Performance Estimation of 2*1 MIMO-MC-CDMA Using Convolution Code in Different Modulation Technique by Zero-Forcing Detection

Performance Estimation of 2*1 MIMO-MC-CDMA Using Convolution Code in Different Modulation Technique by Zero-Forcing Detection

Mr. Atul Singh Kushwah

Asst. Professor

Indore Institute of Science and Technology-II Indore(M.P)

Abstract – In this paper we estimate the performance of 2*1 MIMO-MC-CDMA system using convolution code in MATLAB which highly optimizes 3G and 4G wireless communication system by reducing BER. MC-CDMA (Multi Carrier Code Division for Multiple Access) is a multi-user and multiple access system which is formed by the combination of OFDM and CDMA and convolution encoding scheme is used in encoder of CDMA as FEC (Forward Error Correction) code to reduce BER (Bit Error Rate). MC-CDMA system is a multi-carrier system in which single broadband frequency selective carrier is converted into parallel narrowband flat fading multiple sub-carriers to optimize the performance of system. Now this system further improved by combination of 2*1 MISO (Multiple Input Single Output) system which utilizes ZF (Zero Forcing) decoder at the receiver to reduce BER and also ½ rate convolutionally encoded Alamouti STBC (Space Time Block Code) block code as transmit diversity of MISO for multiple transmission of data through multiple transmit antenna. Main advantage of using MIMO-MC-CDMA using convolution code is to reduce the complexity of system and to reduce BER with increasing gain. In this we analyze system performance in different modulation schemes like, QPSK, 8-PSK, 8-QAM, 16-QAM, 32-QAM and 64-QAM in Rayleigh fading channel using MATLAB.

Keywords: OFDM,CDMA,MIMO,MIMO-MC-CDMA and

convolution code.

1. INTRODUCTION

   Due to increased demand of high data rate and low probability of error in this paper we utilizes the technique of MIMO, CDMA and OFDM results enhanced technique for minimizing error rate. MC-CDMA is multicarrier and multiple access system which is a combination of OFDM and CDMA. CDMA is multiple access system and OFDM is multiple carrier system in frequency selective channel that is in OFDM, the frequency selective channel is converted into a group of N narrowband flat-fading channel or sub-carriers. The

   combination of both the technique results improved efficiency of the wireless communication system which results high data rate and low probability of error.

   After that in this paper MIMO is combined with MC-CDMA to increase throughput. MIMO is multiple antenna system in which multiple receive diversity and multiple transmit diversity i.e half-rate convolutionally encoded Alamouti STBC code is used for synchronization of system to reduce ISI. To detect signal orthogonality ZF detection scheme is used. And finally combined MIMO-MC-CDMA [5] is formed by all above operations using MATLAB is then encoded using convolution code as FEC encoder. This MIMO-MC-CDMA using convolution code then analyzed in QPSK, 8-PSK, 8- QAM, 16-QAM, 32-QAM and 64-QAM modulation techniques in Rayleigh fading channels.

2. MULTI CARRIER CODE DIVISION MULTIPLE ACCESS (MC-CDMA)

MC-CDMA [2,6,4] is a combination of system of OFDM and CDMA system. This system allows the multiple users to access the wireless channel simultaneously by modulating and spreading their input data signals in frequency domain using PN spreading sequences. MC-CDMA combines the multipath fading of OFDM system with the multi-user access of CDMA system.

