controlled ofdm measurements at extreme velocitiesone ofdm frame @ =v 400km/h or four ofdm frames @...

Controlled OFDM Measurements at Extreme Velocities

Sebastian Caban, Ronald Nissel, Martin Lerch, and Markus RuppVienna University of Technology, Austria

http://www.nt.tuwien.ac.at

ABSTRACTLTE is designed to work at velocities exceeding 400 km/h.Measuring the physical layer performance that can be achievedat such high speeds in real-world scenarios is conceptuallystraightforward when using off-the-shelf test equipment indrive-by measurements. However, such measurements arenot well suited to directly compare, for example, the biterror ratio of OFDM transmissions at different velocities,signal-to-noise ratios, or pilot power levels, as the channelsare neither repeatable nor controllable.

In this paper we present a controlled measurement setupthat delivers such high-speed channels while avoiding large-scale shadowing. We use the setup to focus on the effect ofa single parameter on a transmission in a multipath small-scale fading environment with large Doppler spread. In thispaper, we present measurements of LTE-like OFDM signalswhile changing the parameters velocity, signal-to-noise ratio,and pilot power.

1. MOTIVATIONOnce an algorithm is working in simulation, the next step isto find out whether it is also working in real-world. Fortu-nately, with the advent of off-the-shelf hardware such as theEttus USRP family, measuring the physical-layer through-put of wireless communication systems became convenientand affordable [1]. To measure at high velocities is also con-ceptually straight-forward: For example, one could measurefrom a fixed base-station to a high-speed car or train [2, 3]or even a jet fighter [4, 5].

While such “drive-by” measurements deliver real-world re-sults combined with important insights into the performanceof communication systems, they do not allow for controlledexperiments. Technically speaking, one cannot compare theresults of a control sample against different experimentalsamples that are recorded under identical environmentalconditions except for the one parameter whose effect is beingtested. In our case, we are interested in the physical layerbit-error performance of an Orthogonal Frequency-Division

Permission to make digital or hard copies of all or part of this work forpersonal or classroom use is granted without fee provided that copies arenot made or distributed for profit or commercial advantage and that copiesbear this notice and the full citation on the first page. To copy otherwise, torepublish, to post on servers or to redistribute to lists, requires prior specificpermission and/or a fee.ExtremeCom ’14, August 11-15, 2014, Galapagos Islands, Ecuador.Copyright 2014 ACM 978-1-4503-2929-3 ...$15.00.

Multiplexing (OFDM) transmission while changing, for ex-ample, only the velocity, only the average Signal-to-NoiseRatio (SNR), or only the pilot power level.

Even if their validity may be questionable, we believe thatnext to simulations and real-world measurements, controlledexperiments can deliver important (and convenient) input totest existing theories and new hypotheses in wireless com-munications.

2. MEASUREMENT SETUPTo be able to repeatably measure the physical-layer perfor-mance of a wireless communication system we published anidea in 2011 [6] (see Figure 1):

v = 0 to 560 km/h

antenna

2 rotaryjoints

r = 1 m

counterweight

signal feedto receivehardware

rail to move the whole set-up

electricmotor

laser-lightbarrier

Figure 1: Rotating a receive antenna around a cen-tral pivot creates a repeatable high-speed channel.

We then planned to place this setup, for example, in acorridor in the cellar (a tunnel), or on the roof (open field)to achieve different fading scenarios. Whether this set-upfully represents real-live situation is subject to discussion.In any case, we believe that it is useful for the initial test-ing of algorithms and devices in research and developmentas it allows for measurements that would otherwise requireenormous efforts.

