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

- Michael Cheffena
^{1}Email author

**2012**:77

https://doi.org/10.1186/1687-1499-2012-77

© Cheffena; licensee Springer. 2012

**Received:**5 December 2011**Accepted:**1 March 2012**Published:**1 March 2012

## 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.

## Keywords

- channel model
- indoor propagation
- fading
- human body

## 1. Introduction

Wireless communication systems are subject to different propagation impairments, among them are signal shadowing by moving human bodies. For wireless systems operating in indoor environments, human body shadowing is a significant propagation impairment. Unlike to outdoor cellular systems, the transmission power and the height of base-stations is much lower in indoor environments. Thus, the signal loss by body shadowing greatly affects the received signal strength. Furthermore, giving a fixed location of the transmitting and receiving antennas, the random movement of people may cause a time varying channel conditions.

A path-shadowing model for indoor environment using Markov process is reported in [1]. In [2, 3], numerical examination of electromagnetic wave shadowing by human body using method-of-moment (MoM) solutions are presented. A two-state Ricean model for predicting the indoor wireless channel was reported in [4]. An empirical model for describing the large-scale fading pattern of the signal affected by the human body was presented in [5]. Furthermore, reference [6] presents propagation measurement results in the presence of human activity for a 60 GHz channel. In [7], the effect of human body shadowing on the multiple-input-multiple-output (MIMO) channel performance were investigated.

In this article, we propose a physical-statistical channel model for signal effect by moving human bodies. Physical-statistical models are more effective for simulating large scenarios compared to complex physical models, and more accurate than merely empirical or statistical models as they take into account the geometry of the link, and rely on physical (electromagnetic based) methods for calculating the needed model parameters.

The received signal is assumed to be composed of a direct component which might be subject to shadowing and a multipath component due to diffuse scattering, i.e., a Ricean channel. The direct signal component is subject to shadowing effects, and 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 which are modeled as finite dielectric cylinders. The parameters of the Rice distribution change as the people represented in terms of simple volumetric forms walks through the propagation environment. Furthermore, Poisson and Exponential distributions are used to describe the shadowing and inter-shadowing events caused by multiple bodies, respectively.

The article begins in Section 2 by identifying the main propagation mechanisms for moving human body effects in indoor environment, and presents the developed physical-statistical channel model. Simulation results and discussions are given in Section 3. Initial validation of the proposed model using an empirical model for human body shadowing and reported measurement results are shown in Section 4. Finally, conclusions are presented in Section 5.

## 2. Physical-statistical channel model

**x**,

**y**, and

**z**of a rectangular coordinate system. The human body is modeled as a finite lossy dielectric cylinder and is walking in the direction ABC or CBA crossing the line-of-sight (LOS) path between the transmitter (Tx) and the receiver (Rx) at position B. The positions of the transmitting and receiving antennas are fixed.

In addition to the reflection of the signal by the floor, ceiling and walls of the indoor environment (reflections from furniture and other objects are not considered), the signal is also scattered by moving human body at, e.g., position A and C (see Figure 1). As the person approaches position B, the signal is subject to both scattering and diffraction from the top and side of the human body. Note that the strength of the scattered and diffracted signals are position dependent and change as the person walks though the propagation environment. Furthermore, the random movement of people create time varying channel conditions. Characterization of the Doppler spectra is thus important for the determination of the time variance of the wireless channel. Other important parameters are the shadowing and inter-shadowing intervals which are needed when simulating the crossing of the LOS path by multiple bodies. In subsequent sections, we discuss each of the aforementioned propagation mechanisms for signal effect by moving human bodies.

### 2.1. Reflection and scattering

*R*

_{ N }and

*R*

_{ P }are the reflection coefficients when the electric field component is normal and parallel to the reflection plane, respectively. Parameter

*θ*

_{ r }is the angle between the incident ray and the normal to the reflecting surface. Parameter

*η*is the complex permittivity of the reflecting material. The received reflected signal power can then be calculated using

where *P*_{
t
}is the transmitted power, *G*_{
t
}and *G*_{
r
}are transmitting and receiving antenna gains, respectively. Parameter *L*_{
p
}is the path lose and *L*_{
r
}= |*R*|^{2} is the reflection lose with *R* = *R*_{
P
}, *R*_{
N
}.

