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Invehicle channel sounding in the 5.8GHz band
EURASIP Journal on Wireless Communications and Networking volume 2015, Article number: 57 (2015)
Abstract
The article reports vehicular channel measurements in the frequency band of 5.8 GHz for IEEE 802.11p standard. Experiments for both intravehicle and outofvehicle environments were carried out. It was observed that the largescale variations (LSVs) of the power delay profiles (PDPs) can be best described through a twoterm exponential decay model, in contrast to the linear models which are suitable for popular ultrawideband (UWB) systems operating in the 3 to 11GHz band. The smallscale variations (SSVs) are separated from the PDP by subtracting the LSV and characterized utilizing logistic, generalized extreme value (GEV), and normal distributions. Two sample KolmogorovSmirnov (KS) tests validated that the logistic distribution is optimal for incar, whereas the GEV distribution serves better for outofcar measurements. For each measurement, the LSV trend was used to construct the respective channel impulse response (CIR), i.e., tap gains at different delays. Next, the CIR information is fed to an 802.11p simulation testbed to evaluate the bit error rate (BER) performance, following a Rician model. The BER results strongly vouch for the suitability of the protocol for incar as well as outofcar wireless applications in stationary environments.
1 Introduction
1.1 Motivation
Vehicle area networks (VANs) and vehicular ad hoc networks (VANETs) generally refer to networks between cars and infrastructure points located along the road side [1,2]. These networks are aimed to deliver information about the traffic, to ensure safety of the passengers, and to provide driver assistance and passenger entertainment [3]. The majority of future vehicular networks are envisaged to conform with the IEEE 802.11p standard [4] described in wireless access in vehicular environments (WAVE) [5] and governed by a nonprofit organization, the car2car communication consortium (C2CCC) [6]. IEEE 802.11p standardizes signal transmission for vehicletovehicle (V2V) or vehicletoinfrastructure (V2I) communications, operated at a frequency band of 5.8 GHz [7].
Presently, there had been an upsurge in the interest of the automobile industries regarding wider applications of intravehicular wireless transmission. The use of wireless communication in the vehicle cuts down the wiring harness, which will reduce the weight and cost of the car. It also eliminates the need of drilling to pass the cables, thereby improving robustness of the vehicle, and will simplify the design as well as production processes. As mentioned earlier, the IEEE 802.11p protocol was specified primarily for V2V/V2I scenarios where both transmitter and receiver are placed outside the vehicle [8]. In order to realize fully integrated intelligent transportation systems (ITSs), it is important to evaluate the efficacy of the protocol for wireless connections in incar (both transmitter and receiver are inside the vehicle) and outofcar (transmitter is inside, and receiver is outside, or viceversa) scenarios as well.
1.2 Literature survey
There is a good number of open literature available that deal with vehicular channel measurement and modelling. We begin the list with [9], which provides a general review on advances and challenges in V2V channel measurement campaigns. The following papers, namely [1012], describe measurementbased narrowband channel models for different situations: on highways, in cities, and on country roads. Different aspects of nonstationary vehicular channel measurements in the frequency band of our interest are addressed in [13,14]. Reports from some intervehicular measurement campaigns in the 5.9GHz band are also made available in [15] and [16]. In particular, Cheng et al. [15] study the suitability of IEEE 802.11a for vehicular wireless links. Sen and Matolak [16] provide a wide set of power delay profile (PDP) measurements and proposed tapped delay line models based on Markov chains.
As far as intravehicular channel measurements are concerned, most of the articles are focused on ultrawideband (UWB) operating over a broad frequency spectrum of 3 to 11 GHz. For example, the performance of multiband orthogonal frequency division multiplexing (MBOFDM) in intracar communication is studied by Maehara et al. [17]. In [18], Schack et al. report a comparison of broadband channel sounding experiments over a different range of cars. Another interesting paper is by Niu et al. [19], where they describe a detailed UWB channel modelling campaign for intravehicular environments. The characterization of the 5GHz intravehicle communication channel is only attempted recently in [20], where the authors present power delay profiles, delay spread, and statistical channel models for a minivan and a bus.
Finally, in [21] and [22], the throughput and packet delivery performance of the IEEE 802.11p protocol is studied in V2V and V2I scenarios with different speeds of movement. However, to the best of our knowledge, none of the authors utilize the measured intravehicle channel models to evaluate the bit error rate (BER) for the IEEE 802.11p standard.