Figure 2 shows this setup as it was taken into operationin late 2013:

Copyright 2014, In Proc. EXTREMECOM 2014, San Cristobal, Galapagos, Ecuador

counterweight

receiveantenna

signals are fedthrough the rod

r = 1 mrotating atf = 25 Hz

rotaryjoint

channel 2

(note: two-channelrotary joints will

be developed if required)rotaryjoint

channel 1

railto move

the wholesetup

linear guide to move

the wholesetup

laser-lightbarrier

(test setup)

v = 0 to 560 km/h

electricmotor

axis

signal feedto receivehardware

cable tovariable-frequency

drive

Figure 2: The finished set-up to move an antenna at up to 560 km/h.

3. MEASUREMENT PROCEDUREIn the measurements we now carry out with the setup shownabove, we combine

• the idea of creating a, and always the same, high-speedchannel by rotating a receive antenna around a centralpivot

• with visualizing the performance of a wireless communi-cation system as the average-performance obtained in asmall-scale Rayleigh or Ricean fading environment (thatis created by moving the transmit and/or receive antennawithin a small area) [7].

To do so, we utilize a wireless Testbed developed and setup at the Vienna University of Technology [8, 9, 10]. Ameasurement consists of the steps below — as one would doit in a simulation:

1. We transmit several OFDM frames (a control sample) ata center frequency of 2.5 GHz from a static transmit an-tenna to a receiver moving at a certain velocity. The num-ber of bit errors, the SNR, and the Signal-to-Interference-plus-Noise Ratio (SINR) are then calculated off-line in

Matlab from the signal received.

2. Directly after these OFDM frames, we repeat the trans-mission at identical environmental conditions except forthe one parameter whose effect is to be investigated, forexample, the power of the pilots or the transmit power(to change the SNR at the receiver).

Note that when repeating the measurement, the trans-mission is carried out in such a way that the path of thereceive antenna during the transmission of the OFDMframes is exactly equal. This is ensured by keeping thevelocity of the antenna constant and starting at the sameposition using a laser light barrier and dedicated real-timesynchronization hardware [9].

3. We move the transmit antenna by 0.21 wavelengths (ap-proximately 2 cm at a center frequency of 2.5 GHz) to cre-ate a new realization of a small-scale fading environmentthat we arbitrarily define to be three times three wave-lengths large.

Note that for convenience, all experiments in this paperare carried out indoors. When measuring from a fixed


one OFDM frame @ v = 400 km/h

or four OFDM frames @ v = 100 km/hor two OFDM frames @ v = 200 km/h

Figure 3: Scaled 1:1, at 400 km/h the receive antenna moves 13 cm during the transmission of one OFDMframe. Even if being moved around a central pivot on a circle with a radius of one meter, the path of thereceive antenna is approximately linear.

base-station mounted on a roof, moving the transmit an-tenna is not suitable anymore to create a different real-ization of the same small-scale fading scenario. We thenwould alternatively move the whole receiver set-up, whichwe do not do in this paper.

4. We repeat Step 1 to Step 3 until a total of 225 OFDMframe-sets have been transmitted, that is, the wholesmall-scale fading environment has been sampled.

5. We calculate the average bit error ratio from the 225frame-sets received (the dots plotted in the figures). Inaddition, we calculate the 95% BCa bootstrap confidenceintervals for the mean to gauge the precision of the re-sults [11] (the vertical lines in in the figures).

As mentioned in Step 1, we always transmit several OFDMframes. This is necessary as when changing the velocityin our measurements from 100 km/h to 200 km/h, next to300 km/h and finally to 400 km/h, we want to ensure thatthe fading experienced by the receive antenna is equal. To doso, we transmit 3 frames at 400 km/h, 4 frames at 300 km/h,6 frames at 200 km/h, and 12 frames at 100 km/h — suchthat the path moved in total by the receive antenna is equalfor all velocities. Table 1 summarizes the measurement pa-rameters chosen.

