1. International Journal of Electronics and Communication Engineering & Technology (IJECET),ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 3, May – June (2013), © IAEME115CHANNEL ESTIMATION FOR HIGH DATA RATECOMMUNICATION IN MOBILE WI-MAX SYSTEMJagdish D. Kene1, Dr. Kishor D. Kulat21Ph D Scholar, Electronics Engineering Department, V.N.I.T., Nagpur, M.S., India2Professor, Electronics Engineering Department, V.N.I.T., Nagpur, M.S., IndiaABSTRACTHigh data rate is one of the main challenges in next generation wireless system in themobile environment. Mobile Wi-Max system based on IEEE 802.16e standard supports highthroughput by implementing Orthogonal Frequency Division Multiple Access (OFDMA). InMobile environment, channel estimation is the important key for studying the systemperformance. In this paper, downlink channel estimation in mobile Wi-Max is evaluated byexploiting pilot at data symbol. Using two interpolation schemes namely (i) Least SquareError (LSE) and (ii) Minimum Mean Square Error (MMSE), the evaluation of downlinkchannel estimation is carried out in frequency domain that provides high data ratetransmission with low hardware complexity. The characteristics of interpolation methods aremeasure in terms of Bit Error Rate (BER) performance of the channel estimated over OFDMsymbols. Author has shown that, the MMSE outperform LSE technique for mobile Wi-Maxapplication with reference to ideal channel condition.Keywords: Channel Estimation, LSE, MMSE, Mobile Wi-Max, OFDMA.1. INTRODUCTIONThe next generation wireless system demands high data rates to provide the servicesto the applications like internet protocol Television (IPTV), video on demand (VOD), voiceover IP (VOIP) etc. Worldwide Interoperability for microwaves Access (Wi-Max) system isdesign to support high mobility and bit rate greater than 5 M bits/sec and target to reach up to100 M bits/sec[1]. Wi-Max is based upon IEEE802.16 & standard for wireless MetropolitanArea Network (WMAN) [2]. It enables operators to offer diverse wireless services to fixedand mobile users. The IEEE 802.16e standard was published in year 2004 for Fixed WirelessINTERNATIONAL JOURNAL OF ELECTRONICS ANDCOMMUNICATION ENGINEERING & TECHNOLOGY (IJECET)ISSN 0976 – 6464(Print)ISSN 0976 – 6472(Online)Volume 4, Issue 3, May – June, 2013, pp. 115-123© IAEME: www.iaeme.com/ijecet.aspJournal Impact Factor (2013): 5.8896 (Calculated by GISI)www.jifactor.comIJECET© I A E M E
2. International Journal of Electronics and Communication Engineering & Technology (IJECET),ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 3, May – June (2013), © IAEME116Access (FWA) applications using OFDM technology [5]. In December 2005, the IEEE602.16e standard was published to support Mobile Wireless Access (MWA) is based onOrthogonal Frequency Division Multiple Access (OFDMA) technology [6]. OFDM istransmission technique of multi-carrier modulation scheme in which the wide transmissionbandwidth is divided into narrower bands and data is send in parallel on that narrow bands.The basic principle behind OFDM is that a signal with long symbol duration time is lesssensitive to multipath fading than a signal with a short symbol time. Hence Wi-Max systemperformance can be improved by sending several parallel symbols with a long symbol timethan sending them in a series with a shorter symbol time. This result in reducing the InterSymbol Interference (ISI).The remaining ISI effect is eliminated by cyclically extending thesignal [5][8].OFDM is basically used for fixed Wi-Max. OFDMA is a multiuser version of OFDMused for mobile Wi-Max. In addition mobile Wi-Max uses scalable OFDMA (SOFDMA) as atransmission technique where band width is scalable between 1.25 to 20 MHz. (IEEE802.16e). The scalability is achieved by making the FFT size flexible for constant subcarriersspacing [3]. In the realization of promises made by Wi-Max system, the receiver algorithms(Decoder/Demodulator) play a very important role. Channel estimation, which is one of thekey blocks in receiver section of Wi-Max system. It is one of most important elements ofwireless receivers that employ coherent demodulation [4].Generally channel estimation is a