Physical Layer Simulation Results for IEEE 802.11pusing Vehicular non-Stationary Channel Model

Laura Bernado1, Nicolai Czink1, Thomas Zemen1, Pavle Belanovic2

1Forschungszentrum Telekommunikation Wien (FTW), Vienna, Austria2Universidad Politecnica de Madrid, Madrid, Spain

Contact: bernado@ftw.at

Abstract—Traffic safety can be improved by using a vehiculardedicated communication protocol. The standard IEEE 802.11pis being developed for this purpose. The physical layer propertiesof this draft are based on the already widely used IEEE 802.11astandard. Nevertheless, the propagation conditions in vehicularcommunications are different to the ones considered for 802.11a,which is focusing on nomadic indoor usage, and well studieduntil now. In this paper we present the simulation resultsobtained from an implemented physical layer model for thisstandard. The used channel model describes the very peculiarcharacteristics of the vehicular radio channel, specially the non-stationarity. Several channel estimators are tested based on thepilot structure defined in the standard focusing on low complexityimplementations. The results show that diffuse components,present in vehicular channels on highways, have a very significantimpact on the system performance. Furthermore, in situations ofpoor line-of-sight contribution, an acceptable frame error rate isnot achievable even at high signal-to-noise ratio values. Therefore,more complex channel estimation and equalization techniquesbased on the current standard pilot pattern have to be developedthat are able to cope with the properties of the vehicular radiochannel.

I. INTRODUCTION

In the recent years, vehicular communications have raisedinterest due to the fact that they can help reduce traffic acci-dents and traffic jams. The IEEE is currently working on thenew 802.11p standard, dedicated to vehicular communications.The draft specifies special implementation of the mediumaccess control (MAC) and the physical (PHY) layer. In orderto implement receiver algorithms with good performance, sim-ulations have to be performed using realistic channel models.Up to now, several groups have been investigating the resultsof the 802.11p standard from the MAC-layer perspective [1],[2]. However, the PHY layer analysis has not been thoroughlyinvestigated, yet. A bit-error-rate analysis has been presentedin [3], where the authors consider two basic vehicle-to-vehiclecommunications in highway and urban scenarios. Only onekind of channel estimation technique was tested, and that onlyin non-vehicular stationary models.

In this paper we present a performance evaluation of dif-ferent basic channel estimation and equalization techniques

This research was supported by the project REALSAFE, as well asthe project COCOMINT funded by Vienna Science and Technology Fund(WWTF), and the EC under the FP7 Network of Excellence projectsNEWCOM++. The Telecommunications Research Center Vienna (FTW) issupported by the Austrian Government and the City of Vienna within thecompetence center program COMET.

in terms of frame error rate (FER). Several channel modelsrepresenting the vehicular radio channel properties have beendeveloped recently [4]. We use a realistic non-stationary vehic-ular channel model parametrized directly from measurementsat 5 GHz. Since we are interested in the communication ofsafety related messages, we focus on the infrastructure-to-vehicle (I2V) communication between vehicles and the roadoperators’ infrastructure.

II. SYSTEM DESCRIPTION

In this section we give a brief overview of the 802.11pstandard, describe the used estimation techniques, and the non-stationary vehicular channel model.

A. System Overview

We implemented a PHY-layer simulator transmission chain802.11p standard compliant. In the standard there are 8different coding and modulation schemes defined. Each oneachieves different data rates. Only three of them are mandatoryto be implemented. In order to transmit the randomly gener-ated data in the simulation we choose the coding-modulationscheme achieving 6 Mbps. This uses QPSK modulation and aconvolutional code with constraint length 7 at a coding rate of1/2. This is one of the most robust transmission schemes, andthe results will be worse for the other, less protected schemes.Zero forcing is used as equalization method. Since vehicularchannels are strong time-varying, channel estimation has asevere impact in the system performance.

B. Estimation Techniques

The results presented in this paper show the system perfor-mance of the 802.11p standard employing regular estimationand equalization techniques. The 802.11p pilot pattern con-siders two kind of pilots: block, and comb pilots [5]. Thepilot allocation is shown in Fig. 1. All 52 subcarriers of thefirst two OFDM symbols are dedicated to pilots. Afterwards,only 4 subcarriers contain pilots throughout the whole frameduration. These so-called comb pilot subcarriers have indices6, 20, 33, and 47. Based on them, we define three different typeof channel estimators: block, comb, and block-comb. Withinthese categories, the investigated techniques are:

• Block-type channel estimators: An estimate of the chan-nel is calculated from the block pilots. The estimatedchannel coefficients are used for the whole frame. They

978-1-4244-6826-3/10/$26.00 ©2010 IEEE

Fig. 1. Pilot allocation in IEEE 802.11p.

do not take time variation into account. We investigatethe effect of least-squares (LS) channel estimation

hB−LS = X−1p yp1,p2, (1)

and minimum mean square error (MMSE) estimation

hB−MMSE = RHHXHp

(XpRHHXH

p + σ2I)−1

yp1,p2,(2)

referred as to block LS (B-LS) and block MMSE (B-MMSE) estimators [6], [7], [8]. In (1), yp1,p2 has a formof a vector with the pilots of the two first received blocksstacked, and XT

p = [Xp1Xp2], where Xp1,p2 are diagonalmatrices containing the pilot symbols. In (2) RHH isthe channel frequency correlation function, and σ2 is thenoise variance. We use the estimated channel coefficientsfor every instance of the frame.

