Techniques for Improving BER and SNR in MIMO Antenna for Optimum Performance

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61 International Journal for Modern Trends in Science and Technology

Volume: 2 | Issue: 04 | April 2016 | ISSN: 2455-3778IJMTST

Techniques for Improving BER and SNR inMIMO Antenna for Optimum Performance

J Prasanth Kumar 1 | R Sowjanya 2 | P Sameena 3 | P MadhaviSankar Gowd 4

1Assistant Professor, Department of ECE, Ramachandra College of Engineering, Eluru, India.2,3,4 B.Tech, Students, Department of ECE, Ramachandra College of Engineering, Eluru, India.

The use of multiple antennas for diversity, including MIMO (Multiple Input Multiple Output) is one of themost promising wireless technologies for broadband communication applications. This antenna system is avital study in today’s wireless communication system especially when the signal propagates through somecorrupted environments. In our paper new techniques of improving bit error ratio and signal to noise ratio are

discussed. Inter symbol interference is a major limitation of wireless communications. It degrades the performance significantly if the delay spread is comparable or higher than the symbol duration. To removeISI, equalization needs to be included at the receiver end. This paper discusses the merits of the MIMOsystem and the techniques used for improving BER performance and SNR. In MIMO wireless communication,an equalizer is used to recover a signal that suffers from Inter symbol Interference (ISI) and the BERcharacteristics is improved and a good SNR can be obtained. Different equalization techniques are discussedin this paper.

KEYWORDS: MIMO(Multiple input Multiple output), MRC(Maximum ratio combining equalizer), BER(Bit errorrate), SNR(Signal to noise ratio), ISI(Inter symbol interference), OFDM(orthogonal frequency divisionmultiplexing)

Copyright © 2016 International Journal for Modern Trends in Science and Technology All rights reserved.

I. INTRODUCTION

The use of multiple antenna technique hasgained overwhelming interest throughout the lastdecade. The idea of using multiple antennaconfigurations instead of a single one has proven tobe successful in enhancing data transfer rate,coverage, security and the overall performance ofradio networks. In recent years high data ratetechniques have gained considerable interests incommunication systems. Signal – to-noise ratio(SNR) is defined as the ratio of the desired signalpower to noise power. SNR indicates the reliabilityof link between the transmitter and receiver. Themost meaningful criterion for evaluation ofperformance of communication systems is the biterror rate (BER). The development ofnext-generation wireless communication systemsrequires broadband and multiband devicesformulti-functionality and faster data transfers,while maintaining good efficiency, low weight, lowcost, and easy manufacturing.In this context, bit

error rate and signal to noise ratio has become areal challenge for antenna designers. This paperdiscusses the merits of the MIMO system and thetechniques used for improving BER performanceand SNR. In MIMO wireless communication, anequalizer is used to recover a signal that suffersfrom Inter symbol Interference (ISI) and the BERcharacteristics is improved and a good SNR

obtained. Different equalization techniques arediscussed in this paper.1.1 NEED OF MIMO:

Multiple-input multiple-output, or MIMO, is aradio communications technology or RF technologythat is being mentioned and used in many newtechnologies these days.

Wi-Fi, LTE; Long Term Evolution, and manyother radio, wireless and RF technologies are usingthe new MIMO wireless technology to provideincreased link capacity and spectral efficiencycombined with improved link reliability using whatwere previously seen as interference paths.

ABSTRACT


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Techniques for Improving BER and SNR in MIMO Antenna for Optimum Performance IJMTST

Even now many there are many MIMO wirelessrouters on the market, and as this RF technology isbecoming more widespread, more MIMO routersand other items of wireless MIMO equipment willbe seen.MIMO technology has attracted attentionin wireless communications, since it offerssignificant increases data throughput and linkrange without additional bandwidth or transmitpower. It achieves this by higher spectral efficiency(more bits per second per hertz of bandwidth) andlink reliability or diversity (reduced fading).Because of these properties, MIMO is a currenttheme of international wirelessresearch.Point-to-point (single user) MIMOcommunication promises large gains for bothchannel capacity and reliability, essentially via theuse of space-time codes (diversity gain oriented)combined with stream multiplexed transmission(rate maximization oriented). Multiuser MIMO(MU-MIMO) information theory advocates for theuse of spatial sharing of the channel by the users.

II. OFDM The main limiting factors in high data rate

transmission are noise, inter-symbol interference(ISI) and multipath effects. The effects of ISI on thetransmission are negligible as long as the delayspread is significantly shorter than the duration ofone transmitted symbol. At higher data rates thisproblem becomes very obvious. In order to mitigatethe effects of ISI many techniques are suggestedlike equalization which can be used to suppress theechoes caused by the channel. Recently a new andmore robust technique is suggested known asOFDM.

