Equalization Techniques for MIMO Systems in Wireless Communication: A Review

Transcription

1 Equalization Techniques fo MIMO Systems in Wieless Communication: A Review Rashmi Kashyap, Jaspal Bagga Abstact-The main objective of this pape is to povide a eview of equalization techniques in multiple input, multiple output (MIMO) communication system In wieless communication inte symbol intefeence is majo obstacle which geatly affect the data quality The chief goal of equalization techniques is to ebuild the actual signal with the help of filte o any othe methods and emove the effect of ISI so that the eliability of data tansmission is maintained Diffeent kind of equalization techniques poposed ealie has been eviewed hee Keywods- MIMO, intefeence, MMSE, Adaptive, filte, ISI I INTRODUCTION Main aim of wieless technology is to offe bette quality voice, data, pictues, fax, and video Teestial digital TV boadcasting, LTE, 3GPP ae some of the latest advancement in the field of wieless communication These above mentioned advancement ae possible with the help of OFDM and CDMA technology In a last few yeas OFDM has emeged as a most popula technique of tansmitting data/signal ove wieless media In OFDM, signal ae tansmitted in sub-channel of diffeent fequency in paallel fashion The fequency of sub-channel ae so selected that these fequency ae othogonal to each othe and theefoe do not intefee with each othe This phenomenon makes it possible to tansmit the data in ovelapping fequency and hence educed the bandwidth equiement consideably [1] Since in wieless communication data ae sent in adio space, the channel exhibits multipath fading phenomenon which poduces inte-symbol intefeence (ISI) in the signal eceived at the eceive side Inte-symbol intefeence (ISI) is undesiable and it inceases the bit eo ate Wheneve the modulation bandwidth exceeds the adio channel coheence bandwidth, ISI poduced In ode to educe o eliminate inte-symbol intefeence (ISI), diffeent types of equalization techniques ae used which compensate the ISI using impulse esponse of channel [2] Equalize wok by keeping bit eo ate as low as possible and SNR as high as possible[3] These equalization techniques poved to be vey impotant fo designing wieless system with high data ate tansmission capacity Most of the wieless eceive ae equipped with the equalize which gives good esult unde expected In wieless communication, the eceives ae geneally equipped with the equalize which gives aveage pefomance The quality of wieless communication depends upon the thee paametes ie ate, ange and eliability of tansmission These paamete ae elated with each othe Simultaneously impovement in all the thee paametes can be accomplished with the help of new technique called MIMO assisted OFDM Manuscipt Received on June 2014 Rashmi Kashyap, VME Student,ECE, Depatment SSCET, Bhilai, Chhattisgah, India Jaspal Bagga, HOD, IT Depatment, SSCET, Bhilai, Chhattisgah, India Subcaie & Powe Allocation MIMO-OFDM Tansmitte Base Station Resouce allocation Infomation Feedback Infomation Figue 11 MIMO Communication Systems Because of the capability of offeing inceased link ange and data thoughput without the equiement of exta bandwidth and tansmit powe, MIMO has become a main theme fo the eseaches in the field of communication[4] MIMO gives highe link eliability and spectal efficiency The combination of MIMO Communication system and Othogonal fequency division multiplexing (OFDM) modulation technique is capable of achieving high data ate tansmission eliably in boad band wieless channel[5] In any wieless system, in ode to exploit the popety of fequency selectivity of wieless channel, thee is s a need to design an efficient, pactical and low complexity equalization technique fo designing high data ate wieless system[6]diffeent types of adaptive equalize has been poposed in past which can be used to fight inte-symbol intefeence while maintaining the divesity[7] In this pape an effot has been made to go though the backgound and past wok done in the field of equalization pocess II BACKGROUND Subcaie & Powe Allocation MIMO-OFDM Receive Base Station The wieless communication concept came into existence when In 1928 Nyquist laid down the theoy of telegaph tansmission [8] which also became the foundation of pulse tansmission Widow and Hoff [9] poposed least mean squae based adaptive filte algoithm in 1960 which was the foundation of adaptive equalization method Most of the eseach on adaptive equalization in ealy 60 s wee concentated on the basic fundamental and stuctue of zeo focing tansvesal equalize[10] Meanwhile in paallel some wok has been done in the same peiod fo developing the theoy and stuctue of linea tansmit and eceive filtes[11][12] which can minimize the mean squae eo fo additive Gaussian noise channels[13] In ealy 70 s Tansvesal and decision-feedback equalizing pocess with fowad filte tap spacing which is less than the inteval of symbol has been poposed[14] These equalizes wee late on used in commecial telephone line modem[15][16] and in militay adio system[17] Until the ealy pat of 70 s most of the wok elated with equalization wee centeed to the 260 & Sciences Publication Pvt Ltd

