Improvement of Downlink LTE System Performances Using Nonlinear Equalization Methods Based On Wiener Hammerstein

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1 International Journal of Current rends in Engineering & Research (IJCER) e-issn Volume Issue 6, June 06 pp Scientific Journal Impact Factor : Improvement of Downlink LE System Performances Using Nonlinear Equalization Methods Based On Wiener Hammerstein Sunil R.Gosavi, M.G.Wani, Department Of Electronics And elecommunication Engineering Dr. D. Y. Patil School Of Engineering And echnology. Charholi, Bk, Via Lohegaon, Pune-405. Abstract In this work, we propose a nonlinear Wiener-Hammerstein channel estimation algorithm for downlink LE system. For estimation purpose, the LE standard provides known data as pilots symbols and exploits them through a coherent detection to improve system performance. hese drivers are placed in a hybrid way to cover up both time and frequency domain. Our aim is to adapt the Wiener-Hammerstein equalizer to LE standard in order to compensate interference and nonlinear effects introduced by power amplifier and multipath channel. We present the comportment of Wiener-Hammerstein equalizer in Downlink LE System in term of BLER, EVM (%) and hroughput versus SNR. hen we compare our results with basic LMS equalizercurves. Moreover, it is shown that the Wiener-Hammerstein model equalizer can significantly reduce interferences and nonlinear effects and consequently improve the performances of LE Downlink System. Keywords LE; LMS; Wiener-Hammerstein I. INRODUCION he long-term evolution (LE), marketed as 4G system, is a standard for wireless communication of high speed data transmission with mobility, it s developed by the 3rd Generation Partnership Project(3GPP)[][] and based on the GSM/EDGE and UMS/HSPA network technologies. he LE system aim is to afford an important effective bit rate and allows increasing system capacity in terms of numbers of simultaneous calls per cell. In addition, it has a low latency compared to 3G/3G + networks. It offers a theoretical speed of 00 Mbits / s in the Downlink and 50Mbits/s in the Uplink transmission. o satisfy these requirements, LE system uses the association of Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) techniques. he OFDM provides the signal transmitted robustness against the multipath effect and can improve the spectral efficiency of the system [3][4], in addition, the implementation of MIMO system increases channel capacity and decreases the signal fading by sending the same information at the same time through multiple antennas [4]. he combination of these two powerful technologies (MIMO-OFDM) in the LE system improving thus the spectral efficiency and throughput offered without increasing resources for base bands and power output. Moreover, channel estimation plays a crucial role in the improvement of Downlink LE system performance. Many investigations have been conducted on channel estimation strategies in LE [5], [6], [7].But the majority ignores the nonlinear effects in LE transmission. Indeed, the nonlinear effects exist in reality and particularly due to the power amplifier used in the OFDM transmitter. In this paper, we analyze the Wiener Hammerstein nonlinear channel estimation and we adapt it for the Downlink LE system then a simulation is executed over this system after introducing nonlinear effects. Finally, performances of this simulation are discussed. In the section I, we describe the MIMO-OFDM transmission for LE system, as well as the power amplifier model used in our simulation. he Wiener Hammerstein technique is detailed in section III. In Section IV, we evaluate the performance of the channel estimators in terms of data throughput and Block Error Rate (BLER). Finally, Section V concludes the paper with a summary of most important points obtained in this All rights Reserved 38

2 International Journal of Current rends in Engineering & Research (IJCER) II. MIMO-OFDM RANSMISSION A. System description In this section we depict the MIMO-OFDM system model for Downlink LE transmission. he binary information is first grouped, coded using turbo encoder, and mapped using the complex constellation QPSK, 6 QAM or 64 QAM. After inserting the pilots symbol according to the LE standard, an N Inverse Fast Fourier transform (IFF) block transforms the modulated data into time domain. After the IFF block, a cyclic prefix of time length G, chosen to be larger than the expected delay spread, is inserted to mitigate the inter-symbol and inter sub-carrier interferences. ransmission is made through a multipath channel over a multiple antenna system. Multiple antennas can be used in the transmitter and the receiver; consequently, multiple-input multiple-output (MIMO) encoders are needed to increase the spatial diversity or the channel capacity. Applying MIMO allows us to get a diversity gain to remove signal fading or getting a gain in terms of capacity. At the receiver, after removing the CP, the FF block transform the data back to the frequency domain. hen the reference symbols are extracted and the received symbols are estimated. Finally, the binary information data is obtained back after the demodulation and channel decoding. Fig.. MIMO-OFDM transmission B. Pilots Signal For LE Downlink System According to the LE standard, the pilots location are defined on a well-defined ways to cover up the frequency and time domain. heposition of the pilots for MIMO-LE system * is shown in following figure. Fig.. Pilot scattering for Downlink LE All rights Reserved 39

