Channel Estimation and Carrier Frequency Offset Estimation for Multiuser Uplink for OFDM System

Channel Estimation and Carrier Frequency Offset Estimation for Multiuser Uplink for OFDM System

Neetha. L

Department of Electronics and Communication Engineering,

Gitam University, Visakhapatnam, INDIA.

Abstract – In this paper we are estimating carrier frequency offset by using two different methods and we are showing that the performance of proposed method is better than the performance of Fuscos method because in the fuscos method we are using time domain method so that the signal is in the series so we cannot send more signals it takes more time so in the proposed method we are estimated the CFO by using frequency domain in this method the signal is travelled in parallel so more signals can be send through this method. In this paper we also estimated the iterative channel estimation by using LS &MMSE methods in fading conditions. And we show the MMSE is the best method for the channel estimation.

Keyword: Orthogonal frequency division multiplexing (OFDM), Carrier frequency offset estimation (CFO), channel estimation, Least square (LS), Minimum mean square error (MMSE).

I.INTRODUCTION

In the last years, the interest for filter-bank multicarrier (FBMC) systems is incremented, since they provide high spectral containment. Therefore, they have been taken into account for high-data-rate transmissions over both wired and wireless frequency-selective channels. One of the most famous multicarrier modulation techniques is orthogonal frequency division multiplexing (OFDM), embedded in several standards such as digital audio and video broadcasting or Wi-Fi wireless LANs IEEE 802.11a/g. Other known type of FBMC systems are Filtered Multitone (FMT) systems, that have been proposed for very high-speed digital subscriber linestandards and are under investigation also for broadband wireless applications and moreover, OFDM based on offset QAM modulation (OQAM), considered by the 3GPP standardization forum. Unlike OFDM, OFDM/OQAMsystems do not require the presence of a cyclic prefix (CP) inorder to combat the effects of frequency selective channels.

N. V. S. V. Vijay Kumar

Department of Electronics and Communication Engineering,

Gitam University, Visakhapatnam, INDIA.

The absence of the CP implies on the hand the maximum spectral efficiency and, on the other hand, an increased Computational complexity. However, since the subchannel filters are obtained by complex modulation of a single filter, efficient polyphase implementations are possible. Another fundamental difference between OFDM and OFDM/OQAM systems is the adoption in the latter case of pulse shaping filters very well localized in time and frequency.Orthogonal frequency division multiplexing (OFDM) systems have attracted great interest in wireless and wire-line transmissions because of their robustness to multipath channels. OFDM/OQAM systems giving rise to interference between successive symbols and adjacent subcarriers. Therefore, reliable and accurate timing and carrier-frequency offset (CFO) estimation schemes must be designed for these systems.

OFDM system is sensitive to carrier frequency offset estimation (CFO), which leads to degrade the system performance. The CFO is the mismatch of the transceiver oscillators or the Doppler Effect. The CFOs lead to both inter-carrier interference and multiple-access interference, which could severely degrade the system performance. Previously the CFO estimation scheme for OFDMA uplink transmissions has intensively been investigated like by using frequency domain pilot system,Using ML algorithm, but those methods are complex.

In this paper we are estimating carrier frequency offset estimation for multiuser uplink and also we are estimating channel estimation in fading conditions. CFOEstimation is same as in the paper [1] with just changes we are estimating by using same estimators. The CFO estimated by using Fuscos estimator as the first estimator and the proposed method is the second estimator. In paper [2]considered the problem of data-aided joint symbol timing and carrier frequency offset estimation for filter bank based multicarrier systems. In that they provided new joint symbol timing and CFO synchronization algorithm exploiting the transmission of a training sequence.

In the CFO estimation in the uplink of an OFDMA system, the base station (BS) needs to estimate multiple CFOs from multiple users. The choice of CFO estimation

methods in the BS is closely related to the adopted subcarrier allocation scheme [3]. The main aim of this paper is to propose a low-complexity data-aided CFO estimation for OFDM with block allocation scheme.And also we are estimating the iterative channel estimation by using LS (Least square) channel estimation and MMSE (Minimum mean square) channel estimation which is present in paper [4].

The organization of the paper as follows. In the section 2

We consider base station and it has p number of active users communicating with that station. It is considered as that there are total number of sub carriers as = × subcarriers, where is the number of subcarriers allocated to each user, including both active and guard subcarriers. We are assuming 1 = 0, 1, , 1 and =

=0

, for = . The received signal from base station is given by

= +

we describe the considered OFDMA system model. In

Section 3 we derive the Carrier frequency offset estimation

= 1 +

(1)

=0

by using two methods: Fuscos estimation and Proposed estimation. In Section 4 we provide the iterative channel estimation followed by two methods: LS estimation and MMSE estimation. Numerical results are presented in Section 5 and conclusions are drawn in the final Section.