Mathematical Model

In present MC-CDMA system we assume that signal detection scheme will be implemented for two transmitting and one was receiving antennas. Each user use single transmitting antenna system. Assume H represent a channel matrix as hij for the channel gain between the ith transmit antenna and the jth receive antenna, j=1 and i=1 and 2. Each user data and the equivalent received signals are represented by a=[a1, a2]T and y=[y1] T respectively, in which ai and yj denote the transmit signal from ith transmitting antenna and the received signal at the jth received antenna respectively. Assume nj denote the white Gaussian noise of variance of n2

at the jth receiving antenna and hi denote the ith column vector of the channel matrix H. Now received signal y for system can be represented by

y=Ha+n= pa1+pa2+n (1) Where, n=[n1,n2]T

As the interference signals from other transmitting antennas are reduced for detecting the desired signal from the target transmitting antenna, the detected desired signal from the transmitting antenna by inverting channel effect from a weight matrix W is

Ã=[ã1 ã2]=Wy. (2)

In Minimum mean square error (MMSE) scheme, the MMSE weight matrix is given by

WMMSE=(HHH+n2I)-1HH (3)

where ()H denotes the Hermitian transpose. Now detected desired signal from the transmitting antenna is represented by

ãMMSE=WMMSEy . (4)

For Zero-Forcing (ZF) scheme, the ZF weight matrix is represented by

WZF=(HHH)-1HH . (5)

and the detected desired signal from the transmitting antenna using the following relation

ãZF=WZFy (6)

It is intended to find the transmitted signal vector by Sphere Decoding (SD) scheme with minimum least ML metric. Suppose yR and yI represents the real and imaginary parts of the received signal y, i.e. yR= Re{y} and yI= Im{y}. Corresponding to that the input signal xi and the channel gain hi from ith transmitting antenna to single receiving antenna can be showed by aiR = Re{ai} and aiI = Im{ai} and hiR = Re{hi} and hiI = Im{hi} respectively. We can say that,

= 1 2 1 2 . (7)

1 2 1 2

=[yR jyI]T (8)

From (9) , the detected desired signal ãSP with its real and imaginary components from the transmitting antenna can be represebted by[6]:

schemes is required. This paper applies maximal ratio combining (MRC) technique using Zero Forcing (ZF) decoder to combine M received signals to resonate on the mostly required desired transmitted signals. The sum of the received SNRs form M different paths is the effectively received SNR of the system with Alamouti STBC of 2*1 diversity. The receiver required to demodulate all M receive signals in case of ZF for a source with

independent signals in the received antennas.

4. MIMO-MC-CDMA COMMUNICATION SYSTEM MODEL USING CONVOLUTION CODE

Communication system model of MIMO-MC-CDMA using convolution is shown in fig.1.

In this communication system we are assuming transmitter sending random sequence to receiver so we are using random sequence generator using MATLAB. Now convolution encoding is done as FEC technique to reduce error probability. Now spreading of sequence is done using PN sequence generator. Now different modulation scheme is used like QPSK, 8-PSK, 8-QAM, 16-QAM, 32-QAM and 64-QAM this

is shown by modulator block. Previously described system is MC-CDMA system which is already described in section 2 Multi-Carrier Code Division Multiple Access (MC-CDMA). Now MIMO encoder half- rate convolutionally encoded Alamoutis STBC block code is used which will be described in section 3 Multiple Input Multiple Output (MIMO). Combination of MIMO and MC-CDMA forms MIMO-MC- CDMA using convolution code as shown in fig.1. Now signal is then transmitted through Rayleigh Fading Channel [3]. Then receiver receive the signal in reverse fashion for the recovery of transmitted signal and BER calculation is done for estimating the system. In MIMO system two transmit antenna and one receive antenna is used.

ãSP=[ã1R

ã2R

ã1I

ã2I]T=[

T ]-1*[ T ] (9)

1. MULTIPLE INPUT MULTIPLE OUTPUT (MIMO)

   MIMO systems use multiple transmit antennas an multiple receive antennas at the receiver, so both transmit and receive diversity schemes are applied to reduce fading resulting from signal variations by wireless channel. It depends on the degree at which the multiple data replicas are faded independently; the system provides diversity gains which represents the difference in SNR at the output of the diversity combiner as compared to that of single branch diversity at certain probability level.

