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Capacity enhancement of MC-CDMA system through duobinary coded pilot based receiver phase rotated conjugate cancellation scheme.

INTRODUCTION

Multi-carrier code division multiple access (MC-CDMA) scheme, allows many users to access the channel simultaneously by modulating and spreading their input data signals across the frequency domain using different spreading codes. MC-CDMA system combines the robustness to multipath fading accomplished by orthogonal frequency division multiplexing (OFDM) with the enhanced frequency diversity and narrowband interference rejection capability that can be achieved by code division multiple access (CDMA). Due to its high data rate and easy implementation using Fast Fourier transform (FFT); MC-CDMA has been applied widely in multi-user wireless communication systems. The principle of OFDM is dividing the high rate data stream into several low rate substreams. These substreams are transmitted over different subcarriers using different spreading codes. MC-CDMA systems are more sensitive to carrier frequency offset (CFO) than single carrier modulation schemes. CFO comes from Doppler shift and imperfections of transmitter and receiver oscillators. Vivek K.Dwivedi and Singh (2009) stated that the frequency offset destroys orthogonality among the subcarriers and introduces intercarrier interference(ICI). Hence it is necessary to reduce the ICI power and to improve the carrier power to interference ratio (CIR) at the receiver side.

The basic principle of ICI self-cancellation is that the difference between ICI coefficients of two consecutive subcarriers is normally very small compared to the individual coefficients. Thus the ICI generated between the two subcarriers cancel each other. Self cancellation technique reduces the spectral efficiency as the same symbol occupies two subcarriers stated by Mohamed khedr et al (2011). To mitigate the effect of ICI and to overcome the transmitter complexity of MC-CDMA system, duobinary coded receiver phase rotated conjugate cancellation (RPRCC) with pilot based CFO estimation is proposed in this work. The main aim of this work is to suppress the effect of ICI without reducing the system spectral efficiency. The phase rotation at the receiver is done by estimating the CFO at the receiver. The word 'duo' means to double the transmission capacity of the system. But the error propagation makes it unsuitable for efficient communication. In order to avoid the error propagation a precoder (differential encoder) is used with duobinary coder. Due to the artificial phase rotation at the receiver, RPRCC technique outperforms the conventional CC technique in both low and high frequency offset conditions. CFO estimation is performed at the beginning of each frame using a well-designed training sequence. Pilot aided CFO estimation is superior to blind CFO estimation under varying noise power conditions. The blind technique estimates the frequency offset by analyzing the signal at the receiver. Pilot symbols are transmitted along with the data symbols but are known to the receiver. Pilots are mostly used for synchronization and estimation of channel and CFO. Due to the precise CFO estimation, Pilot based RPRCC (PRPRCC) technique reduces the estimation error. Simulation results show that the proposed pilot based RPRCC technique outperforms the conventional CC technique in both low and high frequency offset conditions.

Mc-Cdma System with Duobinary Coded Receiver Phase Rotated Conjugate Cancellation Scheme:

Shinsuke Hara and Ramjee Prasad(1997) stated that the basic principle of MC-CDMA systemis to spread the [m.sup.th] user (1 [less than or equal to] m [less than or equal to] M, where Mis the maximum number of users) binary data sequence [b.sup.m.sub.k] using different spreading sequence [C.sup.m.sub.k](0 [less than or equal to] k [less than or equal to] N - 1, where N is the number of subcarriers). An orthogonal code such as Walsh-Hadamard code is used to achieve minimum multiple access interference (MAI) in the fading channel. The basic principle of duobinary coder is adding an intersymbol interference (ISI) to the transmitted signal in a controlled manner and compensating its effect at the receiver. The signaling rate of duobinary coding is twice the transmission bandwidth of the channel. Char-Dirchung (2006) stated that the duobinary coding doubles the transmission capacityof the system with better error rate performance. Duobinary coding technique involves a correlation span of two binary digits ([a.sub.k], [a.sub.k - l]). This is achieved by adding the input binary digits spaced [T.sub.b] seconds apart, where [T.sub.b] is the bit duration. To eliminate the possibility of error propagation, a precoder is used prior to the duobinary coder. The precoder output is given by

[d.sub.k] = [b.sub.k] [direct sum] [d.sub.k - l] (1)

where [b.sub.k] is the input binary sequence.