OFDM stands for Orthogonal Frequency DivisionMultiplexing. The basic idea is to divide theavailable spectrum into several sub-carrierstransmitted in parallel to each other. This parallel

transmission support high data rates with minimalamount of ISI. For high efficiency the OFDMsub-carriers must be overlapping and orthogonal.

The orthogonality allows simultaneoustransmission of lot sub-carriers in a tight frequencyspace without interference.

The spectrum of each subcarrier has a “null” atthe centre frequency of each of the other sub-carrierin the system.

Fig.1 orthogonality of sub-carriers

OFDM is a wideband system with manynarrowband sub-carriers. The mathematical MIMOchannel model is based on a narrow bandnon-frequency selective channel. The latter is

supported by OFDM as well. Fading effects inwideband systems normally occur only atparticular frequencies and interfere with fewsub-carriers. The data is spread over all carriers, sothat only a small amount of bits get lost, and thesecan be repaired by forward error correction (FEC).

.Each subcarrier is modulated using varying levelsof QAM modulation, e.g. QPSK, QAM, 64QAM orpossibly higher orders depending on signal quality.Each OFDM symbol is therefore a linearcombination of the instantaneous signals on each

of the sub carriers in the channel. Because data istransmitted in parallel rather than serially, OFDMsymbols are generally MUCH longer than symbolson single carrier systems of equivalent data rate.

III. EQUALIZATION TECHNIQUESIn wireless communication, an equalizer in

general is implemented in the receiver side in orderto recover the signal very efficiently from the Intersymbol interference problems . This

implementation of equalizers improves the BitError Rate and hence provides good Signal to Noiseratio. These equalization techniques are also calledas combining techniques as the signals fromvarious paths are combined together. TheEqualization techniques are as follows:

A. Maximal Ratio Combining (MRC):In this MRC technique, each of the signals is

multiplied with a weight function which is

proportional to the signal amplitude. Hence thediversity branch which has strong signals isamplified further and the branches with weaksignals are further attenuated. In this diversity


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combining technique, signals from variouschannels are added together and the gain of eachchannel is proportional to the RMS value of thesignal and is inversely proportional to the meansquare noise level of that particular channel. Thisis also called as pre-detection combining orratio-squared technique and is well suited forindependent AWGN channels.

IV. MIMO

A. History of MIMOArogyaswamiPaulraj and Thomas Kailath

proposed the concept of Spatial Multiplexing usingMIMO in 1993. Their US Patent No. 5,345,599

issued in 1994 on Spatial Multiplexing emphasizedapplications to wireless broadcast. In 1996, GregRaleigh and Gerard J. Foschinirefine newapproaches to MIMO technology, which considersconfigurations where multiple transmit antennasare co-located at one transmitter to improve thelinkthroughput effectively. Bell Labs was the firstto demonstrate a laboratory prototype of spatialmultiplexing(SM) in 1998, where spatialmultiplexing is a principal technology to improvethe performance of MIMO communication systems,

Block Diagram of MIMO

Additive White Gaussian Noise (AWGN) As a last step, we include additive noise in our

input/output model. We make the standardassumption that w(t) is zero-mean additive whiteGaussian noise (AWGN) with power . Theassumption of AWGN essentially means that we are

assuming that the primary source of the noise is atthe receiver or is radiation impinging on thereceiver that is independent of the paths overwhich the signal is being received. This is normallya very good assumption for most communicationsituations.

The system was initially tested under AWGNchannel conditions using BPSK modulationscheme. Next, tests were conducted under Rayleighfading channel conditions. Under these conditions,the system performance degraded enormouslymaking it imperative to come up with a solution.

The Rayleigh fading channel and the solutions to

minimize the effect of channel will be discussed indetail in the next chapter.

B. Alamouti STBC:Space-time block codes are used for MIMO

systems to enable the transmission of multiplecopies of a data stream across a number ofantennas and to exploit the various receivedversions of the data to improve the reliability ofdata-transfer. Space-time coding combines all thecopies of the received signal in an optimal way toextract as much information from each of them aspossible.

Space time block coding uses both spatial andtemporal diversity and in this way enablessignificant gains to be made.

Space-time coding involves the transmission ofmultiple copies of the data. This helps tocompensate for the channel problems such as f

i/p data A/Dconverter BPSK

Modulation S/Pconverter IFFT

P/SConverter

Alamouti STBC

o/p data D/A

converter Demodulation P/Sconverter FFT

S/PCONVERTE

Channel


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Techniques for Improving BER and SNR in MIMO Antenna for Optimum Performance IJMTST

When using space-time block coding, the datastream is encoded in blocks prior to transmission.

These data blocks are then distributed among themultiple antennas (which are spaced apart todecorrelate the transmission paths) and the data isalso spaced across time.

A space time block code is usually represented bya matrix. Each row represents a time slot and eachcolumn represents one antenna's transmissionsover time. ading and thermal noise. Although thereis redundancy in the data some copies may arriveless corrupted at the receiver.

Within this matrix, Sij is the modulated symbolto be transmitted in time slot i from antenna j.