challenging problem in any wireless system becausethe radio channel is highly dynamic compare to other guided media. While travelling throughthe radio channel, signal undergoes various noise effects that corrupt the signal and place thelimitation on the system performance. To realize the changes in received in signals, channelestimation has been studied specifically for OFDM based Wi-Max system [7], [8]. Althoughthere are many channel estimation techniques for wireless system reported in the literature formobile Wi-Max system. It is important to employ estimation techniques that is specificallydesign for Wi-Max pilots or preambles and offers low computational and hardwarecomplexities. In this paper, different channel estimation techniques are analyzed for downlinktransmission of Wi-Max system. For this, approaches based oni) Least Square (LS) estimationii) Minimum Mean Square Error (MMSE)The comparisons are made with reference to different channel parameters. It is observed thatMMSE estimator offers a good trade-off between the system performance and complexity.The rest of the paper is organized as follows: In section 2 system model will be introduced.Channel estimated algorithms will be explained in section 3, Simulation and results arepresented in section 4. Conclusions are drawn in section 5.2. SYSTEM MODELThe mobile Wi-Max system based on IEEE 802.16e is represented in block schematicis shown in fig.1. The data bits are generated randomly, that are encoded and interleaved.Then stream of data are map into QPSK or QAM signals. In the downlink channel,subcarriers are divided into clusters or frames and sub-channelized by inserting the pilots. Inthis one or more sub-channels are assigned to the different mobile users. Data and pilotsubcarriers are allocated within each frame, which is physically transmitted over the channel,after the sub-channelization, frame is fed to a IFFT block that produces corresponding timedomain signal x (n) and cyclic prefix is added to each symbol before transmission. At the
3. International Journal of Electronics and Communication Engineering & Technology (IJECET),ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 3, May – June (2013), © IAEME117receiving end, guard time is removed from received signal y (n) which constitute channelimpulse response h (n) and additive white Gaussian noise w (n) and then fed to FFT blockthat convert signal into frequency domain. After the pilot extraction OFDMA symbols arecollected and rearranging the subcarriers. The original data is recovered by performing thesuitable decoding technique. The principle of OFDM technique is to convert serial datastream into parallel blocks of size N number of symbols stream and each of which ismodulates using Inverse Fast Fourier Transform (IFFT). In the frequency domain, eachOFDM symbol is made by mapping the symbols on the subcarriers.Figure 1: Block Diagram of Mobile Wi-Max (IEEE 802.16e OFDMA-PHY)The OFDMA symbol used in mobile Wi-Max has three classes of subcarriers [8].• Data Carriers: Used for data transmission.• Pilot Carriers: Used for carrying pilot symbols. Since the magnitude and phase ofthese carriers are known to the receiver and they are used for channel estimation.• Null Carriers: These carriers include DC subcarriers and Guard subcarriers do nothave transmitted energy. These are used to enable the signal to naturally delay andprevent leakage of energy into adjacent channels.The typical frequency domain representation of Wi-Max OFDM symbol is shown in fig.2.Figure 2: Frequency Domain Representation of OFDM Symbol