2 Equalization Techniques fo MIMO Systems in Wieless Communication: A Review stuctue and steady state analysis Afte that most of the wok wee centeed on developing some computationally efficient algoithm of equalizationrls algoithm was one of the computationally efficient method of equalization which became the intense eseach subject[18][19][20][21][22] in the field of equalization at that time which eventually led to the tansvesal [23][24][25] and lattice[26][27][28][29][30][31] algoithm Late these method wee applied to adaptive equalize fo HF modem[32] An equalization opeation is geneally pefomed at baseband o IF in a eceive section A lot of wok has been poposed in the past fo combating and educing inte symbol intefeence in MIMO communication system Equalization is one of them Equalization techniques is divided into thee pat The fist one fequency domain equalization techniques which includes pe-tone equalization and it is used whee channel ode is lage than the cyclic pefix The second one is time domain equalization techniques which includes channel shotening and time domain statistics-based techniques The thid one is tubo equalization which is iteative based equalization technique Some of the notewothy algoithm of equalization is pesented in the next sections of this pape III ADAPTIVE EQUALIZER [33] Randomness and time vaying popety of fading channel makes it necessay to design a equalize which is capable of tacking the time vaying popety of mobile channel and hence known as adaptive equalizes Block diagam of adaptive equalization pocess[33] is shown in Figue 12 Modulato Detecto Matched Filte Tansmitte IF Stage Radio Channel RF Receive y(t)=s(t) f * (t) h eq (t)+ n b (t) h eq (t) whee h eq (t) epesent the equalize impulse esponse If the combined esponse of tansmitte, communication channel and IF/RF section of eceive is epesented by g(t) then the above equation can also be witten as y(t)=h(t) g(t)+ n b (t) h eq (t) Impulse esponse of the equalizes tansvesal filte can be expessed as h eq t = c n δ(t nt) n Hee Cn epesent the complex coefficients of filte at the equalize Since the desied output signal of the equalize is nothing but the oiginal signal s(t)theefoe in ode to make y(t)=s(t) in equation y(t)=s(t) f * (t) h eq (t)+ n b (t) h eq (t) the value of g(t) must be g(t)= f * (t) h eq (t)=δ(t) the main aim of equalize is to fulfill the following equation H eq (f) F * (-f)=1 Whee Heq (f) epesents fouie tansfom of h eq (t) and F (f) epesent Fouie tansfoms of f(t) IV ZERO FORCING EQUALIZER [34] In this method [34], the ISI component at the output of equalize is foced to zeo by using appopiate linea time invaiant filte having suitable tansfe function If tansmitted symbol is epesented by and, h 11 epesent the channel fom fist tansmitte to fist eceive,h 12 epesent the channel fom second tansmitte to fist eceive, h 21 epesent the channel fom fist tansmitte to second eceive and h 22 epesent the channel fom second tansmitte to second eceive and n 1, epesent noise on fist and second eceive then the eceived symbol on fist eceive is given by Equalize Decision Make y 1 = h 11 + h 12 = h 11 h 12 And the eceived symbol on second eceive is given by Figue 12 Adaptive Equalize If s(t) is the oiginal signal and f(t) epesent the collective complex base-band impulse esponse of channel, Tansmitte and IF/RF section of eceive, then the signal eceived at the input of equalize is given by x (t)=s(t) f * (t)+ n b (t) Hee f * (t) is complex conjugate of f(t) and n b (t) is noise of baseband while symbol Repesent the convolution opeation The output of equalize can be epesented by y(t)=x(t) h eq (t) o it can be witten as an = h 21 + h 22 + = + These two above equation can also be witten as y 1 = h 11 h 12 It is clea fom this equation that if h 11, h 12, h 21, h 22 and y 1, is known then it is easie fo the eceive to compute the and Now if we ewite the above equation then y = Hx + n Fom hee it is clea that in ode to find x fom above equation, we need to find out the matix which is invese of matix H If W epesent the invese of H then it must satisfy the popety 261 & Sciences Publication Pvt Ltd