**x**,

**y**, and

**z**. The radios

*R*and the height

*H*describe the dimension of the volume. Incident on the human body is a linearly polarized wave with wavenumber

*k*= 2

*π*/

*λ, λ*being the wavelength, and direction of propagation

**k**

_{ i }. The transmitter is assumed to be located far away from the human body, so that the incident field amplitude can be approximated as being constant over the human body. Similarly, the scattered wave is received at a point located far away from the human body, in the direction

**k**

_{ s }relative to the center of the human body. The propagation vectors of the incident

**k**

_{ i }and scattered

**k**

_{ s }fields are given by (see Figure 2)

where angles *θ*_{
i
}and *ϕ*_{
i
}are the elevation and azimuth directions of the incident field. Angles *θ*_{
s
}and *ϕ*_{
s
}are the elevation and azimuth directions of the scattered field.

*F*(

**k**

_{ s },

**k**

_{ i }) [9]. If the field inside the cylinder is estimated as the field inside a similar, but infinite cylinder, then

*F*(

**k**

_{ s },

**k**

_{ i }) can be expressed as in [9, Equation (47)]. The scattering cross-section can then be calculated using

For the propagation scenario shown in Figure 1, at each location of the human body, the incident and scattering angles can be calculated using geometrical relations, and these can be used further to determine the scattered signal utilizing the scattering cross-section defined in Equation (6).

The multipath (fast fading) component of the Rice channel in indoor environment might then be described by the sum of the standard deviations of the reflected and scattered fields, *σ*_{reflected} + *σ*_{scattered}. Together with the direct signal component (discussed in Section 2.2), these are used in Section 2.5 to generate the signal fading caused by moving human body.

### 2.2. Diffraction

*P*, and the free-space electric field

*O*is the intersection point (with Cartesian coordinates

*x*

_{o}and

*y*

_{o}) of the Tx-Rx straight line with the obstacle (see Figure 3). Parameter Σ(

*u,v*) is the surface of the aperture, and

*R*

_{1}is the radius of the first Fresnel zone given by

where *a* and *b* are the distances from the transmitter and the receiver to the aperture plane.

The total received diffracted field is then the combination of the three aperture diffracted fields whose surfaces are Σ_{1}, Σ_{2}, and Σ_{3} with assumed infinite depth. Unlike simple ray tracing and Markov process models, the transitions between LOS and non-line-of-sight (NLOS) conditions are not sharp and thus more realistic regarding the empirical results. Using this model, good estimation of the shadow region behind a human body might be obtained. The standard deviation of the diffracted field, *σ*_{diffracted}, is used to describe the direct (slow fading) component of the received signal affected by moving human body. Together with the multipath signal component (discussed in Section 2.1), *σ*_{diffracted} is used in Section 2.5 to generate the signal fading caused by moving human body.

### 2.3. Doppler spectrum

The random movement of people in the propagation environment create time varying channel conditions. Characterization of the Doppler spectra is thus important for the determination of the time variance of the wireless channel. The situation where the antenna is moving in a random environment leads to the classical Jakes spectrum (with bathtub like shape) for scatters uniformly distributed in azimuth [12]. For the case where the antenna is stationary, moving scatterers in the channel such as vehicles (in outdoor environment) or persons (in indoor environment) will lead to a different Doppler spectrum which peaks at 0 Hz and falls off rapidly [13, 14].

*P*

_{n- 0.5}is the

*n*th order Legendre function. In [15], a strong correlation was found between the shape of the scattering pattern and the Doppler spectrum. Based on this, a simplified model which mimics the measured spectra caused by randomly moving people is proposed, and is given by [15]

where *e* is a model constant with value equal to 0.02 chosen to much the measured Doppler spectrum [15].

### 2.4. Shadowing and inter-shadowing events

where $\hat{N}$ is the average number of bodies crossing the LOS path. Equation (12) gives the probability that *N* persons cross the LOS path within a given period of time.

where *T* is the time interval to the next shadowing event. Equation (13) gives the probability of *T* time interval for the next crossing of the LOS path. Parameter $\hat{N}$ depends on the type of the environment, i.e., indoor (office, home, airport terminal, etc.) or outdoor (urban, suburban, rural, etc.), and should be characterized using measurements for the considered propagation environment.

### 2.5. The simulation model

*σ*

_{reflected}+

*σ*

_{scattered}(see Section 2.1) to produce the multipath (fast fading) component of the received signal. Then, the standard deviation of the diffracted field,

*σ*

_{diffracted}(see Section 2.2) representing the direct (slow fading) signal component, is added to produce the total complex signal envelope of the received signal affected by moving human body.

