Performance Analysis of Video Transmission over MIMO SDR Systems

8/12/2019 Performance Analysis of Video Transmission over MIMO SDR Systems

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Performance Analysis of VideoTransmission over MIMO SDR Systems

Adnan Ahmed Khan, Safwat Irteza, Abubakar Rehman, Atif Sajjad, Qutab ud din, Raja Asad Ajmal

EE Department, Military College of Signals-NUST, Rawalpindi. 46000

E-mail: {adkhan100, safwatirteza, abubakarrehman7, atifsajjad5, qazi.qutab, rajaasad87 }@gmail.com

Abstract Real-time analysis of a spatial multiplexing sys-tem for multimedia transmission is performed in this paper.Also performance verication of MIMO system is importanttherefore we propose an SDR-based MIMO testbed to vali-date the theoretical performance gain. Real-time transmis-sion of live video is used using USRP as a front end to showthat MIMO systems are capable of supporting multimediaapplications at enhanced data rates compared to SISO with-out needing to increase the system bandwidth. Here we pro-pose a simple MIMO clock synchronization scheme utilizingthe USRP clock structure to our advantage. LSE ChannelEstimation and Zero-Forcing Equalizer are used to reducecomputational complexity and enable the MIMO system to

run at high speed.

I. Introduction

The use of multiple transmit and receive antennas canimprove the performance of wireless communication sys-tems signicantly by providing higher data rates andhigher spectral efficiency. MIMO systems realize thesegains by introducing temporal and spatial correlation intothe signals transmitted from different antennas without in-creasing the total transmitted power of transmission [1].Although MIMO communication has drawn a lot of atten-tion in industry and academia, the main focus so far hasbeen on the theoretical aspects. For validating the resultsof theory and simulations and gaining practical experience,testbeds are essential. The main benet of a testbed isthe possibility to study and compare algorithms in real-istic environments before committing them to silicon [2].

The demand for higher data rates over reduced band-width has been one of the primary motivations behind thedevelopment of communications systems. For years, engi-neers assumed that the theoretical channel capacity limitswere dened by the Shannon- Hartley theorem illustratedin Eq. 1.

Capacity = BW log 2 (1 + SN R ) (1)

As the above equation shows, an increase in a channelsSNR results in marginal gains in channel throughput. As aresult, the traditional way to achieve higher data rates is byincreasing the signal bandwidth. Unfortunately, increasingthe signal bandwidth of a communications channel by in-creasing the symbol rate of a modulated carrier increasesits susceptibility to multipath fading [9].

MIMO communications channels provide an interestingsolution to the multipath challenge by requiring multiplesignal paths. In effect, MIMO systems use a combination of multiple antennas and multiple signal paths to gain knowl-edge of the communications channel. By using the spa-

tial dimension of a communications link, MIMO systemscan achieve signicantly higher data rates than traditionalsingle-input, single-output (SISO) channels. Using chan-nel knowledge, a receiver can recover independent streamsfrom each of the transmitters antennas. A 2 x 2 MIMOsystem produces two spatial streams to effectively doublethe maximum data rate of what might be achieved in atraditional SISO communications channel [4].

A. Software Dened Radio

An SDR is a radio that is built entirely or in large partsin software, which runs on a general purpose computer. Amore extensive denition is given by Joseph Mitola in [3].The fundamental characteristic of software radio is thatsoftware denes the transmitted waveforms, and softwaredemodulates the received waveforms. GNU Radio is such afree software toolkit for building software radios. Softwareradio is a revolution in radio design due to its ability tocreate radios that change on the y, creating new choicesfor users.

An RF interface for GNU Radio architecture is realizedby USRP boards, a general purpose RF hardware, whichperforms computationally intensive operations as ltering,up- and down-conversion. The USRP and its recent versionUSRP2 are connected to PC over a USB 2.0 and Ethernetcable, respectively, and are controlled through a robust ap-plication programming interface (API) provided by GNURadio.