3 WH = I Whee I is the identity matix The matix W which satisfy the above mentioned popety is known as the zeo focing linea detecto and is computed by following equation W = H H H 1 H H In this equation the matix H H H is given by H H H = h 11 h 12 h 21 h 22 h 11 h 12 Fom this matix it is clea that the off diagonal tem ae non zeo and hence zeo focing equalize cancel out the intefeence signal It is easonably simple and easy to implement but its main dawback is that it tends to amplify the noise and hence gives noisy output V MMSE EQUALIZER (MINIMUM MEAN SQUARED ERROR) This type of equalize uses the squaed eo as pefomance measuement[35] The eceive filte is designed to fulfill the minimum mean squae eo citeion Main objective of this method is to minimize the eo between taget signal and output obtained by filte The computation fo this method is as follows- If tansmitted symbol is epesented by and, h 11 epesent the channel fom fist tansmitte to fist eceive,h 12 epesent the channel fom second tansmitte to fist eceive, h 21 epesent the channel fom fist tansmitte to second eceive and h 22 epesent the channel fom second tansmitte to second eceive and n 1, epesent noise on fist and second eceive then the eceived symbol on fist eceive is given by y 1 = h 11 + h 12 = h 11 h 12 And the eceived symbol on second eceive is given by = h 21 + h 22 + = + These two above equation can also be witten as y 1 = h 11 h 12 It is clea fom this equation that if h 11, h 12, h 21, h 22 and y 1, is known then it is easie fo the eceive to compute the and Now if we ewite the above equation then y = Hx + n Now, MMSE algoithm computes the coefficient of matix W which minimize the condition E w y x w y x H Solving above equation gives W = H H H + N o I 1 H H Fom the above equation it is clea that this equation is diffeent fom the equation of zeo focing equalize by the tem N o I If we put N o I=0 in this equation then MMSE equalize becomes zeo focing equalize VI ZERO FORCING WITH SUCCESSIVE INTERFERENCE CANCELLATION (ZF-SIC) In this method, fist of all the zeo focing equalize find the estimated symbol and then one of the estimated symbol is subtacted fom eceived symbol to compute the equalized symbol by applying maximum ation combining(mrc)[36] If and ae the estimated tansmitted symbol then = H H H 1 H H y 1 By subtacting one of the estimated symbol (say ) fom the eceived signal y 1 and O 1 = y 1 h 12 = h 12 2 h 22 h = h 11 x 2 h 1 21 = h + n By applying maximum atio combining(mrc), the equalized symbol is given by = hh h h h VII SUCCESSIVE INTERFERENCE CANCELLATION USING OPTIMAL ORDERING [37] In the pevious successive intefeence cancellation method, estimation symbol is chosen abitaily and then its effect is subtacted fom eceived symbol y1 and y2 A bette esult can be obtained if we choose estimated symbol whose influence is moe than othe symbol Fo this fist of all the powe of both the symbol is computed at the eceives and then the symbol having highe powe is chosen fo subtaction pocess The powe of tansmitted symbol is given by P x1 = h h 21 2 Similaly the powe of tansmitted symbol x2 is given by P x2 = h h 22 2 If Px1>Px2, the x1 is subtacted fom y1 and y2 and eestimate the ie 1 2 = y 1 h 11 h 12 = h 12 h = h 12 x 2 h 2 22 = h + n By applying maximum atio combining (MRC), the equalized symbol is given by = hh h h h Similaly if If P x2 >P x1, the is subtacted fom y1 and y2 and e-estimate the These ae some of the most 262 & Sciences Publication Pvt Ltd

4 Equalization Techniques fo MIMO Systems in Wieless Communication: A Review impotant equalization algoithm which ae widely used in MIMO communication system It is vey impotant to see how these methods pefom unde diffeent noise and channel conditions Sahu [38] in his pape discussed the pefomance of MIMO system fo Rayleigh channel In his pape he discussed the MIMO system, its BER pefomance SNR and eo pefomance fo diffeent equalizes he also mentioned the coelation between elements of antenna Maximal Ratio Combining (MRC) technique was used fo analyzing eceive divesity He also done the compaison fo equal gain combining and selection combining The equalization is done by maximum mean squaed eo (MMSE),ML(Maximum likelihood method) and ZF(zeo focing) method Fo 10 Db SNR, BER fo ZF equalize is obtained as 10-4 while the BER obtained fo MMSE equalize is between 10-4 to 10-5 Best pefomance is obtained using ML equalize which gives the BER exactly 10-5 Kanchan [39] in his pape pesented a detailed analysis of OFDM communication system fo diffeent fading envionment In his appoach she applied zeo focing(zf) and Minimum mean squae Eo (MMSE) fo equalization pupose She also applied multicaie modulation fo getting bette eduction in inte-symbol intefeence (ISI) along with high eliability and bette pefomance ove fading channel conditions In he pape she also pointed out that the pefomance of MMSE equalize is bette because it is not able to eliminate the ISI totally but also able to minimize the total noise powe She also showed that if numbe of eceiving antenna is inceased with espect to tansmitting antenna then BER deceases in case of MMSE equalize V Jagan [40] in his pape poposed that limitation of dispesive fading can be eliminated and co channel intefeence can be educed by combining OFDM and CDMA In ode to study the equalization techniques in educing the ISI, he also applied diffeent equalization techniques fo flat fading Rayleigh multipath channel using BPSK modulation He applied zeo focing (ZF), MMSE, Zeo focing equalization with successive intefeence cancellation (ZF-SIC), ZF-SIC fo optimal odeing and MMSE-SIC ZF-SIC shows an impovement of 22 db ove ZF in case of BER of 10-3 while ZF-SIC fo optimal odeing gives 20dB impovement ove ZF-SIC in case of BER of 10-3 MMSE-SIC with optimal odeing gives an impovement of 50 db ove MMSE-SIC fo BER of 10-3 So fom this figue it is clea that MMSE-SIC with optimal odeing is best in ISI cancellation VIII CONCLUSION Multiple input Multiple output tansmission techniques is a new concept of poviding the high speed data tansmission in wieless communication system Inte symbol intefeence is one of the impediments fo achieving eliable high speed data tansmission ove wieless media In ode to nullify the effect of Inte symbol intefeence (ISI) at the eceive side, equalization techniques o equalize is used The main function of the equalize is to econstuct the actual signal with the help of channel esponse and estimated signal o 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