International Journal of Antennas and Propagation

International Journal of Antennas and Propagation / 2012 / Article
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Radio Wave Propagation and Wireless Channel Modeling

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Research Article | Open Access

Volume 2012 |Article ID 150267 |

Miyuki Hirose, Hironobu Yamamoto, Takehiko Kobayashi, "Statistical Modeling of Ultrawideband Body-Centric Wireless Channels Considering Room Volume", International Journal of Antennas and Propagation, vol. 2012, Article ID 150267, 10 pages, 2012.

Statistical Modeling of Ultrawideband Body-Centric Wireless Channels Considering Room Volume

Academic Editor: César Briso Rodríguez
Received31 Jul 2012
Accepted12 Oct 2012
Published22 Nov 2012


This paper presents the results of a statistical modeling of onbody ultrawideband (UWB) radio channels for wireless body area network (WBAN) applications. Measurements were conducted in five different rooms. A measured delay profile can be divided into two domains; in the first domain ( ns) there is either a direct (for line of sight) or diffracted (for nonline of sight) wave which is dependent on the propagation distance along the perimeter of the body, but essentially unrelated to room volume, and the second domain ( ns) has multipath components that are dominant and dependent on room volume. The first domain was modeled with a conventional power decay law model, and the second domain with a modified Saleh-Valenzuela model considering the room volume. Realizations of the impulse responses are presented based on the composite model and compared with the measured average power delay profiles.

1. Introduction

Wireless onbody area communication technologies are significant for both medical and nonmedical applications. Ultrawideband (UWB) technologies have been considered for use in wireless body area networks (WBANs) because of their possible low power consumption and antimultipath capabilities. Numerous studies have been carried out on UWB propagation characterization and the modeling of indoor UWB communication channels. A number of measurements relating to WBAN have been carried out to characterize and model on- and offbody UWB propagation in either a radio anechoic chamber or a specific room type [14]. The conventional UWB propagation loss model in these studies, however, did not consider the impact of surrounding environments. Since multipaths (in particular, the reflected waves from floors, walls, and ceilings) depend strongly on room volume, it is necessary to evaluate the variation of propagation characteristics in various environments. To address this problem, we measured UWB (3.1–10.6 GHz) radio propagation around the human body in a radio anechoic chamber and four different rooms and proposed a new UWB propagation loss model depending on the room volume [5]. In this study, time-domain statistical channel model will be the presented based on the same measurement campaign as [5].

As for statistical modeling of the channel impulse response, Fort et al. [3] separated the WBAN propagation channels into two parts: (1) diffraction around the body and (2) reflections off of nearby scatterers then back at the body, and modeled the second part using a modified Saleh-Valenzuela (SV) model [6]. The applicable area of the modified SV model in [6], however, was limited to wireless personal area networks not including human bodies. Roblin [7] scrutinized the separability of channels for various scenarios in three different rooms, concluded that UWB channels can be separated in the case of a relatively larger room, but it has not established a channel model. We also divided the channel responses into two parts which were then modeled by power decay law and a modified SV model depending on the room volume.

2. Measurement Setup

The measurement campaigns were conducted in five parallelepiped rooms as shown in Figure 1. The dimensions of Room A (a radio anechoic chamber) were measured between the apexes of the radio absorbers paneled on all surfaces. The radio anechoic chamber can be considered as a room extending to an infinite volume (i.e., free space) in terms of radio propagation. Rooms B to E were made of reinforced concrete, and their floors, walls, and ceilings were mostly covered with, respectively, linoleum, wallpaper, and plasterboard, all of which were lossy dielectrics. The measurements were carried out using a human subject (adult male, 1.72 m tall and 56 kg). The subject stood upright with the feet shoulder width apart in either a quiet zone of the radio anechoic chamber or the center of Rooms B to E. The UWB (3.1–10.6 GHz) propagation losses were measured with a vector network analyzer (VNA) between onbody meander line antennas [8]. The voltage standing wave ratio of the antennas was less than 2.5 between 3.1 and 10.6 GHz, and the omnidirectionality in the horizontal plane was within 3 dB in a free space. The transmitting antenna was fixed on the center back waist of the subject and placed at a height of 1.0 m from the floor, as shown in Figure 2. The receiving antenna was placed at approximately 100 mm intervals on the torso. Both antennas were vertically polarized and separated 10 mm from the subject body. When the receiving antenna was placed on the back of the subject’s body, the path was roughly line of sight (LOS), and when on the front, it was non-LOS (NLOS). In total 69 receiving points around the torso were employed. The transmitting and receiving antennas were fed via coaxial cables, perpendicular to each other in configuration without crossing to reduce undesired cable coupling [9]. The calibration was conducted between the feeding points with a coaxial through adaptor. The frequency-domain transfer function (size = 1,024 = 751 measured within the 7.5-GHz bandwidth + 273 zero padding) was inversely Fourier transformed into a delay profile with the use of a rectangular window. Major specifications of the measurements are listed in Table 1.