Table 1: Measurement ParametersCarrier frequency 2.5 GHzVelocity 100, 200, 300, 400 km/hRX movement length 3.3λTX position range 3λ×3λNumber of TX positions 225Number of TX power levels 8, in 5 dB steps

4. THE TRANSMIT SIGNALWe consider an LTE-like signal whereas, for convenience, weemploy a rectangular pilot-symbol pattern and adjust thenumber of subcarriers and OFDM symbols so that pilot-symbols are located at corner positions. With respect toPilot-symbol-Aided Channel Estimation (PACE), we thusonly investigate interpolation of the channel but ignore ex-trapolation. Additionally, the rectangular grid allows forinterpolation first in one dimension and then again in theother dimension. One subframe consists of K = 17 OFDMsymbols, each including L = 73 subcarriers.

The OFDM signal in the time domain is generated by anInverse Fast Fourier Transform (IFFT), applied to L datasymbols. The demodulation is performed by a Fast FourierTransform (FFT). If the cyclic prefix is sufficienty long,the whole OFDM transmission chain can be described by a

simple input-output relationship:

yk = Dkxk + zk, (1)

where xk = [ x1,k . . . xL,k ]T ∈ CL×1 denotes the trans-mitted data symbols at time position k (k = 1, 2 . . . ,K)and subcarrier position l (l = 1, 2 . . . , L), chosen from a 4-Quadrature Amplitude Modulation (QAM) signal constella-tion. The matrix Dk describes attenuation, phase rotation,and Inter-Carrier Interference (ICI), zk denotes the addi-tive white Gaussian noise, and yk = [ y1,k . . . yL,k ]T ∈CL×1 denotes the received data symbols. Table 2 summa-rizes the transmit signal parameters chosen.

Table 2: Transmit Signal Parameters

Modulation type OFDMModulation order 4-QAMNumber of subcarriers, L 73Number of OFDM symbols, K 17Pilot pattern rectangularFrequency pilot spacing 6Time pilot spacing 4

Subcarrier spacing, ∆f 15 kHzCyclic prefix duration 4.67µs

5. MEASUREMENTS

5.1 SINR VS. SNRThe diagonal elements of the matrix Dk are piecewise timeaverages of the channel transfer function and characterizethe desired signal components, while the off-diagonal ele-ments correspond to ICI. By investigating only one sub-carrier, the received data symbol power E{yl,ky∗l,k} can bewritten as:

E{yl,ky∗l,k} =

E{∣∣∣(Dk)l,l

∣∣∣2}︸︷︷︸PSl,k

+

L∑d=1d6=l

E{∣∣∣(Dk)l,d

∣∣∣2}︸︷︷︸

PICIl,k

+E{|zl,k|2

}︸︷︷︸PZl,k

,(2)

where PSl,k denotes the signal power, PICIl,k the ICI powerand PZl,k ∈ R the noise power.

For infinitely many subcarriers and a given temporal au-tocorrelation function, a closed form solution for the signalpower distribution due to Doppler is given by

PSJakes = 1F2

(1

2;

3

2, 2;−(πνmax)2

)(3)

for a two dimensional propagation where the scattering ele-


ments lie on a circle (outdoor) and by

PSuniform =cos(2πνmax) + 2πνmaxSi(2πνmax)− 1

2(πνmax)2(4)

for a three dimensional propagation where the scatteringelements are located on a sphere (indoor) [12]. The variableνmax denotes the ratio between maximal Doppler frequencyand subcarrier spacing. Due to infinitely many subcarriers,the ICI power is given by PICI = 1− PS.

In order to estimate the noise power, we set the first andlast OFDM symbols to zero, that is, x0 = 0 and xK+1 = 0(see Equation (1)). The estimated noise power PZ is thengiven by the average of the received symbol power of |yl,0|2and |yl,K+1|2.

The ICI power is estimated in a similar way by settingthe first and last subcarrier to zero, that is, x0,k = 0 andxL+1,k = 0. Because the first and last subcarrier have inter-ferers only on one side, the ICI power is halved comparedthe subcarriers located within the signal. Again, averagingthe data symbols |y0,k|2 and |yL+1,k|2 gives the estimated

power PICI/2 + PZ.