The sequence [d.sub.k] is applied to the duobinary conversion filter. The filtered output is given by

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (2)

Then the duobinary coder output is spread by the Walsh Hadamard spreading sequence [C.sub.k]. The signal after spreading is given by

[X.sub.k] = [m.sub.k] x [C.sub.k] (3)

The pilot based CFO estimation is employed by inserting the pre-defined data in every MC-CDMA symbol in order to track the variations of channel and CFO. The MC-CDMA signal at the IFFT outputcan be expressed as

[x.sub.n] = [1/N] [(N - 1).summation over (k = 0)][X.sub.k]exp(j2[pi]kn/N) = [1/N] [(N - 1).summation over (k = 0)][m.sub.k][C.sub.k]exp(j2[pi]kn/N) (4)

The simplified block diagram of MC-CDMA system with duobinary coded pilot based receiver phase rotated conjugate ICI cancellation is shown in Fig.1.Chin-Liang et al. (2010) and Yeh H.G et al. (2007) stated that, in RPRCC technique, the transmitter employs the conventional CC algorithm. The transmitted signals at two transmission paths are expressed as

[y.sup.(1).sub.n] = [x.sub.n][y.sup.(2).sub.n] = [([x.sub.n]).sup.*]

Both the signals are time multiplexed (MUX) and transmitted through the AWGN channel. At the receiver, the signal at the first path in the presence of frequency offset is given by

[r.sup.(1).sub.n] = [x.sub.n][e.sup.j2[pi]n[epsilon]/N] + [W.sub.1] (5)

where n = 0, 1, ..., N-1 .The signal at the second path is given by

[r.sup.(2).sub.n] = [x.sup.*.sub.n][e.sup.j2[pi]n[epsilon]/N] + [W.sub.2] (6)

where [w.sub.1] and [w.sub.2] are additive white Gaussian noises (AWGN).

The pilot tones are extracted from the FFT output and it is given for CFO estimation. The phase rotation at the receiver is determined from the estimated CFO value.

The FFT output for first path is expressed as

[R.sup.(1).sub.p] = [(N - 1).summation over (n = 0)][r.sub.n][e.sup.-j2[pi]nP/N] (7)

The first path signal with the phase rotation -[phi] is expressed as

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

where I(p - k [+ or -] [epsilon]) = [1/N][(N - 1).summation over (n = 0)][e.sup.[+ or -] j2[pi]n(p - k [+ or -] [epsilon])/N]

The conjugate of second path signal with the phase rotation [phi] is given by

[R.sup.(2).sub.p] = [(N - 1).summation over (n = 0)][X.sub.k]([e.sup.j[phi]]I(p - k + [epsilon])) + [W.sub.2] (8)

where [W.sub.1] and [W.sub.2] are FFT of [w.sub.1] and [w.sub.2]. The combined output of both the paths becomes

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (9)

The decision rule used by duobinary decoder after despreading of FFT signal [y.sub.p] is

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (10)

The received signal [s.sub.k] can be expressed as the sum of desired signal ([r.sub.k]) and interference signal ([I.sub.k]).

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (11)

The first term in Eq.11 represents the desired information and second term represents the sum of interference from [k.sup.th] subcarrier to [p.sup.th] subcarrier. Third term is the AWGN signal. The CIR of RPRCC is expressed as

[CIR.sub.RPRCC] = [[absolute value of [e.sup.j[phi]]I([epsilon]) + [e.sup.-j[phi]]I(-[epsilon])].sup.2]/[(N - 1).summation over (k = 1)][[absolute value of [e.sup.j[phi]]I(k + [epsilon]) + [e.sup.-j[phi]]I(-[epsilon])].sup.2] (12)

The optimum phase rotation for a given frequency offset [epsilon] can be obtained by performing the pilot based CFO estimation at the receiver. Khedr and Mohammed essam(2010) reported that the CIR performance and the bandwidth efficiency can be improved by duobinary coding technique. The average carrier power E[[[absolute value of [r.sub.k]].sup.2]] and average interference power E[[[absolute value of [I.sub.k]].sup.2]] should be calculated separately to obtain the CIR in the proposed method. The desired signal for RPRCC with duobinary coding scheme is given as