3 International Journal of Current rends in Engineering & Research (IJCER) We can observe, through the first antenna, pilots are disposed, respectively, in OFDM symbols numbers, 5,8 and while for the second antenna; they are placed in the same OFDM symbols, but in the different subcarriers index. his hybrid location of pilots permits to eliminate the risk of interference in reception [8]. C. Solide State Power Amlifier(SSAP): limitter model In Wireless system, the nonlinear effects were introduced principally by the use of power amplifier in OFDM transmission. In our simulation, we choose a Limiter model for a SSAPpower amplifier. he expression of Limiter ransfer function is given as follow: x x s gx ( ) s x s Where gx ( ) is output of power amplifier for a particular input x, S is the Saturation level. his model does not consider AM/PM conversion [9]. he conversion characteristics of power amplifier is given by: * / = /(+( / ) p p Vout Vin Vin Vs ) () Where Vinis the complex input, Vout is the complex output, Vs is the saturation level and p is knee factor which controls the transition from the linear part to the saturation part of characteristic curve (a typical value of p is ). As the value of knee factor increases the SSPA model approaches the Limiter Model. SSPA model is very accurate in defining the transfer characteristics of solid state amplifiers which are now mainly used in transmitters [0]. III. DESCRIPION OF HE WIENER HAMMERSEIN ESIMAION MEHOD A. Least Mean Square (LMS) Algorithm In this paper, we used the LMS algorithm in order to combat linear effects introduced by the multipath transmission: least mean square algorithm is a linear adaptive filtering belongs to the family of gradient algorithms stochastic [] []. For an adaptive linear filter with L number of coefficients, the output signal at many time n can be represented as follow[] [] : L y( n) wi ( n) x( n ) (3) i0 xn ( ) is the filter input signal, wi ( n) is the tap coefficient at time n. We can express all the coefficients wi ( n ) and the input signals xn ( ) in a vector form, as shown in (04) and (05): he desired output vector D is: w ( n) [ w ( n), w ( n),..., w ( n)] (4) i 0 L U( n) [ x( n), x( n),..., x( n L )] (5) D( n) [ d(), d(),..., d( i)] (6) Admit, we use the linear filter to model an unknown system. At time n, en ( ) is the difference between the desired signal dnand ( ) the filter output ynwhich ( ) is given by: e( n) d( n) y( n) d( n) w( n) U( n) (7) he coefficients vectorof the filter can be updated by the gradient of the mean square error as shown in (08): All rights Reserved 330

4 International Journal of Current rends in Engineering & Research (IJCER) w( n ) w( n) { E[ e ( n)]} (8) As en ( ) is a function of filter coefficients, the gradient of the mean square error can be estimated as follows: { E[ e ( n)]} { e ( n)} e( n) { e( n)} (9) en ( ) w0 ( n) en ( ) w ( n) { en ( )} 0 { E[ e ( n)]} e( n) en ( ) wl ( n) xn ( ) xn ( ) x( n L ) B. Wiener Hammerstein model In this section, we detail one of alternative solution for nonlinear compensation:the Wiener- Hammerstein model. Wiener-Hammerstein model is one of the commonly used block-oriented nonlinear structures [3], which comprises a cascading association of a Wiener and Hammerstein systems. he Wiener system is composed by a linear filter attended by memoryless nonlinearity, while the Hammerstein system is composed by a memoryless nonlinearity followed by a linear filter. Consequently, the Wiener-Hammerstein model can be defined by memoryless nonlinearities sandwiched by two linear filters. hememoryless function can be approximate as a polynomial of finite degree. his kind of nonlinear system has been used in the physiological system modeling [4], the power amplifiers modeling [5], the power amplifiers nonlinearity compensation [6], the acoustic echo cancellation [7, 8], the biological applications [9], and etc. he employment of Wiener-Hammerstein model in LE system is used to mitigate both multipath and nonlinear effects introduced respectively by multipath channel transmission and OFDM amplifier. Figure 3 depicts the Wiener-Hammerstein model, the first subsystem is the linear filter(fir). he second subsystem is the nonlinear polynomial filter derived from memoryless nonlinearity sandwiched, and the third subsystem is another FIR filter []. () (0) X(n) Non-Linear d(n) e(n) + ransmission System FIR y(n) Non-Linear z(n) P(n) FIR Filter Fig. 3. Wiener-Hammerstein system for channel All rights Reserved 33

