Performance comparison of power delay profile Estimation for MIMO OFDM

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1 IOSR Journal of Engineering (IOSRJEN) ISSN (e): , ISSN (): Vol. 04, Issue 06 (June. 2014), V5 PP Performance comarison of ower delay rofile Estimation for MIMO OFDM Manisha K. Ahirrao, Beena R. Ballal,Shraddha Panbude Electronics and Telecommunication Deartment Vidyalankar Institute Of Technology Wadala, Dadar (w), Mumbai, India Abstract: - A multile-inut multile-outut (MIMO) communication system combined with the orthogonal frequency division multilexing (OFDM) modulation technique can achieve reliable high data rate transmission over broadband wireless channels. In linear minimum mean square error channel (MSE) estimation for multicarrier system, it is necessary to know channel correlation function. ence estimation of noise variance using null carrier and ower delay rofile can be used to aroximation which can be described in two arameters like; mean delay and root mean square (RMS) delay sread. The aroximate ower delay rofile is used to generate LMMSE coefficient for subcarrier channel estimation. In this aer the estimation of channel at ilot frequencies with Minimum Mean Square (MMSE) estimation algorithms is carried out through Mat lab simulation. The erformance of MIMO OFDM is evaluated on the basis of Mean Square Error (MSE) level.simulation results shows the erformance of MSE for estimated PDP and Constant PDP for varying number of samles and for SNR. The erformance of LMMSE channel estimation using the roosed PDP estimate aroaches to that of Wiener filtering while in the other case, the estimated PDP gives less SNR than constant PDP. Keywords: - Channel estimation, Power delay rofile, Mean Square Error, MIMO, OFDM. I. INTRODUCTION Orthogonal frequency division multilexing (OFDM) is a multi-carrier transmission technology in wireless environment, and can also be seen as a multi-carrier digital modulation or multi-carrier digital multilexing technology [9]. The basic idea of OFDM is to divide available bandwidth into N narrow subchannel at equidistant frequencies.[8]. A large number of orthogonal sub-carriers are used to transmit information. OFDM system has high utilization of frequency sectrum and satisfactory caability of reducing multi-ath inference. So, OFDM has been considered as one of the core technologies of 4th generation (4G) wireless communication system in the future Orthogonal Frequency Division Multilexing. [1] A single stream of data is slit into arallel streams each of which is coded and modulated on to a subcarrier, a term commonly used in OFDM systems.ofdm is a multicarrier system uses discrete Fourier Transform/Fast Fourier Transform (DFT/FFT), sin(x)/x sectra for subcarriers.available bandwidth is divided into very many narrow bands, Data is transmitted in arallel on these bands. In OFDM each subcarrier has a different frequency, Frequencies chosen so that an integral number of cycles in a symbol eriod, Signals are mathematically orthogonal and Data is carried by varying the hase or amlitude of each subcarrier. Orthogonal frequency division multilexing (OFDM) transmission scheme is another tye of a multichannel system, which emloys multile subcarriers, it does not use individual band limited filters and oscillators for each sub channel and furthermore, the sectra of subcarriers are overlaed for bandwidth efficiency, The multile orthogonal subcarrier signals, which are overlaed in sectrum, can be roduced by generalizing the single-carrier In ractice, discrete Fourier transform (DFT) and inverse DFT (IDFT) rocesses are useful for imlementing these orthogonal signals. DFT and IDFT can be imlemented efficiently by using fast Fourier transform (FFT) and inverse fast Fourier transform (IFFT), resectively. Single carrier system Signal reresenting each bit uses the entire available sectrum. In multicarrier system available sectrum divided into many narrow bands data is divided into arallel data streams each transmitted on a searate band II. PILOT BASED OFDM MODEL 48 P a g e