[r.sub.k] = exp(j[phi])[m.sub.k][C.sub.k]I([epsilon]) + exp(-j[phi])[m.sub.k][C.sub.k]I(-[epsilon]) (13)

The interference signal is given as

[I.sub.k] = [(N - 1).summation over (k = 1, k [not equal to] p)]exp(j[phi])[m.sub.k][C.sub.k]I(p - k + [epsilon]) + [(N - 1).summation over (k = 1, k [not equal to] p)]exp(-j[phi])[m.sub.k][C.sub.k]I(p - k - [epsilon]) (14)

where I(p - k [+ or -] [epsilon]) = sin([phi](p - k [+ or -] [epsilon])/Nsin([pi](p - k [+ or -] [epsilon])/N) x exp(j[pi](p - k [+ or -] [epsilon])(1 - N)/N)

The CIR of the proposed scheme is derived after calculating the average carrier power and ICI power at the receiver.

The CIR of RPRCC scheme with duobinary coding scheme is given by

CIR = [[absolute value of exp(j[phi])I([epsilon]) + exp(-j[phi])I(-[epsilon])].sup.2]/[[(N - 1).summation over (k = 1)] [[absolute value of exp(j[phi])I(k + [epsilon]) + exp(-j[phi])I(k - [epsilon])].sup.2] - [S.sub.I]] (15)

Where

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

Pilot based cfo estimation and receiver phase rotation:

To improve the accuracy of detection process and reducing the ICI between the subcarriers, pilot based RPRCC technique is proposed. To achieve the perfect CFO estimation under varying noise power conditions Lee et. al (2012) stated that the data aided or training based algorithm is highly preferred than blind estimation algorithms. Mattera (2013) reported that in blind CFO estimation, the frequency offset is estimated by measuring the correlation between two MC-CDMA symbols at the receiver. For high Doppler Effect, the blind estimation cannot estimate the CFO precisely. Kilbom Lee and Inkyu Lee (2013) and Promsuwanna. N et al. (2013) stated that to estimate the CFO and to reduce the ICI, pilots or predefined sequences are located in the subcarriers of every transmitted symbol. In the proposed method, complex pilot tone and its conjugate is placed in the adjacent subcarriers of every symbol. The pilot arrangement for the proposed scheme is shown in Fig.2. In this, 'P' denotes pilot tone, '[P.sup.*]' denotes conjugate of P. 'D'denotes data carrier. At the receiver, the pilot tones in each symbol are extracted and the two adjacent pilot tones are clustered.

The FFT output of [P.sup.th] demodulated pilot tone of [i.sup.th] MC-CDMA symbol is given by

[R.sub.i, P] = [(N - 1).summation over (n = 0)][r.sub.i, n][e.sup.-j2[pi]nP/N] = [(N - 1).summation over (n = 0)][(N - 1).summation over (K = 0)][X.sub.i, K][e.sup.j2[pi]n(K + [epsilon] - P)/N]

Where [r.sub.i, n] is the received [i.sup.th] MC-CDMA symbol and [X.sub.i, K] is the spread signal corresponding to Kth chip of ith symbol. The FFT output of [(P + 1).sup.th] pilot tone of ith MC-CDMA symbol is given by

[R.sub.i, P + 1] = [(N - 1).summation over (n = 0)][r.sub.i, n][e.sup.-j2[pi]n(P + 1)/N]

The clustered pilot tones at Pth and [(P + 1).sup.th] subcarrier of ith MC-CDMA symbol is given by

[Y.sub.i] = [R.sub.i, P] + [R.sub.i, P + 1] (16)

The CFO is estimated by measuring the phase shift of pilot tones on two successive MC-CDMA symbols as specified by Michele Morelli and Marco Moretti(2013). To determine the relative frequency offset [??], maximum likelihood (ML) estimation algorithm is applied. In ML estimation algorithm, the same data frame is transmitted twice and the two received signals are compared at every frequency as stated by Rahimi and Champagne (2014). The two path signals are same when the frequency offset is zero. When there is a drift in the carrier frequency, the two received signals are not equal. The CFO estimation is given by

[??] = [1/2[pi]][tan.sup.-1]{([N.summation over (k = -N)]Im[[Y.sub.i + 1](k)[Y.sup.*.sub.i](k)])/([N.summation over (1 = -N)]Re[[Y.sub.i + 1](k)[Y.sup.*.sub.i](k)])}

where [Y.sub.i] and [Y.sub.i + 1] is the clustered pilot tones corresponding to two MC-CDMA symbols. The optimum phase rotation at the receiver is given by

[phi] = 2*pi*[??] (18)

The estimated CFO incorporates the correlation between both the path signals. In pilot based estimation algorithm, the estimated value of CFO is very much closer to the actual value in the varying SNR conditions.