5 International Journal of Current rends in Engineering & Research (IJCER) After passing through the first FIR filter, the input-output relation in a discrete and time invariant form is displayed as following, where M is the first FIR filter memory length. M y( n) u( i) x( n i) () i0 u( n) [ x( n), x( n ),..., x( n j),..., x( n L )] (3) he multipath channel is considered as a pass band filter, so only the odd order terms in the sandwiched nonlinear subsystem can generate nonzero output and the even order terms are neglected. In this work, we choose the polynomial order of memoryless function equal to 3. he output signal after the center nonlinear filter is written as: z( n) v(0) y( n) v() y ( n) y ( n) (4) he relationship between the output and the input signals of the second FIR filter is represented as follow : M P( n) w( i) z( n i) (5) i0 where M is the second FIR memory length. he difference between desired signal dn ( ) and the filter output Pnis ( ) given by: he vector form is shown in (7) (8) (9): e( n) d( n) P( n) (6) u( n) [ u ( n), u ( n),..., u ( n)] (7) 0 M v( n) [ v ( n), v ( n)] (8) 0 w( n) [ w ( n), w ( n),..., w ( n)] (9) 0 M he input signals of the first FIR filter, the nonlinear filter and the second FIR filter are shown in: x( n) [ x(), x(),..., x( n M )] (0) y( n) [ y( n), y ( n) y ( n)] () z( n) [ z(), z(),..., z( n M )] () he output signal after the first FIR filter, the nonlinear filter and the second FIR filter can be calculated by: y( n) u ( n) x( n) (3) z( n) v ( n) y( n) (4) P( n) w ( n) z( n) (5) We choose a memory length of for both linear filters. We use the joint normalized LMS (joint NLMS) algorithm to update the linear and nonlinear subsystem coefficients of the Wiener- Hammerstein model jointly [0]. he NLMS method needs to be modified for the complex Wiener- Hammerstein model determination []. he detailed procedure is summarized in able 6 [0, ]. he difficulty in the Wiener-Hammerstein model coefficients estimation is that the coefficients update is dependent among each subsystem [0]. he estimation is very sensitive to the coefficients initial condition and the step size. o solve this problem, we perform the update procedure in All rights Reserved 33

6 International Journal of Current rends in Engineering & Research (IJCER) iterations, making the current coefficients vector as the next estimation starting point. he Wiener Hammerstein algorithm is presented as follow []: Initialisation : u(0) v(0) w(0) Where is a small positive constant. Loop: Update procedure (n=,, 3...): x( n) [ x(), x(),..., x( n M )] (6) y( n) u ( n) x( n) (7) y( n) [ y( n), y ( n) y ( n)] (8) z( n) v ( n) y( n) (9) z( n) [ z(), z(),..., z( n M )] (30) P( n) w ( n) z( n) (3) e( n) d( n) P( n) (3) * ( n) [, y( n) y ( n)] (33) b( n) v ( n) ( n) (34) x ( ) ( ) ( ) n n b n x n (35) X ( n) [ x ( n), x ( n ),..., x ( n M )] (36) n n n p( n) X ( n) w( n) (37) Y( n) [ y( n), y( n ),..., y( n M )] (38) q( n) Y( n) w( n) (39) u( n ) u( n) ( / p( n) )) p ( n) e( n) (40) Where u, and are positive small constant. w Go back to Loop (end after several iterations) u ( n ) ( n) ( / q( n) )) q ( n) e( n) (4) w( n ) w( n) ( / z( n) )) z ( n) e( n) (4) C. Wienner-Hammerstein equalizer structure he figure 4 depicts the Wiener-Hammerstein equalizer structure used in this simulation; first, we calculate the error given by the expression (5) for pilots signal only then, the totality of error matrix is estimated using the block of Lagrange polynomial interpolation []. In the next step, this error matrix is exploited by the Wiener-Hammerstein block to estimate the totality of received signal. All rights Reserved 333

7 International Journal of Current rends in Engineering & Research (IJCER) 6 Y i,k Wienner- Hammerstein Equalizer Lagrange Polynomial Interpolation Fig. 4. Wiener Hammerstein struture for LE system IV. SIMULAION RESULS In this section, we discuss the performance of Wiener Hammerstein equalizer fordownlink LE System. he simulation was made in presence of nonlinear effects introduced by the power amplifier of OFDM ransmitter. he simulation parameters respond tothe LE standard and are summarized in able I.he transmission was executed in a bandwidth of.4mhz,over multipath channel usinga Pedestrian B model.we choose a CQI = 7 (6QAM) according to the LE standard,eachsnr values includes the transmission of 000 frames.weuse a * MIMO transmission mode. We analyze the performance of Downlink LE system when we use a channel estimation based on Wiener Hammerstein method of equalization. For comparison purpose, an adaptive linear equalizer LMS is included in the simulation to evaluate the performance of Wiener-Hammerstein nonlinear equalizer in term of BLER, hroughput and EVM(%) versus SNR are plotted. ABLE I. PARAMEERS SIMULAION ransmission Bandwidth.4 MHz Number of sub frames 000 Number of transmit antennas Number of receive antennas Channel Quality indicator 7 Modulation order 6QAM Channel type IU-Ped B Equalization Wiener Hammerstein & LMS he Fig. 5 and Fig.6 depicts the Block Error Rate and the hroughput versus SNR.For each figures, we present the performance of Downlink LE system through perfectchannel estimation, Wiener-Hammerstein equalizer and LMS equalization method. In Fig. 5,we observe that the Wiener- Hammerstein equalizer gives a much stronger resistance to nonlinear effects than LS estimator. In fact, itimproves the system performance compared with a considerable gain of almost 8 db for a BLER of0-. hrough the Fig. 6, it can be seen that the use of Wiener-Hammerstein equalizer outperform the performance of system in term of hroughput. For example, with a hroughput equal to Mbit/s we have a gain of 4 All rights Reserved 334