There are to be Ttime slots and nT transmitantennas as well as nR receive antennas. Thisblock is usually considered to be of 'length' T.

Since the transmission is done over two periodsof time, the decoding will also be done over twoperiods of time. At the receiver, the receivedvector Y can be represented by the followingequation:

This is for the first time period. For the second timeperiod, the equation is as follows:

where represents the received OFDM symbolat the first time period, for antennas 1 and 2,

respectively, and where represents thereceived OFDM symbol at the second time periodfor antennas 1 and 2, respectively. Both equationscan easily be combined and arranged to producethe following result:

The next step is to find a way to isolate thetransmitted symbols, x 1 and x 2 . One way toreduce the number of unknowns is by using achannel estimator to estimate the channelcoefficients. In Nutaq’s OFDM reference design,channel estimation OFDM symbols are sent witheach transmitted packet to enable estimating thosechannel coefficients at the receiver. Given thefollowing matrix:

we can isolate x 1 and x 2 by simply multiplying thematrix Y by the inverse of H . However, since thismatrix is not square, we need to use theMoore-Penrose pseudo-inverse H + to solve ourequations:

Using this inverse matrix expression, the noisyestimated transmitted symbols can be found usingthe following expression:

The last step would be to make a final decision onthe transmitted symbols. In Nutaq’s OFDM


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reference design, the decision is made based on theminimum squared Euclidian distance criterion. Inthe next figure, we can see that the addition ofdiversity to the system brings a significantperformance gain in terms of BER in simulation.

C. Advantages

Higher data rates in wireless access Improves reliability and coverage. capacity scales linearly with number of

antennas Larger spectral efficiency Larger number of users Better interference suppression.

V. S IMULATION R ESULTS

It isobserved that the Bit Error Rate of alamoutiequalizer based receiver is less as compared tomaximum ratio combining . The BER for

Theoretical MRC is 0.0581, Simulated ALMOUTIis 0.0402 . This shows that alamouti has lowerBER as compared to MRC in every case

VI. C ONCLUSION

To conclude this paper provides the completeknowledge of the key issues in the field of mobile

communication. When data is transmitted at highbit rates over mobile radio channels, the channelimpulse response can extend over many symbolperiods which leads to intersymbol Interference.

The ultimate goal is to provide universal personaland multimedia communication without regard tomobility or location with a high data rates. Toachieve such an objective a strong equalizationtechnique is taken. The receiver scheme is basedon Almouti STBC. Bit Error Rate performance forMIMO-STBC in correlated Rayleigh flat fading

channel is better than Maximum ratio combiningEqualizer. The performance iscompared with thetwo types of equalizer based receiver namely MRC,STBCAs the number of transmitters is less and

more increasing in number and BER decreases fora particular value of Eb/No value. BERperformance of STBC Equalizer is superior thenMRC Equalizer. The BER values from fig.1 are0.0581 for MRC and 0.0402 for Almouti STBC. It isinferred that theSTBC equalizer is the best of theother equalizextend upto orthogonalMIMO-STBC,Integer MIMO-STBC.

R EFERENCES [1] VaishaliW.Sonone,Dr.N.B.Chopade” Techniques for

improving BER and SNR in MIMO antenna foroptimum performance”

[2] Fundamentals of wireless communications-David,Pramodviswanath

[3] Wireless communication & network – 2 nd edition by Theodore S. Rappaport

[4] Pramodini D V and A G Ananth,” study ofperformance of linear & non-linear narrow bandreceiver for 2x2 MIMO systems with STBCmultiplexing and almouti coding”

[5] Digital communication , 3 rd edition by john R. Barry,Edward A

[6] H. Zhang, H. Dai, Q. Zhou, and B. L. Hughes, 2006on the “Diversity - multiplexing tradeoff for orderedSIC receivers over MIMO channels,” IEEEInternational Conference on Communications (ICC),Istanbul, Turkey.

[7] K. Cho and D. Yoon, 2002,on “The general BER expression of one and two-dimensionalamplitudemodulations," IEEE Transactions on

Communications, vol. 50.[8] N.Satish Kumar, Dr.K.R.Shankar Kumar,

“Performance Analysis of M*N Equalizer basedMinimum Mean SquareError (MMSE) Receiver forMIMO Wireless Channel”, International Journal ofComputer Applications (0975 – 8887) Volume 16 – No.7, February 2011

0 5 10 15 20 2510

-5

10-4

10-3

10-2

10-1

Eb/No, dB

B i t E r r o r

R a

t e

BER for BPSK modulation with 2Tx, 2Rx Alamouti STBC (Rayleigh channel)

theory (nTx=1,nRx=1)

theory (nTx=1,nRx=2, MRC)theory (nTx=2, nRx=1, Alamouti)sim (nTx=2, nRx=2, Alamouti)

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Techniques for Improving BER and SNR in MIMO Antenna for Optimum Performance