RESULTS AND DISCUSSIONS

The Simulation parameters are listed in Table 1.

Figure 3 illustrates the comparison of frequency offset estimation of pilot based algorithm and blind estimation algorithm at ([E.sub.b]/[N.sub.o]) of 5dB under different normalized frequency offset values. The graph clearly shows that in the proposed method, the estimated CFO values are very much closer to actual offset values. It is mainly due to the fact that in blind technique the CFO estimation is based on the analysis of signal at the receiver. Figure 4 compares the BER performance of RPRCC with pilot CFO estimation with blind CFO estimation and normal MC-CDMA system. The graph shows that under varying noise power conditions, the blind CFO estimation does not estimate the CFO values precisely.

In Fig.4 the analysis is carried out with the normalized frequency offset of 0.2, the number of data symbols is 5200 and the modulation is QPSK. The graphs show that the BER of [10.sup.-3] is achieved at energy/noise spectral density ([E.sub.b]/[N.sub.o]) of around 8dB in the proposed method and at 8.5dB in the RPRCC with blind CFO estimation. The same BER performance is achieved at 10 dB in normal MC-CDMA system. The graph shows that, the Pilot aided CFO estimation is superior to the blind CFO estimation under high noise power condition. The performance of ICI reduction by the proposed scheme is shown in Figure 5. As seen in Fig. 5, the Pilot based RPRCC with duobinary coding scheme provides less ICI power than the conventional RPRCC and normal MC-CDMA for any value of [epsilon]. In duobinary coding the correlation between adjacent symbols are modulated on each subcarrier. From the CIR expressions, the ICI power of RPRCC with duobinary coding is less compared to other schemes.

Figure 6 compares the BER performance of the proposed method with transmitter PRCC (TPRCC), CC and normal MC-CDMA in both AWGN and Rayleigh fading channel. In Fig.6 the analysis is carried out with the normalized frequency offset of 0.2, the number of data symbols is 5200 and the modulation is QPSK. The graphs show that for the same number of data symbols, the BER of [10.sup.-3] is achieved at energy/noise spectral density ([E.sub.b]/[N.sub.o]) of around 8.5dB in the proposed method and at 9dB in TPRCC and around 10dB in conventional CC method. Unlike TPRCC scheme, the proposed method reduces the complexity of the transmitter by providing the phase rotation at the receiver. The curves also show that the BER of the proposed method is increased due to the amplitude and phase distortion caused by frequency selective Rayleigh fading channel, as stated by Sheetal Patil and Bhosle (2011).

The result shows that to achieve the BER of around [10.sup.-3], the proposed method needs 8.5dB [E.sub.b]/[N.sub.o] in AWGN channel and to achieve the same performance in Rayleigh fading channel the system needs 15dB.

Figure 7 illustrates the BER comparison of the proposed technique for 5, 10, 15 and 20 users at [epsilon] = 0.2. Results show that the simultaneous users experience little difference in their BER performance. This is due to the fact that when the number of simultaneous users is increased, the BER performance is gradually degraded. The graph shows that the BER of [10.sup.-3] is achieved at [E.sub.b]/[N.sub.o] of 12dB for 5 users and at 14.5dB for 10 simultaneous users accessing the channel.

Conclusion:

The Pilot based RPRCC technique with duobinary coding is proposed in this paper. The performance of the proposed method is compared with RPRCC using blind CFO estimation and RPRCC without duobinary coding. The simulation results show that the BER and ICI reduction performance of the proposed method outperforms the conventional methods in both low and high frequency offset conditions. Due to the phase rotation at the receiver side, the proposed technique reduces the transmitter complexity. It increases the capacity of MC-CDMA system by allowing many users to get the services simultaneously without much degradation in the performance. The pilot based RPRCC technique improves the detection accuracy and reduce the ICI which in turn reduces the MAI. MC-CDMA with duobinary coding gives effective utilization of bandwidth. The precise CFO estimation and ICI compression makes the proposed RPRCC technique is the best choice to meet the requirements of future mobile radio communication systems.