Bandwidth3.1–10.6 GHz
Frequency sweeping points by VNA751 points, 10-MHz interval
CalibrationInternal function of the VNA
AntennasMeanderline UWB antennas [8]

3. Measurement Results and Modeling

Examples of the delay profiles when the receiving antenna was placed on the center chest (NLOS) and the back side (LOS) of the subject are presented in Figure 3. An increase in total received power was observed when the room volume was decreased (see Appendix A). This was attributed to the more affluent multipaths from the nearby floor, walls, and ceiling in Rooms B to E. The dominant propagation path in Room A (the radio anechoic chamber) was either a direct or a diffracted (around the body) wave, and thus the total reception power is lower than that in the other rooms. With decreasing room volume, mean free path lengths decreased, the power component contained in the multipaths increased, and consequently the total received power increased.

3.1. Division of Propagation Channels

A delay profile can be treated by dividing it into two domains, in the same way as [3, 7]: the first (approximately arriving time  ns) and second ( ns) domains, as schematically shown in Figure 4. The first domain represents the contribution of the human alone, consisting of either direct (for LOS) or diffracted (for NLOS) wave measured in free space or radio anechoic chambers. And the second domain represents the contribution of the surrounding environments, consisting of remaining multipath components, which depend on room volume. Justification for dividing the profiles at  ns is given in Appendix  B.

3.2. Statistical Analysis of the First Domain

The channel response in the first domain ( ns) can be represented by where is the propagation gain at the reference distance  ( m), is the propagation distance along the perimeter of the body, is the propagation loss exponent, is the arrival time of the first wave, and is the Dirac delta function. The arrival time is proportional to . Equation (1) represents a special case (when the room volume ) of the previously proposed UWB propagation loss model depending on room volume [5] (see Appendix C). The values of and in (1) were found to be 4.3 × 10−4 and 3.8 for LOS and 3.2 × 10−5 and 5.1 for NLOS, respectively, from the data of PLdB shown in Figure 5. The statistics of the followed lognormal distribution with a standard deviation of 4.4 dB ( dB) and 3.4 dB ( dB), for LOS and NLOS, respectively, where the values in the parentheses indicate 95% confidence intervals.

3.3. Statistical Analysis of the Second Domain

The second domain ( ns) can be represented by a modified SV model [6] based on a cluster concept of rays: where are the multipath gain coefficients, is the delay of the th cluster, and is the delay of the th multipath component relative to the th cluster arrival time (). Delay profiles measured in Rooms B, C, D, and E indicated that rays arrived in clusters, as shown in Figure 6, where the abscissas of the graphs are drawn in antilogarithm. While Fort et al. stated cluster interval times fit to the Weibull distribution [3], in all our cases, the arrival time intervals of the clusters were found to follow an exponential distribution by using Kolmogorov-Smirnov (K-S) test with a 95% confidence interval. This means that cluster arrivals are modeled as a Poisson arrival process with a fixed rate of [1/ns]. Within each cluster, subsequent rays also arrived according to a Poisson process with another fixed rate of [1/ns]. The distribution of the cluster and ray arrival times are given by where and are cluster arrival rate and ray arrival rate within each cluster, respectively. The IEEE 802.15.4a channel model [6] used a lognormal distribution rather than a Rayleigh distribution adopted in the original S-V model [10] for the multipath gain coefficients We also adopted a lognormal distribution for because of a better fitting to the measured data. The average power of both the clusters and the rays within the clusters are assumed to decay exponentially, such that the average power of the multipath component at a given delay is given by where is the expected value of the power of the first arriving multipath component, is the delay exponent of the clusters, and is the decay exponent of the rays within a cluster. The first arriving multipath detected in measured delay profiles is lower with decreased room volume, as shown in Figure 7. The first multipath component, , can be represented by The values of and are listed in Table 2.



The values of , , , and were derived from the delay profile data measured in Rooms B, C, D, and E. Figures 8 and 9 present those values against along with regression lines. Note that represents the mean free path length of the rays traveling within a room having a finite (or infinite) volume . The cluster arrival time rate [1/ns] is approximately 0.08, while the ray arrival time rate [1/ns] is 0.4 for both LOS and NLOS. While the arrival rates and exhibited no apparent dependence on or LOS/NLOS scenarios as shown in Figure 8, the power decay factors and slightly increased with , as shown in Figure 9. The propagation distances (and therefore propagation losses) of rays increase with the room volume, and therefore the decay factors increase. The slope was steeper for the NLOS than for the LOS cases. The dependence of the cluster power-decay factor and the ray power-decay factor on , depicted in Figure 9, is formulated by where and are values of and when imaginarily , and are the slope of the cluster and the ray within the cluster against , respectively. The values of , , , and are listed in Table 3. Although the effect of shadowing has not been considered in this paper, it can be included in (2) after the same method as adopted in [6].


