC can be significant and pose a major challenge in predicting channel behavior. Therefore, a model that can accurately estimate the channel loss both across frequencies and channel variations is imperative.

cHBC is an attractive candidate for building a more secure and energy-efficient body area network. This modeling approach supports a more reliable link budget calculation under varying cHBC channel characteristics, which helps inform efficient designs of cHBC circuits under more realistic use cases.