ARTICLE INFO

Article history:

Received 3 September 2014

Received in revised form 30 October 2014

Accepted 4 November 2014

REFERENCES

Char-Dirchung, 2006. Correlatively Coded OFDM.IEEE Transactions on Wireless Communications, 5(8): 2044-2048.

Chin-Liang Wang and Yu-Chih Huang, 2010. Intercarrier interference cancellation using general phase rotated conjugate transmission for OFDM Systems. IEEE Transactions on Communications, 58(3): 812-819.

Khedr, Mohammed essam, 2010. Frequency offset cancellation in OFDM systems using (1-D-D2) correlative codes. Advanced Communication Technology International Conference, 1: 859-863.

Kilbom Lee and Inkyu Lee, 2013. Robust Pilot Designs for consistent Frequency Offset Estimation in OFDM systems. IEEE Transactions on Vehicular Technology, 62(3): 1389-1394

Lee, Kilbom, 2012. Low Complexity Pilot Assisted Carrier Frequency Offset Estimation for OFDMA Uplink Systems. IEEE Transactions on Wireless Communications, 11(8): 2690-2695.

Mattera, D., M. Tanda, 2013. Blind Symbol Timing and CFO Estimation for OFDM/OQAM Systems, IEEE Transactions on Wireless Communications, 12(1): 268-277

Michele Morelli and Marco Moretti, 2013. Joint maximum likelihood estimation of CFO, noise power and SNR in OFDM systems. IEEE Wireless Communication Letters, 2(1): 42-45

Mohamed khedr, 2011. Auto Intercarrier frequency offset rectification using conjugate cancellation in Multi-carrier CDMA broadband communications. International Journal on Computer and Electrical Engineering, 3(1): 1793-8163.

Promsuwanna, N., 2013. A Novel Pilot Scheme for Frequency Offset and Channel Estimation in 2 x 2 MIMO-OFDM. World Academy of Science, Engineering and Technology, 74: 539-543.

Rahimi, S., B. Champagne, 2014. Joint Channel and Frequency Offset Estimation for Oversampled Perfect Reconstruction Filter Bank Transceivers. IEEE Transactions on Communications, 62(6): 2009-2021

Sheetal Patil and U.V. Bhosle, 2011. Simulation of multicarrier CDMA system in Rayleigh channel. Computational Intelligence and Information technology communications in computer and information, 250: 313-320.

Shinsuke Hara and Ramjee Prasad, 1997. Overview of Multicarrier CDMA. IEEE Communications Magazine, 35(12): 126-133.

Vivek, K. Dwivedi and G. Singh, 2009. A novel bit error rate analysis and improved ICI reduction method in OFDM communication systems. Journal of Infrared, millimeter and Terahertz Waves, 30(11): 1170-1180 .

Yeh, H.G., Y.K. Chang and B. Hassibi, 2007. A scheme for cancelling intercarrier interference using conjugate transmission in multicarrier communication systems. IEEE Transactions on Wireless Communications, 6(1): 3-7.

(1) S. Chitra and (2) N. Kumaratharan

(1) Department of Electronics and Communication Engineering, Rajalakshmi Engineering College, Affiliated to Anna University, Chennai, India

(2) Professor, Department of Information Technology, Sri Venkateswara College of Engineering, Affiliated to Anna University, Chennai, India.

Corresponding Author: N. Kumaratharan, Professor, Department of Information Technology, Sri Venkateswara College of Engineering, Affiliated to Anna University, Chennai, India. E-mail: kumaratharan@rediffmail.com

Table 1: Simulation parameters.

Parameter                      Value

Number of symbols              5200
Number of pilot tones            4
Number of users             5,10,15,20
Spreading code            Walsh Hadamard
Modulation                   BPSK,QPSK
Frequency offset        0.1,0.2,0.3,0.4,0.5
Channel                   AWGN, Rayleigh
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Author:Chitra, S.; Kumaratharan, N.
Publication:Advances in Natural and Applied Sciences
Article Type:Report
Geographic Code:9INDI
Date:Oct 15, 2014
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