1. OFDM SYSTEM MODEL

   Where, is an AWGN with variance 2 and is the received signal from the pth user as

   = 2 / , (2) Where, is the transmitted signal from pth user , and

   is the channel response between the pth user and base station. It is assumed that is non-zero only for

   = 0,1, , 1, where is the channel delay spread and is the normalized CFO of the pth user, = 0,1, , 1.

   Fig.1.This is in-phase receiver part of OFDM

   1. Polyphase structure

      The components in the polyphase structure are to

      1 are the components of the filter . We assume each component has length of . The DFT block performs the demodulation at the receiver side of the

      system but, in the polyphase components are often followed by an IDFT. This difference arises, because here we feed the input signal from the bottom of a tapped delay line, i.e., opposite to the general communication system which feed from top to bottom. This arrangement is because, it matches the presentation of an OFDM receiver.

      From the fig.1,

      The × 1 input vector to the DFT at the time instant as shown in Fig. 1 can be written as

   2. The Proposed Estimator

   =1

   In a time-domain CFO estimation algorithm using periodical training sequences was proposed for single user OFDM systems. This may not be extended to multiuser

   Where

   [] =

   ()[], (3)

   case, where signals from users have been received with different CFOs at BS which cannot be easily separated in the time domain. To overcomethis problem, we taken a frequency- domain CFO estimator for OFDM using

   ()[] = [ + ][ ] = + + [ + ] [ ],

   (4)

   And denotes point-wise multiplication of vecors. Furthermorethe output vector of the DFT block is given by

   () = () , (5)

   Where denotes the DFT operation. The output vector

   () is multiplied with a column vector = 01 1 . The equalizer output is then sent to a slicer and decisions on the transmitted data symbols are obtained as shown in fig. 1. Data from different users are then separated through user separation.

2. CFO Estimation

   A. Fuscos multiuser CFO Estimator

   Previously Fusco et al proposed a joint CFO and timing Offset estimator in the uplink using non-periodical training sequences. The CFO estimator for the th user may be

   periodical training sequences. To reduce this computational complexity of the estimator, used only real training sequences such that the estimator only uses the in-phase parts.in this the filter values taken as non-zero values i.e.,

   .

   The transmitted training signal [] satisfies the following condition approximately,

   [ + ] [] , = 1 , , 1 + 1

   (9)

   Where 1 is the transient in the time domain . By calling (2), we have two × 1 successive overlapped signal vector from the Pth user called ,1 and ,2 such that

   ,2 = 2 ,1, = 0,1, , 1

   (10)

   According × 1 vectors 1and2 are the received vectors at the input of DFT block due to 1 and 2 respectively as

   simplified to

   = 1 + +

   1 =0 =1 ,1 1

   1

   = 1

   2

   + +

   = 1

   (6)

   =0

   2 =0 =1

   ,1

   2

   (11)

   =0

   Where 1 denotes the angle of a complex number and the parameters and are equal to

   1

   If we put some subcarriers as a guard between each pair of adjacent user bands for multiuser case, the overlapping between adjacent users may be neglected. The CFO estimation may be performed after DFT.

   = +

   (7)

   The CFO estimated separately for OFDM in

   =0

   = 1 (12)

   1

   2

   1 2

   = + + 2

   (8)

   =0

   Where 1 and 2 are the DFT outputs of vectors 1 and

   And and are transmitted signals produced by in-phase and quadrature phase of the training symbol on the th subcarrier.to achieve an accurate CFO

   estimation on the th user, 1 = 0 should be

   2 respectively. Using similar approach as in (11), the mean squre error of CFO estimation of the Pth user can be given by

   MSE 2 1

   =0

   ( ) = E = 42 .SNR . k 2

   satisfied.

   The CFO estimation can be done before the DFT. Since,

   H

   (13)

   Cfo is estimated for different users independently, also complexity is improves as the number of user increases.

   Where Hk is the channel frequency response on the kth

   2

   2

   subcarrier and SNR , where 2 is the power of the

   transmitted signal.

3. CHANNEL ESTIMATION

   The channel estimation techniques are used for estimating the channel response at the reference symbols. Linear Interpolation is used to determine the channel at data symbols [6].

   A.LS channel estimation:

   LS (Least Square) channel estimation is the simplest channel estimation method. LS channel estimation is the channel estimator from least squares. Loop impulse response using the LS estimate allows (

   ) minimum, obtained by calculating

   = 1

   (14)

   Gaussian white noise and ICI have large impact on LS algorithm, so the accuracy of the algorithm, so the accuracy of the algorithm is limited [6]. Even though the performance of LS estimation is not perfect, but its implementation complexity is very low in certain error conditions, therefore LS is widely used.

   B.MMSE Estimation:

   Because the accuracy based on LS criterion is not high, in order to improve the accuracy of estimation algorithm, the estimation based on the MMSE (minimum mean square error) criterion is used to improve accuracy with the

   The structure of MMSE estimator is more complicated than LS, but it is superior to LS algorithm on SNR.