4. International Journal of Electronics and Communication Engineering & Technology (IJECET),ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 3, May – June (2013), © IAEME118The time domain samples of an OFDM symbol can be obtained from frequency domain datasymbols as( ){ }( )x n IFFT X k=( ) 2 /1eNJ nk NnX k Π== ∑ 0 1n N≤ ≤ − (1)Where X(k) is transmitted data symbol at kthsubcarrier of the OFDM symbol.N defines the size of Fast Fourier transform.The cyclic prefix (CP) is added to the time domain OFDM symbol. After passing the signalthrough Digital to Analog converter, signals are transmitted over the mobile radio frequencychannel. The impulse response of the channel is assumed to be constant over an OFDMsymbol.At the receiving end, the signal is received with noise. Cyclic prefix is removed aftersynchronization. On the basis of perfect synchronization between time and frequencydomain, the simple form of baseband model of the received samples can be formulated as( ) ( ) ( )0( )Lly n x n l h l w n== − +∑ (2)Where L is the number sample spaced channel taps.w(n) is Additive White Gaussian Noise (AWGN) sample with zero mean and variance 2wσ .h(l) is channel impulse response for the current OFDM symbol and given as time invariantlinear filter.In the receiver part, FFT of the received signal y(n) has been done. The samples infrequency domain can be written as( ) ( ){ }Y k FFT y n=( ) ( ) ( )X k H k W k= + (3)Where H(k) and W(k) are FFTs of h(n) and w(n) respectively.3. CHANNEL ESTIMATION METHODSIn order to neutralize the effects of the channel, the channel estimation should be doneat receiver. The channel estimation shown in figure 1 for base band OFDM system can bebased on Least square and minimum mean square error estimates. The structure of OFDMsignaling permits a channel estimator that uses both times as well as frequency correlation.However, such a two dimensional structure is too complex for practical implementation. Thelow complexity estimators can be modeled by the use of frequency correlation only. In thispaper channel estimation of Wi-Max system can be test with following methods.(i) Least Square Error (LSE) Channel Estimator.(ii) Minimum Mean Square Error (MMSE) Channel Estimator.
5. International Journal of Electronics and Communication Engineering & Technology (IJECET),ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 3, May – June (2013), © IAEME1193.1. Least Square Error (LSE) EstimatorThe least square estimator is very basic and simple channel estimator. It can easily beimplemented without knowing the channel statistics compare to other techniques. It works onthe principle of dividing the received signal by the symbols that have actually been sent, i.e.the symbols that are supposed to known [7]. The channel frequency response (CFR) iswritten asLSYHX=WHX= + (4)The division is indicative of an element wise division of Y by X. it is a simplisticmodel with only one division per carrier. In this conventional channel estimation method, theestimation of pilot signals is susceptible to AWGN and Inter Carrier Interference (ISI). Sincethe channel responses of data subcarriers are obtained by interpolating the channelcharacteristics of pilot subcarriers that result in poor performance of OFDM based Wi-Maxsystem [8], [13].3.2. Minimum Mean Square Error (MMSE) EstimatorIn this estimator technique, the mean squared error between the actual and estimatedchannels is minimized. Therefore it is known as minimum mean square error estimator.MMSE uses additional information like the operating signal to noise ratio and the channelstatistics. MMSE provides smooth interpolation and hence it is widely used for channelestimation of OFDM based system with pilot subcarriers in downlink channel ofIEEE802.16e standard. In statistics, the mean squared error (MSE) of an estimator is theexpected value of the square of an error. The error is the amount by which the estimatordiffers from the quantity to be estimated. The difference occurs because the estimator doesn’taccount for information that could produce a more accurate estimate [12]. However thecomputational complexity of MMSE is very high due to extra information such as thecorrelation between subcarriers and noise variance. The MMSE of the variable X (i.e.Diagonal matrix that contains transmitted symbols) for given linear time invariant systemmodel is define as1Y X Y YX R R Y∧−= (5)Where RYX is the cross correlation between variables x and y. when the above equation isapplied to the OFDM channel estimation, then CFR of MMSE estimator is written as( )( )112P P PHMMSE HH H H LSH R R XX Hωσ−∧ −= + (6)Where Hp is the CFR at the pilot subcarriers, RHHP is the cross correlation between all thesubcarriers and the pilot subcarriers, σω represent the channel noise variances [8], [12], [13].