2 I/P data Maing S/P Pilot insertion IFFT Add cyclic refix P/S Chanel AWGN O/P data De maing P/S FFT Pilot extraction Remove cyclic refix S/P Channel estimation Fig 1: Pilot based OFDM system model. Channel estimation lays a very imortant role in OFDM system.many related algorithms have been resented these years, which can be generally searated into two methods, ilot-based channel estimation and blind channel estimation. In aer [4] the resent channel estimation methods generally can be divided into two kinds: One is based on blind channel estimation which does not use ilots. Other is based on the ilots Blind channel estimation ilot-based channel estimation is a ractical and an effective method[1]. Blind channel estimation, this uses statistical information of the received signals.blind channel estimation methods avoid the use of ilots and have higher sectral efficiency. owever, they often suffer from high comutation comlexity and low convergence seed since they often need a large amount of receiving data to obtain some statistical information such as cyclo stationarity induced by the cyclic refix. Therefore, blind channel estimation methods are not suitable for alications with fast varying fading channels. And most ractical communication systems such as World Interoerability for Microwave Access (WIMAX) system adot ilot assisted channel estimation. Pilot channel estimation Pilot-based channel estimation estimates the channel information by obtaining the imulse resonse from all sub carriers by ilot. [1] Pilot based channel estimation is based on the transmission of symbols that are known to receiver so called ilot symbols[4]. The ilot symbols are inserted into data stream and transmitted over mobile channel, at receiver ilot symbols are analyzed in order to obtain channel estimate which is utilized for equalization.as the characteristic of mobile channel is varying with time and frequency in order to obtain estimate which rovides information about time and frequency domain channel variation, ilot symbol need to be transmitted eriodically in time and sread over whole bandwidth which is rovided for data transmission. For the ilotaided channel estimation methods, there are two classical ilot atterns, which are the block-tye attern and the comb-tye attern. The block-tye refers to that the ilots are inserted into all the subcarriers of one OFDM symbol with a certain eriod i.e. symbols are transmitted eriodically, and all subcarriers are used as ilots.[2] The block-tye can be adoted in slow fading channel, that is, the channel is stationary within a certain eriod of OFDM symbols. The comb-tye refers to that the ilots are inserted at some secific subcarriers in each OFDM symbol. The comb-tye is referable in fast varying fading channels [7], that is, the channel varies over two adjacent OFDM symbols but remains stationary within one OFDM symbol. The comb-tye ilot arrangementbased channel estimation has been shown as more alicable since it can track fast varying fading channels, comared with the block-tye one. 49 P a g e

3 III. SYSTEM MODEL The system under consideration is a MIMO-OFDM system with P transmits and Q receive antennas, and K total subcarriers [3].Suose that the MIMO-OFDM system with the secified antennas transmits K d subcarriers at the central sectrum assigned for data and ilots, in order to control interferences with other systems. Let [k, n ] be the ilot subcarrier for the th transmit antenna at the n th OFDM symbol, which is a QPSK modulated signal Fig : 2 Pilot symbol arrangement in a block of OFDM system [3] We assume that the ilot subcarriers are distributed over a time and frequency grid as in Fig. 2, to reserve the orthogonality of ilots among different transmits antennas. At the n th OFDM symbol, the number of ilot subcarriers is defined as K=. The ilot inserted OFDM symbol is transmitted over the wireless channel after erforming an inverse fast Fourier transform (IFFT) and adding a CP. It is assumed that the length of CP, L g, is longer than the channel maximum delay, L ch, making the channel matrix circulate (L ch L g). At the receiver, after erfect synchronization, the removal of CP, and FFT oeration, the received ilot symbol for the q th receive antenna can be reresented as, y q [n ] = diag X F h,q + n q (1)[3] where h,q =[h,q [n,0],h,q [n, 1],.,h,q [n L ch ],0,.,0] T is an L g 1 CIR vector at the th transmit antenna and q th receive antenna. [3] IV. PROPOSED METOD FOR PDP ESTIMATION: The roosed method is as follows: From (1), the CIR at the (, q)th antenna ort can be estimated aroximately using the regularized least squares (RLS) channel estimation with a fixed length of L g as h, R,,q = (F F + I Lg ) 1 F diag(x ) y q [n ] W RLS, y q n, (2)[3] Where ε =0.001 is a small regularization arameter, and I Lg is the Lg Lg identity matrix. To derive the PDP from the estimated CIR in (2), the ensemble average of h, R, h is given by E{h R,,q h R,,q }=WR hh W + σ 2 n W RLS, W RLS, (3)[3] Where R hh =E{h,q h,q }and W=(F F + I L,g ) 1 F F R hh, reresent the PDP of multiath channel within the length of L g, Unfortunately, R hh is distorted by W, which is an ill-conditioned matrix due to the resence of F F. Thus, instead of calculating W 1, we investigate the method for eliminating the sectral leakage of W. The covariance matrix of the estimated CIR is defined as R hh = WR hhw which can be exressed as L g 1 R hh = W diag( l=0 l u l )W (4)[3] Where u l is a unit vector with the l th entry being one and otherwise zeros. Let P h and t l be the L g 1 vectors defined as P h = (R h ) and t l=d g ( Wdiag(u l)w ), resectively, where Dg(A) is the column vector containing all the diagonal elements of A. Then, the relation in (4) is simlified as P h = 0 t t Lg 1 t Lg 1 TP h (5)[3] where T=[t0, t1,..., tlg 1] is defined as a distortion matrix by W.. In addition, the distortion matrix is a wellconditioned matrix. ence, the distortion of W can be eliminated as h =T 1 P h = E {g,q [n ]}- σ 2 n w (6)[3] where g,q [n ]=T 1Dg (hr,,q h R,,q ) is defined as the received samle vector for estimating PDP at the (, q)th antenna ort on the nth OFDM symbol, and w= T 1(WRLS,W RLS, ). 50 P a g e