Once we have estimated the ICI power PICI and the noisepower PZ, we use Equation (2) at each time-frequency posi-

tion, to estimate the signal power PSl,k .We define the SNR, Signal-to-Interference Ratio (SIR)

and SINR as:

SNR =PS

PZ

(5)

SIR =PS

PICI

(6)

SINR =PS

PICI + PZ

(7)

Figure 4 shows, for different velocities, the measured SIRand the theoretical SIR for a uniform and Jakes Dopplerspectral density. Compared with the theoretical curves, weend up with a higher SIR, because the waves do not incidentuniformly from all directions:

velocity (km/h)

Signalto

Interference

Ratio(dB) Measurement

velocity (km/h)

Signalto

Interference

Ratio(dB)

measurement

model: uniformmodel: Jakes

95% confidence intervalestimated scenario mean

Figure 4: The SIR in our setup is higher than inthe theoretical models of Jakes and uniform distri-bution as the waves do not uniformly arrive from alldirections.

If exploiting the whole 360◦ range of the receive antennarotating around the central pivot, a SIR curve very close toa uniform Doppler spectral density is obtained.

Figure 5 shows the SINR as a function of SNR. For lowSNRs, the ICI power can be neglected. Furthermore, we seea saturation effect of the SINR at high SNRs because theICI power scales linearly to the signal power. This meansthat, due to ICI, the performance of a system can no longerbe improved by simply transmitting at higher signal power.Only sophisticated receiver architectures which include ICImitigation can then improve the performance.

Signalto

Interference

plusNoiseRatio[dB].

mean SNR at the receiver [dB]

Signalto

Interference

plusNoiseRatio[dB].


v=100km/hv=200km/hv=300km/h

v=400km/h


Figure 5: By changing only the velocity and trans-mit power, we measure the SINR achieved for dif-ferent velocities over average SNRs at the receiver.Note that the measurement environment is keptconstant making the comparison fair and precise.

5.2 BER VS. VELOCITYIn this section, we treat ICI as an unknown additional noiseterm, so that OFDM can be seen as transmitting the datasymbol xl,k over a 2D time-frequency grid, attenuated byhl,k = (Dk)l,l ∈ C and corrupted by ICI plus noise:

yl,k = hl,kxl,k + yICIl,k + zl,k. (8)

At the receiver side, the channel is estimated by PACE

hl,k = wHl,khLS

P , (9)

where hLSP denotes the Least Squares (LS) channel estimates

at pilot position and wl,k denotes a specific interpolationmethod. The data symbols are then estimated by an LSestimation:

xl,k =yl,k

hl,k

. (10)

After demapping the estimated data symbol xl,k (minimalEuclidean distance, Gray coding) we get an estimate of thetransmitted bit stream.

Under the assumption of a Rayleigh fading hl,k and Gaus-sian ICI plus noise yICIl,k + zl,k ∼ CN (0, PICIl,k + Pnoisel,k ),we found a closed form solution for the Bit Error Proba-bility (BEP) as a function of the signal power PSl,k , ICIpower PICIl,k , the noise power Pnoisel,k , the channel corre-lation E{hl,kh

∗l,k}, the interpolation method wl,k, and the


modulation order [13].In order to test this closed form solution we first measure

the Bit Error Ratio (BER) over velocity for different averageSNRs using our setup. Next, we obtain the ICI power, thenoise power, and the channel correlation of the scenario fromthe signals measured. Finally, we compare the theoreticalBEP to the measured BER as shown in Figure 6. Notethat this comparison can only lead to a high agreement, iflarge-scale shadowing is not present in the measurement.

velocity [km/h]

bit

errorratio/bit

errorprobability

velocity [km/h]

bit

errorratio/bit

errorprobability

SNR=0dB

SNR=15dB

SNR=30dB


measurementclosed formexpressions

Figure 6: By measuring in a controlled environment,we test a theoretically established relationship, inthis example, the BEP over velocity in a Rayleighfading scenario with 2D MMSE channel estimation.