6. International Journal of Electronics and Communication Engineering & Technology (IJECET),ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 3, May – June (2013), © IAEME1204. SIMULATIONS AND RESULTSIn this paper, channel estimation method is based on clusters or frames of transmittedsymbols. The mobile Wi-Max standard has implemented very unique permutation methodcalled Downlink Partially Used Sub channelization (DL-PUSC) that helps to minimize theeffect of channel fading [7]. The channel estimation methods which are mentioned inprevious section are evaluated based on IEEE 802.16e OFDMA physical layer. Forsimulation, we consider the Wi-Max system operating in the 2.3 GHz frequency band anddownlink PUSC sub channel allocation. The various system parameters used in thesimulation are indicated in Table 1. The parameters are considered with the assumption thatthe channel frequency response is constant over an OFDM symbol, but the time varyingwithin an OFDMA frame for low and medium mobility [9]. The estimator performance ismeasured in terms of Bit Error Rate vs. SNR as shown in fig. 3. The BER performance ofMMSE is smoother than LS. i.e. MMSE estimation method performs better than LSEmethod. The BER performance of these estimator also compare with reference to estimationof ideal channel. i.e. the channel conditions are perfectly known to the receiver section alsoshown in fig. 3. The scatter plot of received signal and equalized signal with QPSKmodulation based on LSE and MMSE estimation are shown in fig. 4 and Fig. 5 respectively.We observed that the MMSE estimated symbols are comparatively less scattered than LSestimation. It means that MMSE method is more robust against noise and helps Wi-Maxsystem to get better recovery of corrupted symbols.Table 1: Parameters considered for simulation Mobile WiMax systemParameters ValueSystem Bandwidth 8.75 MHzSampling frequency 10 MHzFFT size 1024Channel coding Convolution coding (CC)Code rate ½Modulation QPSKCyclic prefix ratio 1/8Guard interval 128Channel AWGNTotal number of subcarriers(Data and Pilot)841Number of Guard subcarriers183(92 on left and 91on right)
7. International Journal of Electronics and Communication Engineering & Technology (IJECET),ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 3, May – June (2013), © IAEME121Figure 3: BER Performance of LSE and MMSE Channel Estimator w. r. t. Ideal channelconditionFigure 4: Scatter Plot of LSE Channel Figure 5: Scatter Plot of MMSE ChannelEstimator Estimator
8. International Journal of Electronics and Communication Engineering & Technology (IJECET),ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 3, May – June (2013), © IAEME1225. CONCLUSIONIn this paper, two different channel estimation algorithms for downlink mobile Wi-Max are analyzed and compare the BER performance under different system parameters. Thechannel response of pilot subcarriers are estimated by LSE and MMSE estimator based onpilot at symbol data in frequency. From the simulation results, we conclude that, the proposedMMSE channel estimation method with fixed coefficients achieves better BER performancecompare to conventional LSE estimation method. Thus by fixing the parameter coefficient,MMSE helps to mitigate the practical hardware and computational complexity. Owing to thisMMSE channel estimation scheme is effectively useful than LSE estimation for high datarate applications of downlink mobile Wi-Max (IEEE 802.16e standard) system.REFERENCES[1] S. Y. Hui and K. H. Yeung, Challenges in the Migration to 4G Mobile Systems, IEEECommunications Magazine, 41(12), 2003, 54-59.[2] J. D. Kene, and K. D. Kulat, Performance Evaluation of IEEE 802.16e Wi-Max PhysicalLayer, Proc. of IEEE Conf. NUiCONE, Ahemdabad, Gujrat, India2011, 1-4.