II. System Model

In our system we input live video streaming from a cam-era into the programming language on a GPP. The entirebaseband signal processing like packet formation, serial toparallel conversion, modulation etc. is done on the GPP.The modulated signal is then passed to the USRP1 whichused as RF front end. The two different data streams aretransmitted at RF (2.4GHz) by the two daughter cards(RFX 2400 A & B) mounted on USRP1. The completesystem model is shown in Fig. 1. The transmitted signalx undergoes channel effects denoted by H and AdditiveWhite Gaussian Noise n.

The received signal y can be written as

Capacity = y = H x + n (2)

On the receiver the clock synchronization of the two USRPmaster clocks takes place along with frequency synchro-nization. After estimating the channel coefficients of H,


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Fig. 1. System Model

from the received signal y the transmitted signal is recov-ered using ZF detection. The parallel streams are com-bined and the packets are decoded to recover the trans-mitted video frames and are subsequently displayed in real-time.Fig. 2 shows system setup snapshot of a 2 x 2 MIMOTestbed.

Fig. 2. System Setup of 2 x 2 MIMO Testbed

III. System Design

A. Transmitter Design The complete transmitter design is given in Fig. 3

Fig. 3. Block Diagram of Transmitter Design

A.1 Importing Video and Frame Extraction

Importing video in the GNU radio python is a difficulttask as python itself does not provide with a built-in li-brary to do it. OpenCV is a very useful tool to importvideo in GNU radio python. OpenCV was designed forcomputational efficiency and with a strong focus on real-time applications. It is written in optimized C and cantake advantage of multi-core processors [5].

To capture live video, rst the camera capture must beinitialized. After the camera has been initialized we canquery the camera to read a video frame whenever the cam-era is ready. The video frame captured is then broken downinto 4kB payloads and after adding the header, are sendover different antennas. The payloads recieved at the other

end are combined to recover the video frame and displayed.The video format used is MJPEG.

A.2 Packet Design and ModulationThe actual structure of the frame is shown in Fig. 4. At

the start there is an 8-bit ag which represents the start of a data frame. Then a variable length packet number eldfollowed by another 32-bit ag ( F F F F )16 which denotesthe end of the packet number eld. Then is the payloadeld which contains the actual data and in the end thereis a 32-bit CRC for correct packet detection. The totallength of the frame is 4Kb. The modulation scheme usedfor both SISO and MIMO communications is Gaussian l-tered Minimum Shift Keying, GMSK, a form of modulationused in a variety of digital radio communications systems.GMSK modulation is based on MSK, which is itself a formof phase shift keying.

Fig. 4. Data Link Layer Packet Structure

B. Receiver Design

The complete receiver design is shown in the Fig. 5:

Fig. 5. Block Diagram of Receiver Design

B.1 Carrier Recovery

Carrier frequency recovery is performed by frequencymultiplexing a low power pilot signal with the data sig-nal. The decision to use a dedicated pilot, which requiresadditional transmit power, was made to eliminate the ef-fects of carrier frequency estimation and tracking errorswhen testing various MIMO detectors. Fig. 6 shows a plotof the spectrum for a signal packet. The single frequencytone, positioned at exactly 0.8 rad/sample, representsthe pilot signal. As depicted in Fig. 7, the pilot signal

Fig. 6. Spectrum of a single packet showing frequency multiplexedcarrier

is used directly to compensate for frequency offsets. Thismethod provides perfect frequency synchronization at the


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cost of noise enhancement. In practice, however, the low-pass lter applied to the pilot eliminates enough of theout-of-band noise that the nal increase in noise varianceis typically less than the noise variance estimation error.

Fig. 7. Pilot Recovery Circuit

B.2 Timing Recovery

As both daughter cards are mounted onto the samemotherboard, they are both driven by the same masterclock. We can exploit this property to simplify the process

of timing recovery. So we have to synchronize only onedaughter board at the transmitter side with one daugh-ter board at the receiver side. In effect, this becomes aSISO scheme. So the boards can be synchronized usingand SISO technique before commencing data transmission.These clocks are synchronized before actual data transmis-sion starts, during the phase of channel estimation whenthe pilots are transmitted by one antenna at a time. Oncethe clock has been synchronized, it is assumed that theyremain synchronized throughout the phase of MIMO datatransmission.