In general, two main effects influence the performance ofOFDM systems at high velocities. Firstly, the ICI degen-erates the performance if the ICI power exceeds the noisepower. Secondly, the channel may fluctuate so rapidly thatat a given pilot-symbol spacing in the time domain, the chan-nel estimation violates the sampling theorem. This effectexists at all noise power levels.

In Figure 6, only the first effect can be identified becauseour LTE-like signal employs a very close pilot-symbol spac-ing in the time domain, so that velocities of up to 500 km/hdo not violate the sampling theorem.

5.3 BER VS. SNR VS. PILOT POWERThe BER performance of an LTE-like transmission is notonly dependent on the SNR of the received data symbols butalso on the quality of the channel estimation. To optimizethe quality of the channel estimation, LTE allows the pilotsymbol power to be adjusted with respect to that of the datasymbols. Such power increase of the pilot symbols results ina more accurate channel estimate, but in turn reduces theamount of power available for the data transmission as thetotal transmit power is to be kept constant [14].

To measure the effect of an increased pilot power at avelocity of 400 km/h, we carry the controlled experimentoutlined below:

• We measure the BER of an OFDM transmission wherethe average power per pilot-symbol is equal to the averagepower per data-symbol (one control sample).

• Under exactly the same channel conditions, we measurethe BER of an OFDM transmission with the same total

power but a by 3 dB increased pilot power (one experi-mental sample with +3 dB pilot power)

• We then change the transmit power to alter the averageSNR of the scenario and the position of the transmitterto create a different realization of the same small-scalefading szenario.

The top of Figure 7 shows the two BER curves obtained bythis procedure when linear interpolation is used for the chan-nel estimator. Note that although curves look very smoothas they have been obtained under exactly the same environ-mental conditions, their absolute position is not very accu-rate as only 225 channel realizations have been measured:


bit

errorratio[]


bit

errorratio[]


relativeBER

improvem

ent[]


relativeBER

improvem

ent[]

95% confidence interval

control sample

--- ABSOLUTE ---

--- RELATIVE ---

+3dB pilot power

relative improvement

estimated scenario mean



estimated scenario mean

of the estimated scenario mean

The confidenceintervals of the absolute

BER values overlap

The confidence interval of the BER difference starts at -9%.

v=400km/h

v=400km/h

Figure 7: Measuring the effect of increasing thepilot power of an OFDM transmission by 3 dB at400 km/h while keeping the total signal power con-stant: Looking, for example, at the absolute val-ues for 15.5 dB mean SNR, we cannot be confidentto have a significantly improved the BER. On theother hand, the relative BER improvement observedis significant. The reason is that both, the controlsamples and the samples with +3dB pilot power aremeasured under the exactly same set of environmen-tal conditions, thus fluctuations in the channel can-cel each other when looking at the relative differ-ence.


In principle, we could increase the accuracy by takingmore samples. However, this is not always feasible as thesamples will get correlated if measuring too many of themin a small area. In addition, measurement time will increaseexponentially.

On the other hand, a measurement with high absolute ac-curacy is not required, as the confidence intervals of the rel-ative difference between the two curves suggest (see bottomof Figure 7). The reason is that the two curves have beenmeasured at the same set of positions within a small-scalefading scenario. When looking at the relative difference, thefluctuations in the scenario cancel each other and we observea significant BER improvement, for example, more than 9%for 15.5 dB mean SNR.

Note that we have calibrated the whole transmission andreception chain prior to the experiment. Thus the meanscenario SNR can be estimated precisely; its confidence in-tervals are too small to be shown in the figures.

