Electrical characteristics of the materials for the measurement scenario
1. Introduction
Ultrawideband (UWB) technology has developed rapidly over the past several years. This technology is especially attractive in highdatarate and shortrange wireless communications. These applications make UWB technology suitable for indoor mobile communication applications, such as wireless personalarea networks (WPAN). This interest has motivated the study of the propagation of the UWB signals in indoor environments as an important task for the implementation of WPANs.
In the last decades, significant effort has been focused on the characterization of the indoor channel for narrowband systems. Statistical (Motley & Keenan, 1990; Saleh & Valenzuela, 1987; Seidel & Rappaport, 1992; Tornevik et al., 1993) and deterministic (Lauer et al., 1984; Saez de Adana et al., 2000; Tarng et al., 1997; Whitman et al., 1995) models have been used most frequently in these studies. The statistical models are based on the obtention of closed formulas to characterize the propagation channel. These formulas are derived from the data obtained from measurement campaigns in different environments. Alternatively, the deterministic models are based mostly on the use of raytracing techniques (Saez de Adana et al., 2000; Tarng et al., 1997; Whitman et al., 1995) to predict the multipath phenomena and the Uniform Theory of Diffraction (UTD) technique (Kouyoumjian & Pathak, 1974) to calculate the received power or the propagation losses. However, the features of the UWB systems (with bandwidth in the range of GHz) render the conventional narrowband propagation models, both statistical and deterministic, inapplicable. These models are based primarily on frequencydomain analysis, while UWB requires a timedomain analysis due to its wide bandwidth. Therefore, special models must be used to predict the signal propagation in UWB systems. Although the number of statistical models developed for UWB systems is not as extensive as that for narrowband systems, some recent examples can be found in the literature (Cassioli et al., 2001, 2002, Dabin et al., 2006; Molisch et al., 2006). These models have been obtained, as in the case of narrowband systems, by obtaining closed expressions that fit the behavior of the received signal measured in several locations in a measurement campaign.
Regarding the deterministic models, frequencydomain UTD can be applied, performing an analysis at several frequencies and obtaining the time response using an Inverse Fourier Transform. However, this procedure is computationally inefficient in comparison to direct analysis in the time domain. Instead, the TimeDomain Uniform Theory of Diffraction (TDUTD) was developed to obtain a solution in the time domain for the reflection and the diffraction of a transient electromagnetic wave. The inclusion of the multipath phenomena in this theory and the analysis in the TD makes this technique suitable for UWB applications. TDUTD was first developed by Veruttipong and Kouyoumjian (Veruttipong & Kouyoumjian, 1979), who applied the inverse Laplace transform to the frequencydomain UTD formulation. Later, Rousseau and Pathak (Rousseau & Pathak, 1975) presented closedform solutions for the diffraction by an edge by modifying the formulation presented in (Veruttipong & Kouyoumjian, 1979). The results obtained in (Rousseau & Pathak, 1975) can be directly applied to develop a method for the calculation of the indoor propagation in UWB systems.
In this chapter, the formulation of both a statistical approach and a deterministic approach are presented. The statistical approach has been selected from the available literature because it seems very suitable for the case of UWB systems. The deterministic formulation was developed by the author of this chapter and consists of modifying the formulation presented in (Rousseau & Pathak, 1975) to introduce the contribution of lossy materials present in the indoor environment to reflection, transmission and diffraction. The goal is to obtain the reflection, transmission or diffraction coefficients using an Analytical Time Transform (ATT) from their expressions in the frequency domain. In addition, multiple interactions are also considered in this approach. These interactions include multiple reflections and transmissions and the interactions between reflected and diffracted rays. Thus, both reflectiondiffraction and diffractionreflection interactions are included. These interactions, which are obtained in the frequency domain from the product of the coefficients involved in the propagation mechanisms, can also be computed in the time domain by convolving those coefficients instead.
The deterministic approach is completed, including the analysis of a real site, which proves the validity of the model and its ability to analyze realistic environments. Some experimental measurements and comparisons with the predictions of the proposed model are presented in this chapter.
2. Statistical approach
The statistical models are obtained based on the statistical analysis of the experimental data. Some measurements are performed for a given scenario and a propagation model is obtained based on these results after the statistical analysis. In this chapter, one of these statistical models is presented. This model was obtained from measurements performed in a typical modern office building (Cassioli et al., 2002).
This propagation model provides all the parameters and distributions necessaries to obtain the power delay profile. First, the attenuation of the received power satisfies the following expression, dependent on the distance:
The smallscale averagepower delay profile (SSAPDP) accomplishes the following expression:
where
An exponential decay from the second bin can be assumed. Therefore,
where is the decay constant of the SSAPDP.
The total average energy received in the observation interval T is
Because the second term of expression (4) is a geometric series,
where
A lognormal distribution around the mean value of the path loss can be considered:
The average energy gains are obtained by inverting (5)
and therefore
This model characterizes the local PDP by the pairs
The first step is to obtain G_{tot} from (12). Next, the power ratio r and the decay factor are generated. These two values are treated as stochastic variables, modeled as lognormal variables according to the experience for narrowband systems shown in (Hashemi, 1979). The shape of the distribution is obtained from the measured data. Therefore,
and the width of the observation window is T=5.