This results in physical-statistical modeling of the propagation channel where the received signal envelope is Rice distributed with its position dependent parameters calculated using electromagnetic based methods (i.e., reflection, scattering, and diffraction). Note that parameters *σ*_{scattered} and *σ*_{diffracted} are position dependent, and change as the person walks through the propagation environment. Furthermore, Poisson and Exponential distributions are used to generate the shadowing and inter-shadowing events which can be used to simulate the crossing of the LOS component by multiple moving human bodies (see Section 2.4).

## 3. Simulation results and discussions

*ϕ*

_{ i }= 180°, so the forward scattering direction is

*ϕ*

_{ s }= 0°. We can observe that the forward lobe of the scattering pattern becomes narrower with decreasing background as the frequency increases. This results in decreasing the received scattered fields in directions other than the forward direction.

Simulation parameters

Parameter | Value |
---|---|

Frequency, | 3.35 GHz, 60 GHz |

Tx antenna height, | 1 m |

Rx antenna height, | 1 m |

Average human body radius, | 0.18 m |

Average human body height, | 1.80 m |

Average number of crossing, $\hat{N}$ | 3 |

Average walking speed, | 1 m/s |

Human body relative permittivity at 3.35 GHz, | 50-j100 [19] |

Human body relative permittivity at 60 GHz, | 11-j4.5 [20] |

Relative permittivity of the floor, ceiling and wall at 3.35 GHz, | 5.31-j0.47 [8] |

Relative permittivity of the floor, ceiling and wall at 60 GHz, | 5.31-j0.27 [8] |

*v*, and radius,

*R*, of the people crossing the LOS path in a given environment. One way is to use an average values of

*v*and

*R*as done in our simulations. A different approach might be to generate random values of

*v*and

*R*for each body crossing the LOS path from statistical distributions. In this case, statistical knowledge of

*v*and

*R*are required.

*N*= 1, 2, ..., 5 persons). The same is shown in Figures 10, 11, and 12 for 60 GHz signal. Again we can observe that as the frequency increases the effect of moving human bodies on the first and second order statistics of the channel increases. Generally, as the frequency increases the wavelength decreases and thus small change of the path length may result in fast variations of the channel. From Figures 7 and 10 we can see that as the number of pedestrians increase (for

*N*= 1, 2, ..., 5), the slope of the CDFs of the received signal decreases proportionally. Similarly, as the number of pedestrians increase, the LCRs and AFDs of the received signal increases, see Figures 8, 9, 11, and 12. These results are consistent with the measurement results of human body effects reported in [18].

## 4. Model validation

where *A*_{
s
}represents the maximal shadowing attenuation in dB, *T*_{
s
}is the shadowing duration in seconds, *t*_{0} is the shadowing instance, and *u* is a model constant.

In this section, an attempt has been made to initially validate the developed physical-statistical channel model using an empirical model for human body shadowing and reported measurement results. Further validation of the proposed model will be performed in the future as radio frequency (RF) measurements are made available.

## 5. Conclusion

A novel physical-statistical generative model for signal effect by moving human bodies is developed. 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. The parameters of the Rice distribution thus change as the people walk thought the propagation environment.

The shadowing and inter-shadowing events caused by multiple crossing of the LOS path are modeled using a Passion and Exponential distribution, respectively. The physical-statistical approaches discussed are more effective for simulating large scenarios compared to complex physical models, and more accurate than merely empirical or statistical models as they take into account the geometry of the link, and rely on electromagnetic based methods for calculating the needed model parameters.

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. The results show that as the frequency increases the effect of moving human bodies on the first and second order statistics of the channel increases. Moreover, as the number of pedestrians increase, the slope of the CDFs of the received signal decreases proportionally. Similarly, as the number of pedestrians increase, the LCRs and AFDs of the received signal increases.

An attempt has been made to initially validate the developed model using an empirical model for human body shadowing and reported measurement results. Further validation of the proposed model will be performed in the future as RF measurements are made available.

In general, the proposed model can be used to generate time series of moving human body effects for indoor wireless access systems. This enables simulation of for example capacity-enhancing techniques such as route diversity, space-time coding and adaptive coding and modulation.

## Declarations

### Acknowledgements

The author would like to thank Gjøvik University College, Norway for supporting this study. This study was supported by Gjøvik University College, Norway.