1.3 Contributions of the paper
In this paper, we present the results of intravehicle channel model measurements for the 5.8GHz band for a typical passenger car. We provide measurements for a vehicle parked in an underground garage. The results were collected in the frequency domain by a vector network analyzer (VNA), then they were transformed into the time domain using the inverse fast Fourier transform (IFFT), and the largescale variations (LSVs) from the measured PDPs were separated. The smallscale variations (SSVs), which were obtained after subtracting the LSV from the PDP, were characterized statistically thereafter. Finally, simulated BER results were obtained using a 802.11p simulation model. Specifically, the contributions of the paper are as follows:

Results for intravehicle and outofvehicle single input single output (SISO) channel measurements performed in the 5.875 to 5.885GHz frequency band.

Characterization of the LSV trends via a twoterm exponential decay model and the SSV random variations via logistic, normal, and generalized extreme value (GEV) distributions with the twosample KolmogorovSmirnov (KS) test.

Comparison of PDP decay trends of UWB and 802.11p for an identical measurement setup.

BER simulation for 802.11p using the measured channel data.
1.4 Organization of the paper
The paper reads as follows: Section 2 describes the measurement setup realized through a VNA. In Section, we provide a decay model to characterize the PDP, characterize the LSV trends, and find the optimal statistical distribution for the SSV. This section also includes a comparison between 802.11p and vehicular UWB channel characteristics. Next, the BER performance for the 802.11p standard is presented in Section 4. Finally, some conclusions are presented in Section 5.
2 Measurement setup
Measurements were performed with one transmitter (Tx) and three receiver (Rx) antennas in a righthand, fourdoor Skoda Octavia car with dimensions: 4.659 × 1.814 × 1.462 m. The vehicle was parked on the sixth floor in an underground garage of the Faculty of Electrical Engineering (FEKT), Brno University of Technology (VUT). Walls and floors of the garage premises are made of reinforced concrete, and they provide an environment that is free from narrowband interference. There were no parked cars on neighboring parking lots.
Figure 1a shows the underground garage and the parking lot with the car and equipments. The receive/ transmit antennas inside the car are zoomed in Figure 1b. The four omnidirectional conical monopole antennas that were used are identical. The vertical polarization of the antennas can be confirmed from the radiation patterns presented in Figure 2 which depicts that the azimuth plane pattern is circular.
Equipments for recording data were placed outside of the vehicle. All doors and windows were closed, except the driver’s window, which was slightly open to pass the cables between the antennas and the recording equipment.
For measuring and recording the transmission coefficient between transmitter and receiver, a fourport VNA from Agilent Technologies, E5071C (Agilent Technologies, Inc., Santa Clara, CA, USA) [7] was used (Figure 3 [bottom]). The transmit power was set at 5 dBm, and phase stable coaxial cables were used for interconnections. The VNA measures the complex valued channel transfer function in the frequency domain through the sparameter, S _{ x1}, which is the transmission coefficient between port 1 (Tx) and port x∈{2,3,4} (Rx). The measured frequency range (5.775 to 5.875 GHz) was swept with a frequency step size of f _{ s }=0.125 MHz. Due to the fine resolution in the frequency domain, we were able to construct the delay domain up to t _{ d }=1/f _{ s }=8 μs without aliasing effect of the VNA. The information, when translated into the spatial domain, gives us a propagation space resolution P _{sr}=c/BW=3 m and a maximum propagation distance of L _{ d max}=c/f _{ s }=2.4 km. The parameters, c and BW, denote propagation speed of electromagnetic waves in free space (3×10^{8} m/s) and the bandwidth of the measured band, respectively.
The schematic diagram of the setup for IEEE 802.11p frequency domain channel sounding is presented in Figure 3 [top] [23]. Real and imaginary parts of the transfer function (S _{ x1}) were exported to MATLAB. The frequency domain data over the entire bandwidth BW = 100 MHz were partitioned into 10 MHz bins, where each bin corresponds to a subchannel of 802.11p. All results were transformed from the frequency domain into the time domain, $h(\tau)=\mathcal {F}^{1}H(f)$ , utilizing the IFFT with a typical rectangular window. The PDP was calculated by averaging over ten subchannel timedomain data.