• Comb-type channel estimators: Only the four pilot sub-carriers are used to obtain a LS estimate of the channelat the comb positions. The channel coefficients for therest of the subcarriers are calculated using interpolationtechniques. We consider linear, and spline interpolation.The resulting channel estimators under this categoryare comb LS with linear interpolation (CLS-linear), andcomb LS with first order spline interpolation (CLS-spline)[8], [9], [10].

• Block-comb-type channel estimators: An initial channelestimate is calculated based on the block pilots using theLS technique. Subsequently, a linear MMSE filtering inthe time domain is applied. We propose two channel esti-mators, block-comb MMSE 1 (BC-MMSE1) that assumesthe time correlation function Rhh based on the Clarke’smodel [11], and block-comb MMSE 2 (BC-MMSE2) thatestimates the time correlation function Rhh from thecomb pilots. Both are described by

HBC−MMSE1 = RhhXHp

(XpRhhXH

p + σ2I)−1

Y,(3)

where Y is a matrix containing the received symbols atthe pilot positions. For BC-MMSE2, the channel timecorrelation function is estimated as

Rhh =1P

P∑

p=1

hphHp , (4)

where P is the number of pilot subcarriers and hp is the

estimated channel frequency response at the comb pilotpositions for all time instances using LS.

These techniques have been used in other communicationsystems with good results [6], [7], [8], [9], [10]. Our objectiveis to check if they are also suitable for vehicular commu-nications or whether more sophisticated techniques need tobe implemented. From an implementation point of view itis important to take the complexity of the different channelestimators into account. Figure 2 shows the number of floating-point operations (flops) needed per frame of length 200 bytesfor the different channel estimators.

B−LS

B−MMSE

CLS−linear

CLS−spline

BC−MMSE1

BC−MMSE2

0 2 4 6 8x 10

4flops

Fig. 2. Number of floating point operations needed for different channelestimators.

C. Non-Stationary Channel Model

The channel model adopted by the 802.11p standard is a tapdelay line model. However, this model does not accuratelyrepresent the most important features of the vehicular radiochannel [4].

Dedicated vehicular radio channel measurements at 5 GHzhave shown that the main contributions in the channel impulseresponse are: line-of-sight (LOS), deterministic scattering, anddiffuse scattering. The LOS component has a strong poweras long as there is a direct connection between transmitter(Tx) and receiver (Rx). It drops to lower power values dueto shadowing when there is an obstructing object. The diffusescattering contribution has a significant impact on the channelpower. A very important characteristic of vehicular channels isthat different delays present correlated fading due to reflectionscoming from the same object along time. The statisticalproperties of the channel change over time and therefore thechannel is non-stationary. All these characteristics are nottaken into account in the model proposed for 802.11p. Forthis reason, we use a geometry-based stochastic channel model[4] directly parameterized from vehicular measurements at 5.2GHz [12] which models all of these effects, and thus leads totrustworthy simulation results.

We generate a typical highway vehicular scenario. It con-sists of a road-side unit acting as Tx, a vehicle acting as Rx,discrete scattering objects (mobile and static, MD and SD),and diffuse scattering (D) at both sides of the road. The mobilediscrete scatterers symbolize other cars moving on the road,while the static discrete scatterers represent traffic signs. Thediffuse scattering wall models foliage and other objects presentalong the road. The position of the scatterers is randomlydetermined. The speed of the mobile discrete scatterers is

(a) 3D representation (b) 2D representation

Fig. 3. Channel impulse response.

stochastically assigned from a distribution with a mean of90 km/h and a standard deviation of 2 km/h. Tx and Rx aredeterministically placed. The velocity of the Rx is set to 100km/h. Since we want to simulate a I2V scenario, the Tx doesnot move and is located in the middle of the road. The time-varying channel transfer function is calculated as

H(f, t) =γ(LOS)(t) exp[−j2πfτ (LOS)(t)]+NSD∑

k=1

γ(SD)k (t) exp[−j2πfτ (SD)

k (t)]+

NMD∑

k=1

γ(MD)k (t) exp[−j2πfτ (MD)

k (t)]+

ND∑

k=1

γ(D)k (t) exp[−j2πfτ (D)

k (t)], (5)

where γ(·)k denote the complex-valued attenuation coefficient

of the different paths, which include the effects of path loss,antenna patterns, and large-scale fading. τ

(·)k denote the delays

of the paths, and N(·) denote the numbers of SD, MD and Dscatterers, respectively.