Symbol timing recovery is a crucial aspect of synchro-nization. It is important that there be some mechanism to

know when decision samples for every symbol are to betaken once the baseband signal has been received at thereceiver. The ideal location of the sampling instants fordecision making is depicted in Fig. 8.

Fig. 8. At transmitter side after DAC or reconstruction lter

Fig. 9. At receiver side with un-optimized sampling locations

The actual scenario is not like the ideal situation. Afreely running sampler samples samples the received ana-log signal. It is highly unlikely that the sampling instants

will be located at the optimum location for decision mak-ing which is the center of symbol. If a decision is based onsuch samples, noise tolerance will decrease i.e even a smallamount of noise may cause a wrong decision which in turnwould drastically increase the bit error rate as shown inFig. 9. Therefore, in order to shift these sampling instantsto an optimum location symbol timing recovery must bedone. The timing synchronization process consists of twostages. First, we nd an estimate of how much far thesamples are from optimum location, as shown in Fig. 10.Then this estimated timing offset and the wrongly sam-pled signal are passed to the fractional delay lter whichconceptually interpolates the signal and then performs re-sampling at optimum location based on the estimate, est ,passed to it.

Fig. 10. The mechanism of Symbol Timing Recovery

The Muller and Muellers decision directed timing offsetestimator has been used in order to estimate the timingoffset, est . Its DFT based algorithm was given by M.Oerder and H. Meyr as: [6]

Where the sgn (.) function represents the decision di-rected portion of the estimator and x is the wrongly sam-pled baseband signal (either real or imaginary part). Thevalue of estimate lies between -0.5 and 0.5 depending onwhere the samples of the received baseband signal lie i.ebetween half-way left and half-way right to the optimumlocation [6] [8].

C. Channel Estimation

Channel Estimation is the process of characterizing theeffect of the physical medium on the input sequence. Itis an important and necessary function for wireless sys-tems. Even with a limited knowledge of the wireless chan-nel properties, a receiver can gain insight into the data sentover by the transmitter. The main goal of Channel Esti-

mation is to measure the effects of the channel on knownor partially known set of transmissions.

C.1 Least Square Error

The Least Squares Error (LSE) estimation method canbe used to estimate the system h[m] by minimizing thesquared error between estimation and detection. In matrixform, it can be written as

y = X h (3)

So the error e can be dened as

e = y y (4)


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Where y is the expected output. The squared error (S)can be dened as e = y-y

S = |e| 2 (5)

S = ( y y)2 (6)

S = (

y

y) (

y

y)

t

(7)Where the super-script t stands for complex transpose

of a matrix.

S = ( y X h ) (y X h ) t (8)

This equation can be minimized by taking its derivativew.r.t h and equating it equal to zero. The nal equationcan be written as,

h = X 1 y (9)

This equation can be implemented on SISO as well as

MIMO systems [7][13].C.2 MIMO Implementation Issues

When inputs are transmitted from Tx antennas, theyare affected by the channel. Each Rx antenna is receivingsignals from each Tx antenna. Now the received signalat Rx antenna is not a product of single channel responseand single input signal but it is combination of signals fromeach Tx antenna multiplied with their respective channelresponses.

C.3 Solution

To overcome these problem matrix properties were ex-ploited. Instead of using one pilot, two pilots were used.The uses of two pilots were to make the matrices squareand the number of equations must be equal to the numberof unknown variables. Then equating the equations chan-nel response for each channel is calculated. Use of multiplepilots is a drawback in multiple antenna system.

IV. Results

A. Increased Data Rates for the Same Utilized Bandwidth

The graphs in the Fig. 11 show the case of Video overMIMO at 4Mbps using GMSK with both transmitters op-

erating at 2Mbps. Both antennas operate at a center fre-quency of 2.4 GHz and 1100 Kbps Bandwidth. Both an-tennas have a peak transmit power of 15dBm

B. Comparison of Transfer Times

Time taken for different les to be transmitted on SISOand MIMO links were also recorded for comparison. TableI shows the increased data rates due to MIMO communi-cation.