Figure 4 shows transmitter and receiver antenna positions. A total of 15 different TxRx combinations were tested with separations ranging from 0.53 to 3.38 m. The Tx antenna was placed at three different locations inside the vehicle and at two locations outside the vehicle. Incar Tx antenna positions were set at the right rear seat, armrest in the middle of the car, driver seat, while for locations outside the car, two positions at a height of 1.09 m were chosen: one in front of the car and the other one near the right headlamp. The Rx antennas were installed on the left and right upper edges of the windshield and on the roof in the rear part of the vehicle. The positions of antennas were chosen for realizing both lineofsight (LOS) and nonlineofsight (NLOS) scenarios. All these combinations are described in detail in Table 1.
3 Channel description
The measured channel impulse response (CIR) is h(τ)=h _{ch}(τ)∗h _{fil}(τ) where h _{ch}(τ) refers to the actual channel response, ∗ denotes timedomain convolution, and $H_{\text {Fil}}(f)=\mathcal {F}\{h_{\text {fil}}(\tau)\}$ is the transfer function of the windowing operation. The bandlimiting postprocessing, however, does not lead to any serious causality issues [24]. The generic multipath CIR is described by:
where τ _{ i } is the propagation delay, a _{ i } exp(j θ _{ i }) is the complex amplitude coefficient of the ith multipath component, δ(.) is the Dirac delta function, and N is the total number of multipath components.
The PDP is simply the squared channel impulse response P(τ)=h(τ)^{2}, and it includes LSV and SSV, which can be designated mathematically in the following manner: P(τ)=γ(τ)+ξ(τ), where γ(τ) denotes LSV and ξ(τ) is the SSV.
Figure 5 shows the measured PDP for different LOS and NLOS conditions for both inside and outside of the vehicle. As expected, the power levels are significantly higher for the LOS measurements compared to the NLOS situations. The power levels for the NLOS case show rapid variations near the noise floor (−110 dB).
3.1 Largescale variations
Analytical calculations performed for closed spaces [25,26] demonstrated an exponential decay of the PDP. As intravehicular channels appear to have similar signal propagation environments, we express the LSV with a twoterm exponential model:
The first term includes power from direct and major reflected rays, and the second term, with a very low slope (close to linear), reflects the power from diffused multipath components. This exponential model (2) offers more flexibility compared to singleterm exponential or linear models. Further, it also avoids discontinuities in the extracted LSVs, which are present when one attempts to divide the PDP into two (or more) delay parts and tries to fit individual expressions for each of these parts [27].
In order to evaluate how well the LSV model (2) corresponds to the actual PDP, we evaluated the mean square error (MSE) between the PDP and the LSV:
The MSE is 6.59×10^{−5}, averaged over all the M=15 measurements.
The LSVs for the twoterm exponential model for different distance between Tx and Rx antennas of 0.53, 0.87, 1.28, 2.74, and 3.15 m are shown in Figure 6. It is observed that LSV for the LOS case drops more rapidly than for the NLOS scenario. This is in perfect harmony with earlier observations [28].
The parameters of the model for these five distances are given in Table 2. Both the coefficients, A and C, show similar values for incar (or outofcar) situations. On the other hand, the coefficient B decreases steadily with increasing TxRx separation whereas D increases.
3.2 Smallscale variations
The SSV was obtained through subtracting the LSV from the measured PDP, i.e., ξ(τ)=P(τ)−γ(τ). Considering that the LSV follows (2), we separated SSVs for five different distances, and the results are depicted in Figure 7.
The characterization of the SSV is based on finding the appropriate random process using the normal, the logistic, and the GEV distributions. The GEV distribution [29] appeared to be a good fit for both UWB [30] and milimeter wave [31] smallscale variations. On the other hand, the logistic family of distributions [32] has been used successfully in rayclusterbased modelling at 5 GHz [33] and modelling the probability density function (PDF) of the rms delay spread at 2.35 GHz [34]. We have also included the normal distribution as a reference. The PDF of these three distributions are given in (9), (10), and (11), respectively.
where $\beta =1+k\frac {x\mu }{\sigma }$ . The parameters, μ, σ, and k, are the location, scale, and shape parameters, respectively.
To compare distributions and SSV, we have performed the twosample KS test [35]. The p values are presented in Table 3. Higher p values for the logistic distribution inside the car and for the GEV distribution outside the car prove that they are the best candidates among the three for describing the SSV in incar and outofcar situations, respectively. The same can be observed from Figure 8, where the cumulative distribution functions (CDFs) of measured SSVs are fitted with these three distributions for results measured at different distances.