The Tx sends 100 frames, each having 200 bytes, everysecond during 7 seconds. In 7 seconds the Rx covers adistance of 200 m. Between transmissions Tx and Rx remainsilent. This allows us to calculate FER over time. A 3D and2D representation of the envelope of the generated impulseresponses are depicted in Fig. 3 (a) and (b) respectively. The

−250 −200 −150 −100 −50 0 50 100 150 200 250−10

−5

0

5

10

15

20

25

y [m]

Initial geometry

RxDiscrete StaticScatterers

Diffuse Scatterers

DiscreteMobileScatterers

Tx

Fig. 4. Scatterers distribution over 500 m.

duration of the transmission of 100 frames of 200 bytes is29.6 ms. In order to see the channel impulse response, thetransmission time in Fig. 3 is scaled a factor of 30.

The Rx approaches the Tx from 0 to 3 s. During thistime, the impulse response shows a very well defined LOScomponent and other contributions appearing at later delays.A similar behaviour is observed from 5 to 7 s. Noteworthyis to mention that at time 1 s the LOS component has a verylow power due to shadowing. The contributions from the otherdelays are stronger and therefore resulting in a larger delayspread. On the other hand, at 5 s the LOS is very strong,and thus the effective delay spread is shorter. From 3 to 4 sthe Rx drives by the Tx. At this point, the influence of thediffuse components becomes important and can be noticedon the distribution of the channel power in the delay. Afterthe LOS, we observe a diffuse tail which increases the delayspread of the channel.

III. RESULTS

We simulate the transmission of 100 frames every secondin the scenario shown in Fig. 4, over which we calculate theFER. We consider that the Tx transmits with constant power.The maximum signal-to-noise ratio (SNR) achieved is 40 dB.Since the channel model includes the path loss, the SNR variesin time. Figure 5 depicts the FER over time for the differentestimation and equalization techniques presented in Section II.At the bottom of Fig. 5 we plot the time evolution of the SNRat the Rx. At time 5 s, the SNR is 40 dB and maximal.

The channel estimator BC-MMSE1 results in a FER of1 during almost the whole simulated time. This is becausethis estimator assumes a given channel correlation function,which is based on a non-LOS model, while the current I2Vcommunication scenario is mainly LOS. Assuming the wrongstatistical properties of the channel leads to a high error rate.

As already commented in Fig. 3, at 1 s the channel hasa very weak LOS component due to shadowing and also alarge delay spread. None of the tested channel estimators cancope with these bad channel conditions. Regardless of the usedtechnique, no frame is decoded correctly. On the other hand, at5 s the LOS component is very strong and the estimation can

0 2 4 60

0.2

0.4

0.6

0.8

1

Time [s]

FE

R

0 1 2 3 4 5 6 710

20

30

40

Time [s]

SN

R [d

B]

B−LSB−MMSECLS−linearCLS−splineBCMMSE

1

BCMMSE2

Fig. 5. Comparison of different channel estimators and equalizers. FER overtime for a maximal SNR of 40 dB.

be performed satisfactorily. Therefore a FER of 0 in almostall tested cases is achieved.

If we focus now on seconds 3 and 4, we observe a significantperformance difference between the estimators. During thistime interval the effect of the diffuse component becomesrelevant. Even though we have a higher SNR at 3 and 4 s, theFERs achieved are lower in average than the ones achievedat 6 s, where the SNR is much lower. This points out theinfluence of the diffuse components in the channel estimationperformance.

The estimation technique achieving the worst FER arethe block-based channel estimators. Since the SNR at theseinstances is still high, above 30 dB, there is no performancedifference between B-LS and B-MMSE.

The BC-MMSE2 estimator follows the block type estimatorsachieving a FER below 0.3. In this case, the estimation of thechannel time-correlation function provides a good estimate ofthe time-variability of the channel. This improves the perfor-mance of a simple block pilot based channel estimator. Thetwo estimators performing best in this area are the comb-pilot-based estimators. By using the four pilot-dedicated subcarriers,CLS-linear and CLS-spline get a very good estimate of thephase evolution during the frame.