With these parameters, SSAPDP is completely specified by (9) and the local PDP can be obtained by obtaining the normalized energy gains
with
3. Deterministic approach
The classical UTD in the frequency domain obtains the field at an observation point inside an indoor environment as the sum of the contribution of different rays. These rays that started from the source reached that observation point either directly or after one or several reflections, diffractions, transmissions or combinations of these effects. Accordingly, the TDUTD analytical impulse response in that environment can be obtained from an ATT, which consists of a onesided Inverse Fourier Transform (IFT) of the frequency response, as can be seen in (Rousseau & Pathak, 1975; Veruttipong & Kouyoumjian, 1979), and can be written as
where
impulse response shown in equation (15) includes all multipath phenomena, as mentioned previously. Each term in equation (15) will be described in the following sections.
3.1. Direct field
The contribution of the direct field to the impulse response is obtained as the ATT of the usual Geometrical Optics (GO) incident field and can be expressed by the following equation (Rousseau & Pathak, 1975):
where
where
3.2. Reflected field
Similar to the case of the direct field, the contribution of the reflected field to the impulse response is obtained from the ATT of the classical GO expression in the frequency domain by the following equation:
where
where
In equation (18),
where I_{n} is the modified Bessel function of order n and
The perpendicular component is as follows:
where, in this case,
and the rest of parameters are the same.
3.3. Transmitted field
The impulse response for the transmitted field is analogous to that response for the reflected field and can be written as
where
where I is the identity matrix.
3.4. Diffracted field
In the case of diffraction, its contribution to the impulse response is given by the ATT of the UTD expression for the frequency domain as follows:
where
where
On the other hand,
The expressions for
with
The L^{i} are distance parameters associated with the incident shadow boundaries and are the same as in the frequency domain. These parameters are given by
where
The function a^{} in the expressions (27)(30) is given by
The geometrical parameters involved in the calculation of the diffraction coefficients are the same as in the frequency domain as shown in Figure 2 and are explained in (Kouyoumjian & Pathak, 1974).
3.5. Multiple reflected and multiple transmitted fields
The expressions for the morder reflections and transmissions are easily derived recursively from the firstorder effects. For instance, the secondorder reflection is a single reflection where the source is set as the first reflection point and the incident field is the simple reflected field. Using this recursion, an morder reflection that reaches the observation point would contribute the following term to the impulse response:
where
Analogously, the morder transmitted field is
where.
3.6. Reflecteddiffracted and diffractedreflected fields
Following the same procedure as for multiple reflections, the contribution of the interaction between an edge and a reflecting surface to the impulse response can be written as
for the case of reflectiondiffraction interactions and
for the case of diffractionreflection interaction.
The meaning of these parameters is analogous to the previous effects.
4. Deterministic analysis of a realistic environment
The results of the deterministic approach presented in this chapter compared with measurements are shown in this section. The measurements were performed in a complex realistic site to investigate the validity of the approach. The measurements were done in the corridor of the second floor of the Polytechnic Building of the University of Alcala. Figure 3 shows the plan schematic of the measurement site. The dimensions of the scenario are 7.9x20.7 m. A 3D planefacets model has been designed to represent the realistic environment composed of 77 facets. The material composition of the elements of the site was concrete for the walls, wood for the doors and glass for the windows. Table I lists the electrical properties of these three materials considered in our model.



Concrete  4.5  0.01 
Wood  2  10^{5} 
Glass  6.5  10^{12} 
Several measurements were performed on the site. Examples of one LineofSight (LOS) and one NonLineofSight (NLOS) case will be shown in this section. Figure 3 illustrates the position of the transmitter and the receiver in both cases. The coordinates of the transmitter were (1.60, 5.55, 1.10). The coordinates of the receivers were (5.0, 1.78, 1.10) for the LOS case and (9.55, 1.3, 1.10) for the NLOS case. All the coordinates are given in meters.
Measurements were conducted in the frequency domain using the network analyzer (VNA) Agilent E8362B. A linearly polarized doubleridged waveguide antenna was used as the
transmitter (TX) and as the receiver (RX). The frequency range of these antennas was 1 to 18 GHz. In this range, the average VSWR ratio was lower than 1.5.
Figure 4 shows an overview of the measurement setup. The VNA was set to transmit 3201 tones uniformly distributed over the 118 GHz frequency range. This transmission gave an excess delay of 188 ns and a maximum distance of 56.4 m. The temporal resolution for the 17 GHz frequency was 59 ps.
The input signal is expressed as a sum of a small number of simple expansion functions for a more efficient convolution with the TDUTD impulse response. In this approach, the input signal is represented as the sum of waveforms with analytical signal representations that are simple poles in the complex time plane. This representation allows the convolution to be expressed in a closed form, thus speeding up computation. Moreover, the antenna transfer function is included in this representation. Equations (38) and (39) show the representation of the input signal and the closed form for the convolution, respectively.
The comparisons between the measurements and our approach for the normalized PDP in the LOS and the NLOS cases are shown in Figures 5 and 6, respectively. As can be seen, a good agreement between calculation and measurement is obtained in both cases. The mean errors were 3.5 dB for the LOS case and 4.6 dB for the NLOS case, which are very good for UWB applications.
Acknowledgments
This work has been supported, in part, by the University of Alcalá, project UAH GC2010004.
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