A similar comparison was made for different resolutionsof video frames. Fig. 12 shows enhanced frames rates forMIMO communication

Fig. 11. MIMO Link Bandwidth

TABLE IComparison of transmission times for SISO and MIMO links

File Type File Size MIMO (s) SISO (s)

JPEG 333.9KB 0.58 1.03

MP3 3.5MB 7.94 14.67FLV 4.6MB 10.58 20.15AVI 10.9MB 23.95 45.91

V. Analysis of results

The major outcome of this project is the proof of the con-cept that spectral efficiency can be achieved using MIMOcommunication. Enhancement in the data rate is shownutilizing the same bandwidth. All the future work in theeld of communications will be done with the perspectiveof saving the spectrum and increasing data rates. Thegraphs in the Fig. 11 show that both antennas operate si-multaneously at the same frequency enabling us to achievehigher data rate using the same resources. Thus showingthe advantage of MIMO communication over SISO com-munication.

Time taken for different les to be transmitted on SISOand MIMO links were also recorded for comparison. Asshown in Table I, time taken for the le to transfer onMIMO is half as that taken while communicating on SISO.A similar comparison was made for different resolutions of video frames. Fig. 12 shows the enhanced frame rates inMIMO communication which we get due to the increased

Fig. 12. Frame Rates Comparison Between SISO and MIMO


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throughput. This is the result of spatial multiplexing tech-nique.

A. Performance of MIMO over SDR Platform

For real-time processing, the performance of the Cen-tral Processing Unit (CPU) and the sample rate limit thenumber of mathematical operations that can be performedper sample, as samples must be processed as fast as theyarrive. Thus the processing power of a CPU serves as abottleneck in MIMO communication over SDR. In prac-tice, this means that fast CPUs, clever programming andpossibly parallelization is needed to overcome this hurdle.If this does not suffice, a compromise must be found, touse a less optimal but faster signal processing algorithm sothat the data rate does not have to be compromised [12].

Because of the use of general purpose processing units,an implementation of a given wireless application as anSDR is likely to use more power and occupy more proces-sor resources than a hardware radio with analog lteringand a dedicated signal processor.

Since processing power in commercial setups is not anissue, MIMO communications can easily be implementedand can be benetted from, as it has been shown that theyprovide higher data rates using same bandwidth comparedto the conventional SISO communication methods. Also,dedicated hardware design for baseband signal processingcan take the burden off the CPU of the computer to achievemuch higher data rates and spectral efficiency.

The main issue with MIMO receiver is its complexity, asthe number of transmit and receive antennas increase thecomputational complexity increases. The computationalcomplexity is computed in terms of the N t , N r and Mconstellation size. ML detection requires N r [N t + 1] M N t .

complex multiplications. Where M Nt

(NrNt ) is for ma-trix multiplication and M Nt Nr is for square operation.For VBLAST, the pseudo-inverse of matrix ( H H H ) 1 H H

takes 4 N 3t +2 N 2t N r multiplications [10]. Actual number of calculations for ML detection are shown in Table II.

TABLE IIComputational complexi ty comparison

No. of Antennas Complex Multiplications

2 x 2 3843 x 3 7684 x 4 51208 x 8 4.7M

On the other hand zero forcing algorithm is the simplestalgorithm which can be used for MIMO decoding. Becauseof the increasing complexity with increase in number of antennas, ZF is the most suitable solution for SDR basedMIMO systems but the drawback of ZF is that it ampliesthe noise as well, which acts as a trade-off between compu-tational complexity and system degradation. Since com-putational capacity is a bottleneck in case of the generalpurpose processor so system degradation can be catered

by increasing the transmit power. Fig. 13 shows the per-centage usage of a 2 GHz processor to be above 95% evenin case of ZF MIMO receiver. Thus in order to implementmore sophisticated MIMO detection algorithms dedicatedsignal processing hardware is required like FPGA and DSPkits which are also recongurable.