Figure 6 shows the true and the estimated channel transferfunction for subcarrier 21 for one channel realization. In Fig.6 (a) and (b), the channel phase and magnitude are plotted.These channel estimators that take into account time evolutionare able to estimate a changing phase and magnitude. Thisis the case for CLS-linear, CLS-spline, and BC-MMSE2.As commented before, BC-MMSE1 also accounts for timevariability, but not correctly, therefore we see a wrong timeevolution in phase and magnitude. For the block-based channelestimators no time variation is considered. In Fig. 6 (c), thereal and imaginary part of the true and estimated channeltransfer function are plotted. There we see that the closest

0 10 20 30 40

−100

−90

−80

−70

−60

−50

−40

−30

−20

OFDM symbol in a frame of 200 bytes

Pha

se [°

]

HH

BLS

HBMMSE

HCLS−linear

HCLS−spline

HBCMMSE1

HBCMMSE2

(a) Channel Phase

0 10 20 30 40

−45

−44

−43

−42

−41

−40

−39

−38

OFDM symbol in a frame of 200 bytes

Mag

nitu

de [d

B]

HH

BLS

HBMMSE

HCLS−linear

HCLS−spline

HBCMMSE1

HBCMMSE2

(b) Channel Magnitude

−2 0 2 4 6

x 10−3

−13

−12

−11

−10

−9

−8

−7

−6

x 10−3

Re(h)

Im(h

)

HH

BLS

HBMMSE

HCLS−linear

HCLS−spline

HBCMMSE1

HBCMMSE2

(c) Channel

Fig. 6. Estimated and true channel for subcarrier 21 at time 4 s for a maximalSNR of 40 dB (SNR4s = 33.5 dB.)

approximation to the true channel is given by the comb-type channel estimators. Although the tendency in the timeevolution of the phase is well estimated in these cases, insubfigure 6 (a) we see that there is an offset with respectto the true phase. Since we are using a QPSK modulation,as long as the phase offset is within ±45 degrees; the Rxdecides for the correct symbol. Figure 7 presents the averagephase offset per subcarrier over the 100 transmitted frames at4 s. With a modulation where the symbols are closer, the FERwill increase. We observe in this figure as well that there is

10 20 30

10

20

30

40

50

OFDM symbol in a frame of 200 bytes

Sub

carr

ier

inde

x

0

1

2

3

4

5x 10

−5

(a) B-MMSE

10 20 30

10

20

30

40

50

OFDM symbol in a frame of 200 bytes

Sub

carr

ier

inde

x

0

1

2

3

4

5x 10

−5

(b) CLS-linear

10 20 30

10

20

30

40

50

OFDM symbol in a frame of 200 bytes

Sub

carr

ier

inde

x

0

1

2

3

4

5x 10

−5

(c) BC-MMSE2

Fig. 8. Time-frequency MSE at 4 s for a maximal SNR of 40 dB (SNR4s = 33.5 dB.)

10 20 30 40 500

20

40

60

80

Subcarrier index

mea

n an

gula

r of

fset

[°]

Fig. 7. Average phase offset per subcarrier for CLS-linear channel estimatorat 4 s.

a range of subcarriers where the interpolation does not workproperly. This is because the subcarrier 26 is in a fade notchand, since it is in between two of the comb pilot positions, theinterpolation is not able to estimate this subcarrier properly.

In order to further quantify the performance of the estima-tors over time and frequency, we calculate the MSE over the100 frames per each subcarrier and OFDM symbol as

MSE[m, q] =1F

F∑

f=1

∣∣∣hf [m, q] − hf [m, q]∣∣∣2

. (6)

In (6), the number of transmitted frames is F = 100,m = 1 . . . 37 is the OFDM symbol index, q = 1 . . . 52 denotesthe subcarrier number, and hf [m, q] and hf [m, q] are the truechannel transfer function for the frame f and its estimate,respectively. We show the results of the MSE calculation inFig. 8. Comparing Fig. 8 (a) and (c), it can be clearly noticedthat the BC-MMSE2 estimator presents less degradation intime than the B-MMSE. On the contrary, the CLS-linearestimator does not show any degradation in time, but someof the subcarriers are not well estimated, and thus have a highMSE.

IV. CONCLUSIONS

We presented the results of a PHY-layer 802.11p simula-tor using a realistic non-stationary vehicular channel model.We have shown that the channel estimation performance isstrongly influenced mainly by two factors: the diffuse compo-nents strength, and the SNR. Whereas comb-based estimatorsperform best in environments with rich diffuse componentsand high SNR, they do not reach a satisfactory result forlow SNRs. On the other hand, we find that block- basedchannel estimators perform well in the opposite case. The

phase evolution is well estimated in the comb-type estimators,but a phase offset is present in almost subcarriers. Thisphase offset stays within ±45 degrees which is the phaseseparation between symbols for the used QPSK modulation,and consequently no wrong symbol decisions are made for theused modulation. Due to the frequency separation betweencomb-pilots, fade notches can not be estimated. We expectto achieve much better results using a midamble, which willallow to track changes in time for all subcarriers.

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