[3] J. D. Kene, and K. D. Kulat, Performance Optimization of Physical Layer Using TurboCodes: A Case Study of Wi-Max Mobile Environment, Proc. of IEEE Conf., ET2ECN, Surat,Gujrat, India, 2012.[4] J. D. Kene, and K. D. Kulat, Soft Output Decoding Algorithm for Turbo CodesImplementation in Mobile Wi-Max Environment, Elsevier Journal, 6, 2012, 666-673.[5] IEEE 802.16-2004, Part 16: Air interface for fixed broadband wireless access systems,2004.[6] IEEE 802.16e-2005, Part 16: Air interface for fixed and mobile broadband wireless accesssystems, 2006.[7] M. F. Mohamad, M. A. Saeed, and A.U. Priantoro, Downlink Channel Estimation andTracking in Mobile WiMAX Systems, Proc. of IEEE Conf. on Computer andCommunication Engineering, 2008.[8] T. Yucek, M. K. Ozdemir, H. Arslan, and F. E. Retnasothie, A Comparative Study ofInitial Downlink Channel Estimation Algorithms for Mobile WiMAX, Proc. of IEEE Conf.on Mobile WiMAX Symposium, 2007, 32-37.[9] S. Galih, R. Karlina, F. Nugroho, and A. Irawan, High Mobility Data Symbol BasedChannel Estimation for Downlink OFDMA IEEE 802.16e Standard, Proc. of IEEE Conf. onElectrical Engineering and Informatics,2009, 478 – 483.[10] B. Ouarzazi, M. Berbineau, I. Dayoub and A. M. Rivenq, Channel estimation of OFDMsystem for high data rate communications on mobile environments, Proc. of IEEE Conf. onIntelligent transport Systems Telecommunications, 2009, 425 – 429.[11] J. Rinne and M. Renfors, Pilot spacing in orthogonal frequency division multiplexingsystems on practical channels, IEEE Trans. Consumer Electron., 42(4), 1996, 959–962.[12] J.J. van de Beek, O. Edfors, M. Sandell, S. Wilson, and P. Borjesson, On channelestimation in OFDM systems, Proc. IEEE Veh. Technol Conf., Chicago, IL, 1995, 815–819.[13] D. H. Lee, S. C. Kim, D. C. Park, and Y. Kim, A Comparative Study of ChannelEstimation for Mobile WiMAX System in High Mobility, Proc. of IEEE Conf. on AdvancedCommunication Technology, 2008, 781–785.
9. International Journal of Electronics and Communication Engineering & Technology (IJECET),ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 3, May – June (2013), © IAEME123[14] M. Morelli and U. Mengali, A comparison of pilot-aided channel estimation methods forOFDM systems, IEEE Trans. Signal Processing, 49(12), 2001, 3065–3073.[15] O. Edfors, M. Sandell, J.J. van de Beek, S. Wilson, and P. Borjesson, OFDM channelestimation by singular value decomposition, IEEE Transactions Communication, 46(7), 1998,931–939.[16] H. Yaghoobi, Scalable OFDMA Physical Layer in IEEE 802.16 Wireless MAN, IntelTechnology Journal, 8(3), 2004, 201-212.[17] M. K. Ozdemir and H. Arslan, Channel estimation for wireless OFDM systems, IEEECommunications Survey & Tutorials, 9(2), 2007, 18-48.[18] S. Vaughan-Nichols, Achieving wireless broadband with WiMax, Computer, 37(6),2004, 10–13.[19] J. Andrews, A. Ghosh, and R. Muhamed, Fundamentals of WiMAX: UnderstandingBroadband Wireless Networking, Prentice Hall, 2007.[20] R. Prasad and R. Van Nee, OFDM For Wireless Multimedia Communications, ArtechHouse Publisher, 2000.[21] Abhishek choubey, Mayuri Kulshreshtha and Karunesh, “Determination of OptimumFFT for Wi-Max under Different Fading”, International Journal of Electronics andCommunication Engineering &Technology (IJECET), Volume 3, Issue 1, 2012,pp. 139 - 146, ISSN Print: 0976- 6464, ISSN Online: 0976 –6472.[22] Kamatham Harikrishna and T. Rama Rao, “An Efficient Radix-22 Fft For Fixed &Mobile Wimax Communication Systems”, International Journal of Electronics andCommunication Engineering &Technology (IJECET), Volume 3, Issue 3, 2012,pp. 265 – 279, ISSN Print: 0976- 6464, ISSN Online: 0976 –6472.
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