Fig. 13. Percentage CPU usage

VI. CONCLUSION

Researchers who choose to work in the eld of MIMO

require a testbed to evaluate the performance of this tech-nology and this project provides them exactly that. Videois the most bandwidth demanding service and that is whereSpatial Multiplexing comes to the rescue by drastically re-ducing the bandwidth requirement hence enabling betterbandwidth utilization. This project provides a basic yetwell-performing testbed for researchers to use in testingMIMO algorithms and different video transmission tech-niques.

Future work on the testbed will include renement of each component of the receiver with the goal of support-ing higher order modulations, such as multilevel QAM. Thevideo transmitted is currently uncompressed and in the fu-ture mp4 compression could be used to transmit 720p videoat acceptable frame rates. Also, work is currently neededto make an FPGA handle the MIMO decoding scheme suchas ML or Sphere Decoder which would greatly improveBER as the current software method requires too muchprocessing power.

The relatively low cost of the USRP hardware and GNURadio software makes the combination well suited for labo-ratory settings. The simple, extensible design of the GNURadio API gives researchers the exibility to easily reas-sign testbed nodes to different projects as needed.

References

[1] Allert van Zelst , MIMO-OFDM for Wireless LANs, Ph.D.dissertation, Department of EE, Eindhoven University of Tech-nology, Netherlands , 2002 .

[2] S. Lang et al. , Design and Development of a 5.25 GHz Soft-ware Dened Wireless OFDM Communication Platform, IEEE Communication Mag., Radio Comm. Supp. , 2004.

[3] J. Mitola, The software radio architecture, IEEE Communica-tions Magazine , pp. 2638, May 1995

[4] Ana Katalinic , Benets of MIMO Systems in Practice: IncreasedCapacity, Reliability and Spectrum Efficiency, 48th International Symposium ELMAR , 2006.

[5] G. Bradski, A. Kaehler, Learning OpenCV, OReilly Media, Inc.,CA, September 2008

[6] Mueller, K. H., and M. S. Muller , Timing Recovery in DigitalSynchronous Data Receivers, IEEE Transactions on Communi-cations , Vol. COM-24, pp. 516-531, 1976.


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[7] Li, Y., Simplied Channel Estimation for OFDM Systems withMultiple Transmit Antennas, IEEE Transactions on Communi-cations ,vol. 1, pp. 67-75, January 2002.

[8] D. Palchak, B. F. Boroujeny , A Software Dened Radio TestbedFor MIMO Systems Proc. of the SDR 06 Tech. Conf. and Prod.Exposition , SDR Forum, 2006.

[9] L. Zheng and D. Tse, Diversity and multiplexing: a fundamentaltradeoff in multiple antenna channels, IEEE Tran. Inform. Th.,vol. 49, pp. 1073-96, May 2003.

[10] Adnan A. Khan, S.I. Shah, M. Naeem , A Particle Swarm Al-

gorithm for Symbols Detection in Wideband Spatial MultiplexingSystems, Proc. of the 9th annual conf. on Genetic and evolu-tionary computation , pp. 63-69, 2007.

[11] V. Tarokh, N. Seshadri, and A. R. Calderbank, Space-timecodes for high data rate wireless communication: Performancecriterion and code construction, IEEE Trans. Inform. Theory ,vol. 44, no. 2, pp. 744 765, Mar. 1998.

[12] G. J. Foschini, G. D. Golden, R. A. Valenzuela, and P. W. Wolni-ansky, Simplied processing for high spectral efficiency wirelesscommunication employing multi-element arrays, IEEE J. Select.Areas Commun. , vol. 17, pp. 1841-1852, Nov. 1999.

[13] Song Bo-wei, Guan Yun-feng, Zhan Wen-jun, An efficient train-ing sequences strategy for channel estimation in OFDM systemswith transmit diversity, Journal of Zhejiang University Science ,pp. 613-618, 2005.

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Performance Analysis of Video Transmission over MIMO SDR Systems