The increase in electronic entertainment equipments within vehicles has rendered the idea of replacing the wired links with intra-vehicle personal area networks. Ultra-wideband (UWB) seems an appropriate candidate technology to meet the required data rates for interconnecting such devices. In particular, the multiband OFDM (MB-OFDM) is able to provide very high transfer rates (up to 480 MBps) over relatively short distances and low transmit power. In order to evaluate the performances of UWB systems within vehicles, a reliable channel model is needed. In this paper, a nomadic system where a base station placed in the center of the dashboard wants to communicate with fixed devices placed at the rear seat is investigated. A single-input single-output (SISO) channel model for intra-vehicular communication (IVC) systems is proposed, based on reverberation chamber theory. The model is based on measurements conducted in real traffic conditions, with a varying number of passengers in the car. Temporal variations of the wireless channels are also characterized and parametrized. The proposed model is validated by comparing model-independent statistics with the measurements.
Interest in wireless personal area networks (WPANs) for intravehicle communications (IVCs) has significantly increased. In addition to sensor networks used to perform vehicle maintenance (tire pressure, oil levels,…), WPANs are considered as a possibility for interconnecting entertainment equipment within the vehicle. Currently, electronic devices (e.g., DVD players, positioning devices, mobile phones,…) are connected using wired or fiber-optic links. The replacement of those wired links by wireless networks will decrease installation cost and time and increase the network flexibility. In this paper, a nomadic system is investigated: the base station is placed in the center of the dashboard and wants to communicate with fixed devices placed at the rear seat.
To enable high connectivity, a solution must be found that provides sufficient data rate, has low power consumption, and if possible, has low cost. One potential candidate is the Ultra-wideband (UWB) technology, and in particular the Multi-Band Orthogonal Frequency Division Multiplexing (MB-OFDM). The MB-OFDM is standardized by the European Computer Manufacturers Association (ECMA) under the standard ECMA-368 [
In order to evaluate the performance of MB-OFDM systems within vehicles, a reliable UWB channel model is needed. This model has to include the specific propagation characteristics of IVC environments. Several studies have started to investigate the IVC-UWB channel [
However, none of these papers propose a channel model that suits well for the scenario investigated here and that can be easily implemented for performance evaluation. In [
The measurements were carried out inside a common urban Peugeot 207 car (4030 mm × 1970 mm × 1465 mm). Three different measurement sites were chosen in order to compare the influence of different environments on the IVC propagation. The first site was a large avenue with two lanes in each direction separated by a central cleared area. The second measurement site consisted of a set of narrow streets with one-way traffic and streets with one lane of traffic in each direction. The third measurement site was a highway, with three lanes in each direction. The measurements were carried out in normal traffic condition (traffic lights, passing, stopping,…) and at different speeds depending on the environments. For the first environment, the average speed was 50 km/h, 30 km/h in the narrow streets and 120 km/h on the highway.
For each environment, measurements were made by varying the number of passengers from 1 to 4 people. The seats at the front of the vehicle were first filled, then the seats at the rear. To be the most realistic, the passengers' movements were like in a normal situation for car travel. As mentioned previously, a nomadic system was investigated: the transmitter was placed in the center of the dashboard and the receiver was placed behind the driver's seat. There was no line of sight between the two antennas, and the distance between the antennas was approximately one meter. The location of the antennas and the different positions of the passengers are shown in Figure
Pictures and top view of the experimental setup.
Front Antenna (TX). The TX was placed in the center of the dashboard
Backside Antenna (RX). The RX was placed at a fixed position behind the driver's seat
Experimental Setup. Measurements were made by varying the number of passengers from 1 to 4 people. The seats at the front were first filled, then the seats at the rear
The channel frequency responses were collected with a Rohde & Schwarz ZVL vector network analyzer (VNA), with omnidirectional broadband SMT3TO10M SkyCross antennas. The VNA was placed in the trunk of the car to avoid any interaction with the measurements. The measurements were made in the first frequency band of the MB-OFDM standard [
Comparisons of Power Delay Profiles (PDPs) for the different environments and different number of passengers are shown in Figure
Comparison of the Power Delay Profiles for the different environments (a) and for different number of passengers (b).
Comparison of the Power Delay Profiles between the different environments for 3 passengers. No significant differences can be observed between the different environments
Comparison of the Power Delay Profiles between different number of passengers in the Large Avenue environment. A faster attenuation occurs when the number of passengers is increased
Rms delay spread (a) and path loss (b) in function of the number of passengers. For both parameters, values decrease with the number of passengers and are quite similar for all the environments.
Rms delay spread versus the number of passengers
Path loss versus the number of passengers
In [
In Figure
In order to statistically compare the results of the different environments, Student's
Mean values and standard deviations of the rms delay spread and path loss for the global set including the measurements of all the environments.