4 PDP Estimation in MIMO-OFDM Systems The received samle vector in (6) can be exressed as g,q [n ]= Dg(h,q h,q ) + n,q + e,q (7)[3] where n,q = T 1 D g(w RLS, n qn q W RLS, ) and e,q = 2 R e { T 1 (Wh,q n q W RLS, )}. ere, R e {a} denotes the real art of a. We assume that n,q is an effective noise by AWGN. Then, the samle average of g,q [n ] is given by 1 T Q < g,q [n ]> N g N n =1 =1 q=1,q [n ] (8)[3] = Dg h,q h,q + n,q N + e,q N Where N T PQ reresents the total number of samles for PDP estimation. T is the number of ilot symbols at the k th subcarrier in a time slot. When is sufficiently large, the PDP can be erfectly estimated, since Dg h,q h,q N P h, n,q N σ 2 n w, and e,q N 0. owever, it is difficult for a receiver of ractical MIMO-OFDM systems to obtain such a large number of samles. With an insufficient number of samles, the PDP can be aroximated as P h Dg h,q h,q N To imrove the accuracy of PDP estimation with insufficient we mitigate the effective noise as follows < g,q [n ]> N σ 2 n w = Dg h,q h,q + Z N, (9)[3] where Z N e,q N + n,q N σ 2 n w is defined as a residual noise vector, in which each entry has a zeromean. Then, the error of PDP estimation with N samles can be calculated as e N = ( Dg h,q h,q N P h ) + Z N (10)[3] Since [P h ]i 0 for all i, the PDP can initially be estimated as 1 T P Q P init = S N n =1 =1 q=1,q [N ] (11)[3] where S,q [N ] is the samle vector of roosed PDP estimator with the l th entry l S,q [N ] = g l,q N σ 2 n w ~1 l if g,q N > σ 2 n w ~1 (12)[3] 0 otherwise l where g,q N = [g,q [N ] ]l and w ~1 = [w] To mitigate the detrimental effect of residual noise Z N, the roosed scheme estimates the average of residual noise at the zero-tas of P h. At the l th entry of, init the zerota can be detected as l t z = 1 if init < βth (13)[3] 0 otherwise 1 Lg 1 l where βth = Lg L g =1 init is defined as a threshold value for the zero-ta detection. Then, the average of residual noise at the zero-tas can be estimated as nr avg = 1 Lg 1 l l Nz l=0 init t z (14)[3] Lg 1 where N z = l=0 t l tl reresents the total number of detected zero-tas. With the mitigation of residual noise, the lth ta of the PDP estimate, P h, can be exressed as l l l h = init nr avg if init > nr avg (15)[3] 0 therwise Then, the estimated PDP in (15) can be used to obtain the frequency-domain channel correlation in the LMMSE channel estimator. V. SIMULATION RESULTS We consider a MIMO-OFDM system. The system bandwidth is 5 Mz with 301 subcarriers for transmitting data information and ilots at 2-Gz carrier frequency. Number of ilots are 12. One frame consist of 14 OFDM symbols. The MIMO-OFDM system utilizes two transmit and one receive antennas (P=2, Q=1). The length of CP is 40 (Lg=40). One imortant arameter of the channel is the ower delay rofile which reresents the average ower (also called multiath intensity rofile) associated with a given multiath delay. [6] For all simulations, the channel estimator is based on results.in fig 3.1 and in fig 3.2 we calculated effect of Mean square Error over variable number of samles for estimated PDP and for constant PDP resectively. The erformance of roosed scheme is better than the constant ower delay rofile. In fig 3.3 we lotted MSE for given SNR range. The erformance of two PDPs constant and estimated is observed. 51 P a g e

5 Fig :3.1 Performance of MSE with number of samles 500 Fig :3.2 Performance of MSE with number of samles1000 Fig :3.3 Performance of MSE with SNR VI. CONCLUSION In this aer, we roose to evaluate the erformance of LMMSE estimation techniques for the systems under the effect of the number of samles for estimated PDP and for constant PDP. The transmitted signals are quadrature hase-shift keying (QPSK) modulated. The cyclic refix inserted at the beginning of each OFDM symbol is usually equal to or longer than the channel length in order to suress ICI and ISI [5]. Simulation results show that in the case where the number of samles is increased the erformance of constant PDP remains constant than the estimated PDP. MSE of the roosed scheme imroves the MSE erformance with an increase in the number of samles for PDP estimation. Simulation results show that the erformance of LMMSE channel estimation using the roosed PDP estimate aroaches that of Wiener filtering. [3]In the other case, the estimated PDP gives less SNR than constant PDP. 52 P a g e

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