Physical-statistical channel model for signal effect by moving human bodies

Abstract

A novel physical-statistical channel model for simulating the signal effect by moving human bodies is presented. The human body is modeled as vertically oriented dielectric cylindrical volume. The received signal is assumed to be composed of a direct component which might be subject to shadowing and a multipath component due to reflection and diffuse scattering, i.e., a Ricean channel. The shadowing effect of the direct signal component is calculated using Kirchhoff diffraction equation. The multipath component is parameterized by calculating the reflected fields from the floor, ceiling and walls of the indoor environment as well as scattered fields from moving human bodies. Poisson and Exponential distributions are used to describe the shadowing and inter-shadowing events caused by multiple bodies, respectively. Furthermore, simulation results of the first and second order statistics of the received signal affected by moving human bodies for 3.35 GHz and 60 GHz signals are presented. In addition, initial validation of the developed model are performed using an empirical model for human body shadowing and reported measurement results.

Physical Modeling and Performance Bounds for Device-free Localization Systems

“…In line with the rapidly evolving software-defined radio (SDR) paradigm, the DFL technology is expected to provide a protocol-independent infrastructure that supports sensor-less localization and context recognition services tailored for industrial and smart workspace applications. Despite some recent attempts to model the fading effects induced by moving bodies on shortrange radio propagation [6], these mostly addressed inter- [7] and intra-body [8] body-induced propagation losses for narrow [9] or wide-band [10] applications with the purpose of mitigating these effects. Another important topic discussed so far is modeling of body effects for critical network layout optimization [11].…”

“…On the other hand, the diffraction term provides a simple but effective tool to predict the power perturbation as a function of the target position and size. For the sake of simplicity, here we approximate the expectations in (5)- (6) by assuming that the target orientation can only take its extreme values with equal probabilities . Under these assumptions, and can be expressed as…”

Physical-Statistical Channel Model for Off-Body Area Network

“…Due to their canonical shapes, the uniform theory of diffraction (UTD) will be used to calculate, by electromagnetic computation (physically), the time-varying signal contributions from the human body. UTD can also account for the creeping waves which cannot be predicted with geometrical optics and Kirchhoff diffraction equation [9,11]. The environmental contributions to the total received signal causes multipath fading, and will be described statistically using a Rayleigh distribution.…”

“…Generally, physical-statistical models are more accurate than merely empirical models as they rely on electromagnetic based methods for calculating the needed model parameters. They are also more effective for simulating large scenarios compared to purely physical models [9].…”

Measurement Evaluation of the TGN Radio Channel Models Usefulness in Predicting Wlan Performance

“…For this reason, for distances smaller than d BR the attenuation slope is 20 dB/decade whereas for distances greater than d BR the attenuation slope is 35 dB/decade. The model can also take into account the shadow fading (SF) coefficient to compensate for the signal fast fading (see also [26][27][28][29]). Parameters for the pathloss model are presented in Table 1.…”