3.3 Comparison with UWB
UWB possesses a great potential for highspeed data communication and precise localization operations in cluttered closedspace hostile towards radio frequency (RF) signal propagation. Traditionally, the PDP for UWB channels are described with a modified SalehValenzuela (SV) model [19]. However, in a recent work by Demir et al. [36], the authors found that the PDP for UWB transmission in vehicular networks may be characterized by segmenting the PDP (in dB scale) into several linear slopes. Inspired by these facts, we tried to compare our results for 802.11p protocol with measurements for UWB (3 to 11 GHz) performed using the same VNAbased setup. The goal was to find if one can attain similar LSV trends in wider bandwidths as well.
The number of measured points was the same; however, due to the larger BW (8 GHz) and a frequency step size of f _{ s }=100 MHz, we have a smaller time range t _{ d }=1/f _{ s }=10 ns. Thus, we cannot compare the PDPs in an onetoone basis. For analyzing LSV models, we use a scaled comparison instead. For the UWB measurement, the propagation space resolution is P _{sr}=3 cm and the maximum propagation distance is L _{ d max}=30 m.
Figure 9 shows the UWB and the 802.11p normalized PDP vs normalized time samples. For UWB, it was found that the PDP constitutes one (or a few) major peaks followed by somewhat linear decreasing slope. The reader may also note the delay and lower power for the first peak of the UWB PDP. In addition, the delay and the maximum value of the peak is more for larger distances between the transmitting and receiving antennas. However, this phenomenon is not observed in narrowband 5.8 GHz measurements for 802.11p.
4 Simulation results
For BER simulation, we used MATLAB. A detailed description of the simulation methodology can be found in [37]. Parameters for generating the signal are available in [38]. A blockscheme diagram that corresponds to the SISO transmission model is presented in Figure 10.
The Tx generates 10^{6} data subframes for a given energy per bit to noise power spectral density ratio (E _{ b }/N _{0}). For signal transmission, we use rate 1/2 convolutional coding and BPSK modulation. On the receiver side, we use simple least square (LS) estimation and a hard Viterbi decoder. The 802.11p standard is based on OFDM with N _{IFFT}=64 for a bandwidth of 10 MHz. In the time domain, each OFDM symbol contains 80 chips including a cyclic prefix of length 16. The final transmitted signal has a duration of 8 μs.
Rician distribution had been successfully utilized for nongeometrical stochastic modelling of vehicular channels [11]. A Rician model also encompasses the Rayleigh model as a special case. For the channel block in Figure 10, the tap gains were calculated from the LSV based on a simple Rician model. The respective CIRs are shown in Table 4. It is interesting to find that reallife measurements exhibit a very fast decrease of the tap gains, similar to some earlier reports [39].
The BER results for different distances between Tx and Rx, for LOS and NLOS cases, and for intravehicle and outofvehicle situations are shown in Figure 11. Results can be classified quite unambiguously into two groups, one for intravehicle and the other for outofvehicle measurements. The BER worsens for higher TxRx distance inside the car, but outside the vehicle, the BER values almost remain unaffected when the TxRx separation is altered. The BER results for intravehicle simulations are slightly better than the results for the outofvehicle channel. For a target BER of 10^{−5}, the required E _{ b }/N _{0} differs by 6 dB.
5 Conclusions
The aim of the text was to assess the suitability of protocol 802.11p for incar wireless applications. For the purpose, extensive vehicular channel measurements in the 5.8GHz band were carried out. A double exponential decay model was used for describing the basic trend of the measured PDP, and we utilized it for IEEE 802.11p BER simulation. The BER achieves the recommended values for all variants of the channel, and it can be concluded that 802.11p standard (in particular, the PHY protocol stack) can be adopted for intravehicular communication systems.
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Acknowledgements
This work was supported by the SoMoPro II programme, Project No. 3SGA5720 Localization via UWB, cofinanced by the People Programme (Marie Curie action) of the Seventh Framework Programme of EU according to the REA Grant Agreement No. 291782 and by the SouthMoravian Region. The research is further cofinanced by the Czech Science Foundation, Project No. 1338735S Research into wireless channels for intravehicle communication and positioning, and by Czech Ministry of Education in frame of National Sustainability Program under grant LO1401. For research, infrastructure of the SIX Center was used. The generous support from Skoda a.s. Mlada Boleslav are also gratefully acknowledged.
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Keywords
 Intravehicle channel
 IEEE 802.11p
 Channel sounding
 Ultrawideband
 Power delay profile
 Bit error rate