Cluster [ns/m]1.250.70

Ray [ns/m]0.280.11

4. Realization of Onbody UWB Channels Based on the Composite Model

A composite statistical UWB channel model between onbody antennas is formulated by summing the models described in Section 3. A realization is calculated upon providing input data—whether the path is either LOS or NLOS— (the distance between the antennas along the perimeter of the body), and the room volume, as shown in Figure 10. Once a number of realizations of the channel responses are calculated randomly, they are then served to estimate transmission performances (e.g., average bit error rates) and/or system capacity of communication systems, detection and false alarm rates of radar systems, and so forth, by simulation.

Examples of the channel response realizations for LOS and NLOS, assuming  mm for LOS and 450 mm for NLOS and  m3, are presented in Figure 11, where 20 realizations are overwritten. Average power delay profiles (APDPs) for LOS and NLOS were derived from these realizations and compared with the measured data. Moving average was conducted over a 3 ns period for calculating the APDPs. The APDPs derived from the calculated realizations and from the measured delay profiles reasonably agree, as shown in Figure 12. The validity of the proposed composite model was therefore confirmed.

5. Conclusions

In this study, a series of propagation measurements campaign were carried out between onbody antennas in five different rooms. A measured delay profile can be divided into two domains. In the first domain ( ns), there is either a direct (for LOS) or diffracted (for NLOS) wave which depends on propagation distance along the perimeter of the body but essentially unrelated to room volume. This domain was modeled with a power decay law against the distance, and its amplitude followed a lognormal distribution. In the second domain ( ns), multipath components are dominant and dependent on room volume. Observations of the second domain indicate that rays generally arrive in clusters. Arrivals of clusters and rays within each cluster were found to be modeled by Poisson processes. As a result, the second domain was modeled by a modified Saleh-Valenzuela model with the use of lognormal distribution rather than Rayleigh distribution for multipath gain coefficients. Finally, the composite model to calculate the UWB onbody channel realizations was obtained by combining the two domains and validated with the use of the measured delay profiles.


A. UWB Propagation Loss

Examples of spatial distributions of UWB propagation losses, measured in the same five rooms as those described in Section 2, are shown in Figure 13. The UWB propagation losses were calculated by integrating the power of the losses between the feeding points of the antennas over occupied bandwidth: where is the propagation loss in dB measured at frequency , and and are the lowest and highest frequencies. The propagation losses increased with decreasing the room volume, as shown in Figure 13.

B. Validity of 4 ns for Dividing the Delay Profiles

Figure 14 depicts average power delay profiles (averaged over 3 ns) for LOS and NLOS measured in Rooms A to E.

Curves are almost equal for a period between 0 and approximately 4 ns: the effect of the surrounding environment was insignificant up to 4 ns. Beyond the 4 ns, the propagation loss decreased (the curves move upward) with decreasing room volume. Furthermore, the amplitude distribution was examined to confirm the validity of  ns for dividing the delay profiles. The amplitudes within the measured delay profiles were found to follow lognormal distribution up to an excess delay of 10 ns. The averages in the lognormal distribution up to 3, 4, 5, and 7 ns were estimated for LOS and NLOS, as shown in Figure 15, where the 95% confidence intervals derived of Room A data are plotted by dashed lines. While all the averages up to 4 ns for Rooms B to E fell within the 95% intervals, some (Rooms C, D, and E for LOS and Rooms D and E for NLOS) were outside the intervals, as shown in Figure 15. This fact also ratified the validity of  ns for dividing the profiles.

C. Proposed UWB Propagation Loss Model

Based on a series of propagation measurements conducted in a frequency bandwidth from 3.1 to 10.6 GHz, the authors proposed a UWB propagation loss model [5]: where is the propagation loss at the reference distance ( m), is the propagation loss exponent when the room volume , and is the slope of against . The values of , , and for LOS and NLOS are given in [5].


This study was in part supported by the Japan Society for the Promotion of Science and the Academy of Finland with the Japan-Finland Bilateral Core Program. The authors would like to thank the program participants of the Tokyo Institute of Technology, Japan and Aalto University, Finland, for valuable discussions.


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Copyright © 2012 Miyuki Hirose et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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