8 hroughtput(mbit/s) BLER International Journal of Current rends in Engineering & Research (IJCER) 0 0 BLER vs. SNR, IU-PedB, BW=.4 MHZ, MIMO ransmission MIMO-6QAM-Perfect MIMO-6QAM-WH MIMO-6QAM-LMS SNR Fig. 5. BLER vs SNR usin Wiener Hammerstein & LMS.5 hroughput vs. SNR, IU-PedB, BW=.4 MHZ, MIMO ransmission 0.5 MIMO-6QAM-Perfect MIMO-6QAM-WH MIMO-6QAM-LMS SNR Fig. 6. hroughtput vs SNR usin Wiener Hammerstein & LMS In Fig. 6, we depict the percentage of Error Vector Magnitude (%) against SNR. EVM is given by the percentage of error concerning the distance between ideal and transmitted symbol position. We observe, over the Fig. 6 that the EVM measurement performance for Wiener Hammerstein algorithm is clearly better than LMS algorithm. In fact, for a SNR=0dB, the percentage of error is almost 0% whereas it is almost 0% for LMS All rights Reserved 335

9 EVM(%) International Journal of Current rends in Engineering & Research (IJCER) EVM vs. SNR, IU-PedB, BW=.4 MHZ, MIMO ransmission MIMO-6QAM-Perfect MIMO-6QAM-WH MIMO-6QAM-LMS SNR Fig. 7. EVM(%)vs SNR usin Wiener Hammerstein & LMS Over the results plotted in Fig. 5, Fig. 6 and Fig.7, it can be observed that the Wiener Hammerstein equalizer is clearly more adapted to the nonlinearity transmission ofdownlink LE system. In fact, it s due to the Wiener Hammerstein structure which includes two FIR that remove linear interference caused by multipath channel. In the other hand, the nonlineareffects are taken into account by thememorylesslinear block presented by a third order polynomial. V. CONCLUSION In this paper, we analyzethe Wiener-Hammerstein channel estimation method fordownlink LE System. We executed a simulation in presence of nonlinear effects introduced by Solid State Power Amplifier (SSAP) model of OFDM transmitter power amplifier. hen we investigate the impact of nonlinear effects ondownlink LE system performance in presence of Wiener-Hammerstein channel estimation. he system with Wiener Hammerstein equalization achieves a considerable performance improvement in terms of BLER and hroughput compared to a linear equalization LMS. For BLER = 0 -. It offers a gain of 8 db compared to LMS equalizer in term of throughput = Mbit/s, we have a gain of 5dB also. It s due to the structure of Wiener-Hammerstein equalizer which contains a memoryless nonlinear block to mitigate nonlinear effects.he major problem is the complexity of Wiener Hammerstein equalizer, in our future research we aim to reduce this complexity by simplifying the memoryless nonlinear complexity. REFERENCES [] 3rd Generation Partnership Project, echnical Specification Group Radio Access Network; evolved Universal errestrial Radio Access(URA): Base Station (BS) radio transmission and reception, pp. 33, S 36.04, V8.7.0, 009. [] 3rd Generation Partnership Project, Evolved Universal errestrial Radio Access (E-URA); User Equipment (UE) radio transmission and reception, pp. 33, ARIB SD , V8.4.0, 008. [3] S. Sesia, I. oufik, and M. Baker, LE he UMS Long erm Evolution from heory to Practice, st ed, Jonh Wiley and sons, LD.UK;009 [4] echnical White paper: Long erm Evolution (LE): A echnical Overview, bymotorola. 0Providers/Wireless%0Operators/LE/_Document/Static%0Files/6834_MotDoc_New.pdf [5] M. Simko, D. Wu, C. Mehlf uhrer, J. Eilert, and D. Liu, Implementation aspects of channel estimation for 3GPP LE terminals, in Proc.European Wireless 0, Vienna, April 0. [6] A. Ancora, C. Bona, and D..M. Slock, Down-sampled impulse response least-squares channel estimation for LE OFDMA, in 007. ICASSP 007. IEEE International Conference on Acoustics, Speech and Signal Processing, April 007, vol. 3, pp. III 93 III All rights Reserved 336

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