4. RESULTS

In this paper we considered as =256 and number of users is P=4. So total number of subcarriers is 64. The channel delay 0, 0.31,0.71,0.09,1.73 and 2.51 microseconds [1]. The value of is in between [0.5 0.5]. The MSE of the proposed estimator and Fuscos multiuser CFO estimator are shown in Fig.2.

1

10

Proposed Est. Fuscos Est.

0

10

-1

MSE

10

-2

10

-3

10

-4

10

0 5 10 15 20 25 30 35 40

SNR, dB

Fig.2. MSE of the proposed estimator and Fuscos multiuser CFO estimator.

The MSE for the proposed estimator is has better performance than the Fuscos estimator.

1

information of channel estimation. 10

The algorithm in the frequency domain is defined as the

formula: 100

= 1

BER

(15) 10-1

Proposed Est. Fuscos Est.

Where,

-2

10

=

-3

10

= + 2

2is the variance of additive Gaussian noise, =

-4

10

0 5 10 15 20 25 30 35 40

Eb/No, dB

is the autocorrelation matrix about channel response [6]. Therefore, the estimation algorithm based on channel response MMSE in frequency domain can be expressed as the formula

= + 2 1 1

Fig.3.BER using proposed estimator and Fuscos multiuser CFO estimator.

In the above Fig.3. Shows that the Bit Error Rate for both estimators, for different values. We see that the BER of the

(16)

proposed estimator has the better performance than the other estimator.

1

0.8

0.6

Amplitude

0.4

0.2

0

-0.2

OFDM Signal

The Fig.4 &Fig.5 shows the performance of the LS channel estimation and MMSE channel estimation scheme. The performance of the iterative LS estimation could near the MMSE estimation.The simulationresult shows that through the proposed iterative channel estimation method, dramatic performance improvements can beachieved with the LS estimation and that the advantage of low implementation complexity makes it possible widely used inthe practical OFDM system.

-0.4

0 200 400 600 800 1000 1200

Time

Fig.3. The OFDM signal

V. CONCLUSION

In this paper we presented the Carrier Frequency Offset estimation by using two estimators, in that proposed estimator has better performance. In channel estimation also two estimated two methods: LS channel estimation

This is the signal we took for the channel estimation. By

using this OFDM signal, we perform channel estimation by using LS and MMSE channel estimation techniques.

2 LS estimation scheme

10

1

10

Bit Error rate —>

0

10

-1

10

-2

10

-3

10

0 10 20 30 40 50

SNR(dB) —>

Fig.5.LS channel estimation scheme

1 MMSE estimation scheme 10

0

10

Bit Error rate —>

-1

10

-2

10

-3

10

-4

10

0 10 20 30 40 50

SNR(dB) —>

scheme and MMSE channel estimation scheme. MMSE channel estimation scheme is the good scheme compared to the LS scheme.

REFERENCES:

1. Hamid Saeedi-Sourck ,Yan Wu ,J.W.M. Bergmans, Saeed Sadri , Behrouz Farhang-Boroujeny Low complexity carrier frequency offset estimatation for multiuser offset qam filter bank multi carrier systems uplink IEEE Commun. Mag., 2012, pp. 978-1- 4673-0990-5 pages,12.

2. T.Fusco,A.Petrella andM.Tanda,Data- aidedtimedomainsynchronizationforfilter- bankmulticarriersystems,Proc.of EUSIPCO2008,Lausanne,Switzerland,August 25-29,2008.

3. B.M.Morelli,C.C.J.Kuo,andM.N.Pun, Synchronization techniquesfororthogonalfrequencydivisionmultipleaccess(OFDMA

   ):Atutorialreveiew,Proc.oftheIEEE,vol.95,no.7,pp.1394- 1427,July2007.

4. T. Fusco, A. Petrella and M. Tanda,Data-aided time domain synchronization for filter-bank multicarrier systems, Proc.ofEUSIPCO 2008, Lausanne, Switzerland, August 25-29, 2008.

5. T. Fusco, A. Petrella, and M. Tanda, Data-aided symbol timing and cfo synchronization for filter-bankmulticarrier systems, IEEE Trans. on Wireless Commun., vol. 8, no. 5, pp.2705-2715, 2009.

6. Ting Peng,XizhongLou,JiaXu,QianZhou,XiaochenLi,Yanmin Chen,An improved Iterative Channel Estimation based LTE Downlink, 2012 9th International Conference on Fuzzy Systems andKnowledgeDiscovery(FSKD2012).

7. H. Saeedi-Sourck,Y. Wu, J.W.M. Bergmans, S.Sadri,B.Farhang- BoroujenyComplexity andperformce comparisonoffilterbankmulticarrierandOFDMinuplinkof multicarriermultipleaccessnetworks,IEEETrans.onSignalProcess.,v ol.59,no.4,pp.1907-1912,April2011.

8. B.LeFloch,M.Alard,C.Berrou,Codedorthogonalfre- quencydivisionmultiplex,Proc.oftheIEEE,vol.83,no.6,pp.982- 996,1995.

Fig.5.MMSE channel estimation scheme