6. CONCLUSIONIn this work we have shown that by rotating an arm arounda central pivot, the performance of wireless communicationsystems experiencing large Doppler spreads can be measuredin an controlled experiment. This allows to conveniently andprecisely investigate the effect of a single parameter as thetransmission is equal in every other aspect:

• We avoid a changing path-loss and large-scale shadowingin high-speed scenarios by moving the transmitter and thereceiver by not more than three wavelengths throughoutthe whole experiment. (Actually the receiver is moved ina large circle, but periodically, which allows us to trig-ger all transmissions within always the same, small, well-defined area.) Within these bounds, we then measureseveral samples of a small scale fading scenario to calcu-late the average performance and gauge the accuracy ofthese results by bootstrapping.

As we are able to control the average SNR of our en-vironment (by measuring it once and then adapting thetransmit power), we can directly compare theoretical re-sults for Rayleigh and Ricean fading scenarios at differentaverage SNRs to measurements.

• We show that measurements at different velocities can becompared fairly by adapting the number of blocks trans-mitted in such a way that the receiver moves exactly thesame path at different velocities.

• We show that — even at very high velocities — the per-formance of different transmit signals can be comparedprecisely if measured under exactly the same high-speedchannel conditions. The reason is that by doing so, vari-ations of the fast changing channel that would otherwisedominate the results cancel each other during the com-parison.

While the high internal validity, combined with the conve-nience of being able to carry out a high-speed measurementin less than 5 minutes, make controlled experiments an in-teresting tool for the initial investigation of algorithms, theexternal validity of the results obtained is subject to debate.

AcknowledgementsThis work has been funded by the Christian Doppler Laboratory forWire- less Technologies for Sustainable Mobility, the A1 Telekom Aus-tria AG, and the KATHREIN-Werke KG. The financial support bythe Federal Ministry of Economy, Family and Youth and the NationalFoundation for Research, Technology and Devel- opment is gratefullyacknowledged.

References[1] J. Rodrıguez-Pineiro, J. A. Garcıa-Naya, A. Carro-Lagoa, and

L. Castedo, “A testbed for evaluating LTE in high-speed trains,”in 2013 Euromicro Conference on Digital System Design, pp.175–182, Sept 2013, doi: 10.1109/DSD.2013.27.

[2] L. Liu, C. Tao, T. Zhou, Y. Zhao, X. Yin, and H. Chen, “A highlyefficient channel sounding method based on cellular communica-tions for high-speed railway scenarios,” EURASIP Journal onWireless Communications and Networking, vol. 2012, no. 1,pp. 1–16, 2012.

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[4] Youtube – Testing LTE Onboard A Jet Plane Flying At700km/h.http://www.youtube.com/watch?v=ROZCTtgOn6s

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[8] M. Lerch, S. Caban, M. Mayer, and M. Rupp, “The viennaMIMO testbed: Evaluation of future mobile communicationtechniques,” Intel Technology Journal, vol. 4G Wireless Com-munications: Real World Aspects and Tools, no. 3, pp. 58–69,2014.

[9] S. Caban, A. Disslbacher-Fink, and J. A. Garcıa-Naya,“Synchronization of wireless radio testbed measurements,” inProc. IEEE Instrumentation and Measurement TechnologyConference (I2MTC2011), Binjiang, Hangzhou, China, May2011, doi: 10.1109/IMTC.2011.5944089.http://publik.tuwien.ac.at/files/PubDat 201643.pdf

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http://dx.doi.org/10.1109/DSD.2013.27

http://www.youtube.com/watch?v=ROZCTtgOn6s

http://dx.doi.org/10.1109/IMTC.2011.5944019

http://dx.doi.org/10.1109/MIM.2011.6041377

http://dx.doi.org/10.1109/MIM.2011.6041377

http://publik.tuwien.ac.at/files/PubDat_201647.pdf

http://dx.doi.org/10.1109/IMTC.2011.5944089


http://dx.doi.org/10.1109/VETECF.2009.5378994



http://at.bookbutler.com/do/bookCompare?searchFor=0412042312&amountIn=eur&shipTo=at&searchIn=de&zip=


controlled ofdm measurements at extreme velocitiesone ofdm frame @ =v 400km/h or four ofdm frames @...

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