Number of passengers | Delay spread (nsec) | Path loss (dB) | ||
Mean | Std. | Mean | Std. | |
1 | 9 | 0.29 | −50 | 0.38 |
2 | 7.47 | 0.41 | −51.95 | 0.42 |
3 | 6.36 | 0.38 | −54.42 | 0.65 |
4 | 5.18 | 0.39 | −56.76 | 0.73 |
The results indicate that the propagation inside the car is independent of the environment outside the car. Only what happens inside the car (e.g., number of passengers, size of car, etc.) will have an impact on the IVC channel so confirming [
From an electromagnetic point of view, the intra-vehicular environment can be considered as a loaded cavity or a loaded reverberation chamber (RC) [
In Figures
In tapped-delay line models [
In [
For a given delay
For each tap,
Using (
Model parameters.
Number of passengers | ||||||||||
Mean | Std. | Mean | Std. | Mean | Std. | |||||
1 | 10.73 | 0.41 | −59.32 | 1.19 | 10.73 | 0.63 | −9.13 | 7.43 | 3.45 | 3.5 |
2 | 9.25 | 0.35 | −60.03 | 1.23 | 8.83 | 0.63 | −9.81 | 6.58 | 3.2 | 3.6 |
3 | 7.23 | 0.28 | −60.45 | 1.33 | 7.57 | 0.63 | −7.4 | 2.35 | 3.03 | 3.45 |
4 | 5.61 | 0.23 | −61.79 | 1.54 | 6.94 | 0.63 | −7.49 | 1.71 | 3.15 | 3.6 |
Extraction of the powers associated with the coherent (blue line) and diffuse (black line) components. The red dotted line presents the exponential model used to characterize the diffuse component. The example presented here is the case with two passengers.
For the same case with two passengers, the measured Rice factor
Measured Rice factor
In every scenario, the mean value of the Rice factor
Detailed view of the measured Rice factor
The experimental parameters used to characterize the diffuse component and the
In order to ensure that the amplitude and the phase of the impulse responses' taps can be modelled separately, the correlations between them were extracted. The results show that there is no significant correlation between the amplitude and the phase of the impulse responses' taps. Kolmogorov-Smirnov (KS) tests, with a level of significance of
For the coherent component of the impulse responses, no correlation in the delay-domain is experimentally observed for the amplitudes of the different taps. For the diffuse component, two correlations are considered: the correlation between the amplitudes of successive taps of one single impulse response and the correlation between the amplitudes of identical taps of successive impulse responses.
In this paper, the following notation is used for the correlation coefficient
The two data sets are defined as
Delay correlation of the diffuse component. Only correlation coefficient with adjacent taps have significant values (above 0.5).
The two data sets are defined as
Example of the auto-correlation coefficient in the time domain. All those auto-correlations are similar. After 1.575 sec, the correlations become insignificant (below 0.5).
The averaged time auto-correlation is fitted with an exponential model.
The parameter
Mean and standard deviation of
Number of passengers | Mean (s) | Std. (s) |
---|---|---|
1 | 0.68 | 0.28 |
2 | 0.57 | 0.15 |
3 | 0.42 | 0.09 |
4 | 0.35 | 0.08 |
To summarize the description of the model, a brief summary of the model implementation is presented. For every sets of channel impulses' responses, for each tap For each set and for each tap From ( From the previously generated The diffuse component of the impulse responses The coherent component of the impulse response Finally, the impulse response is given by the sum of coherent and diffuse components:
In order to validate the model presented previously, comparisons between measured and simulated channels were performed. The rms delay spread, the path loss, and the coherence bandwidth were evaluated. The coherence bandwidth is given by the frequency auto-correlation evaluated at 0.5. Those three parameters are not directly used in the model and can be good metrics in order to validate the model. For each number of passenger, the following parameters were used to generate the channel impulses' responses: number of sets: 20, number of impulses' responses per set: 200, bandwidth: 1584 MHz, number of taps: 529, sweep time: 0.175 sec.
The comparison of the rms delay spread and path loss are, respectively, presented in Figures
Comparisons between simulated and measured channels. The parameters extracted from the simulations do not differ significantly from the measurements. Mean values agree well, and the standard deviations do not differ significantly.
RMS delay spread versus the number of passengers
Path loss versus the number of passengers
Coherence bandwidth versus the number of passengers
The results presented previously show that the parameters extracted from the simulations do not differ significantly from the measurements. Some small differences (<10% relative error) can be observed between measured and simulated channels but, in general, there is a good matching between the measurements and the model, meaning that this model can be applied for these scenarios.
In this paper, the UWB wireless communication channel inside a car has been investigated. A channel model, based on the reverberation chambers theory, has been proposed, that splits the channel into a coherent and a diffuse component. A measurement campaign has been conducted to extract parameter values for the model. A preliminary study of the channel's first-and-second order statistics showed that the influence of the environment outside the car is negligible, whereas the influence of the number of passengers significantly changes the propagation environment. The time- and delay-correlations for the diffuse component were also extracted from the measurements. It could be observed that delay-correlation is only significant between adjacent taps, while temporal correlation can last as long as a few hundreds of milliseconds. Finally, the model was successfully validated by comparing generated and measured channel statistics.
As the amplitudes of the diffuse component are Rayleigh distributed, the generation starts with a matrix
For the auto-correlation matrix
For the delay matrix
Taking into account (
(i) for tap
(ii) for taps
To correlate Rayleigh-distributed random variables, the correlation coefficients