## Authors’ Affiliations

## References

- Kashiwagi I, Taga T, Imai T: Time-varying path-shadowing model for indoor populated environments.
*IEEE Trans Veh Tech*2010, 59(1):16-28.View ArticleGoogle Scholar - Yokota M, Ikegamai T, Ohta Y, Fujii T: Numerical examination of EM wave shadowing by human body. In
*Proc 4th European Conf Ant Prop (EUCAP2010)*. Barcelona; 2010.Google Scholar - Eid AM, Wallace JW: Accurate modeling of body area network channels using surface-based method of moments.
*IEEE Trans Ant Prop*2011, 59(8):3022-3030.View ArticleGoogle Scholar - Robert JA, Abeysighe JR: A two-state Rician model for predicting indoor wireless communication performance. In
*Proc IEEE Int Conf Commun*.*Volume 1*. Seattle, WA; 1995:40-43.View ArticleGoogle Scholar - Pagani P, Pajusco P: Chracterization and modeling of temporal variations on an ultrawideband radio link.
*IEEE Trans Ant Prop*2006, 54(11):3198-3206.View ArticleGoogle Scholar - Collonge S, Zaharia G, Zein GE: Influence of the human activity on wide-band characteristics of the 60 GHz indoor radio channel.
*IEEE Trans Wirel Commun*2004, 3(6):2396-2406. 10.1109/TWC.2004.837276View ArticleGoogle Scholar - Torre PV, Vallozzi L, Jacobs L, Rogier H, Moeneclaey M, Verhaevert J: Characterization of measured indoor off-body MIMO channels with correlated fading, correlated shadowing and constant path loss.
*IEEE Trans Wirel Commun*2012, 11(2):712-721.View ArticleGoogle Scholar - Propagation data and prediction methods for the planning of indoor radiocommunication systems and radio local area networks in the frequency range 900 MHz to 100 GHz Geneva; 2009.Google Scholar
- Karam MA, Fung AK, Antat YMM: Leaf-shape effects in electromagnetic wave scattering from vegetation.
*IEEE Trans Geosci Remote Sens*1989, 27(6):687-697. 10.1109/TGRS.1989.1398241View ArticleGoogle Scholar - Hristov HD:
*Fresnel Zones in Wireless Links*. Zone Plate Lenses and Antennas, Artech House; 2000.Google Scholar - Fontan FP, Abele A, Montenegro B, Lacoste F, Fabbro V, Castanet L, Sanmartin B, Valtr P: Modelling of the land mobile satellite channel using a virtual city approach. In
*Proc 2nd European Conf Ant Prop (EUCAP2007)*. Edinburgh; 2007.Google Scholar - Clarke RH: A statistical theory of mobile radio reception.
*Bell System Tech J*1968, 47: 957-1000.View ArticleGoogle Scholar - Bultitude RJC: Measurement characterization and modeling of indoor 800/900 MHz radio channels for digital communications.
*IEEE Commun Meg*1987., 25(6):Google Scholar - Howard SJ, Pahlavan K: Doppler spread measurements of indoor radio channel.
*Electron Lett*1990, 26(2):107-109. 10.1049/el:19900074View ArticleGoogle Scholar - Andersen JB, Nielsen JO, Pedersen GF, Bauch G: Doppler spectrum from moving scatterers in a random environment.
*IEEE Trans Wirel Commun*2009, 8(6):3270-3276.View ArticleGoogle Scholar - Promislow DS:
*Poisson Processes, in Fundamentals of Actuarial Mathematics*. 2nd edition. John Wiley and Sons Ltd., Chichester, UK; 2010.View ArticleGoogle Scholar - LLC-Books, Poisson Processes:
*Exponential Distribution, Shot Noise, Poisson Process, Radioactive Decay, Poisson Distribution.*Proportional Hazards M. Books LLC, Wiki Series; 2010.Google Scholar - Ziri-Castro KI, Evans NE, Scanlon WG: Propagation modelling and measurments in a populated indoor environment at 5.2 GHz. In
*Auswirel Conf*. Sydney; 2006.Google Scholar - Johanson CC, Guy AW: Nonionizing electromagnetic wave effects in biological materials and systems.
*Proc IEEE*1972, 60(6):692-718.View ArticleGoogle Scholar - Chahat N, Zhadobov M, Augustine R, Sauleau R: Human skin permittivity models for millimetre-wave range.
*Electron Lett*2011, 47(7):427-428. 10.1049/el.2011.0349View ArticleGoogle Scholar

## Copyright

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.