   A MIMO system consisting of N transmit antenna elements equal to two, and of M receive antenna elements equal to one was modeled, accordingly diversity order of 2 can be achieved. For improving the performance combination of the multiple versions of the signals created by different diversity

   Fig.1. Communication System Model OF 2*1 MIMO-MC-CDMA using convolution code

2. SIMULATION RESULTS AND DISCUSSION:

   Table 1 shows the simulated model parameters of MIMO- MC-CDMA [5-8] using convolution code in mentioned different modulation technique.

   Performance estimation of MIMO-MC-CDMA using convolution code is shown in fig.1-7.

   Fig. 8 depicts the comparative estimation of different modulation schemes in MIMO-MC-CDMA using convolution code.

   Table 2 shows the performance estimation of all modulation schemes in terms of gain and BER.

   From table.2 and Fig.8 we can say that QPSK shows high gain (17.86 dB) and very low BER with respect to other modulation schemes. This is possible by using convolution code as FEC encoding scheme in MIMO-MC-CDMA.

   Table:1. Simulated model parameters.

   No. of bits transmitted by user			1560
   FEC Encoder			Convolution encoder
   Channel Encoder			½ rate convolution encoder
   Signal scheme		detection	Zero forcing
   Channel			Rayleigh Fading Channel
   Signal Ratio	to	Noise	-10dB to 20 dB
   CP Length			1280
   OFDM Sub-carriers			6400
   No. of transmitting and receiving antennas			2*1
   Modulation Schemes			QPSK, 8-PSK, 8-QAM, 16-QAM, 32-QAM and 64 QAM

   Fig.2. Performance estimation of MIMO-MC-CDMA using convolution code in QPSK modulation scheme.

   Fig.3. Performance estimation of MIMO-MC-CDMA using convolution code in 8-QAM modulation scheme.

   Fig.4. Performance estimation of MIMO-MC-CDMA using convolution code in 64-QAM modulation scheme.

   Fig.5. Performance estimation of MIMO-MC-CDMA using convolution code in 32-QAM modulation

   Fig.6. Performance estimation of MIMO-MC-CDMA using convolution code in 8-PSK modulation

   Fig.7. Performance estimation of MIMO-MC-CDMA using convolution code in 16-QAM modulation

   Fig.8. Performance estimation of MIMO-MC-CDMA using convolution code in 8-QAM, 16-QAM, 32-QAM, 64-QAM, 8-PSK and QPSK modulation

   scheme.

   Table: 2. Performance estimation at 1dB SNR with respect to 64-QAM modulation technique as shown in fig.8:

   Modulation	BER at -1dB	Gain w.r.t 64- QAM
   QPSK	0.005385	17.86 dB
   8-QAM	0.09949	5.2 dB
   8-PSK	0.1731	2.798 dB
   16-QAM	0.2063	2.03 dB
   32-QAM	0.2872	0.599 dB
   64-QAM	0.3297	0 dB

3. CONCLUSION

   Fig.8 shows the comparative estimation of MIMO-MC- CDMA using convolution code in QPSK, 8-PSK, 8-QAM, 16- QAM, 32-QAM and 64 QAM modulation schemes. Table 2 shows the comparative estimation of different modulation schemes which shows that as modulation scheme order is lower results increase in BER. This paper aims to reduce bit error rate which is found in QPSK modulation scheme with gain of 17.86 dB with respect to 64-QAM which represents that the gain of QPSK is higher as compared to other modulation technique with very low probability of error because errors were removed at 2dB in QPSK.

4. REFERENCES

1. Yong Soo Cho, Jaekwon Kim Yonsei, Won Young Yang and Chung G. Kang, MIMO Wireless Communication with MATLAB, 1st ed., August 2010, Wiley-IEEE Press.

2. Sohag Sarker, Farhana Enam, Md. Golam Rashed, Shaikh Enayet Ullah Performance Estimation of Two-Layer Spreading scheme based FEC encoded MCCDMA wireless communication system under implementation of various signal detection schemes Journal of Emerging Trends in Computing and Information Sciences, VOL. 3, NO. 4, April 2012.

3. A. Sharmila and Srigitha S. Nath Performance of MIMO Multi-Carrier CDMA with BPSK Modulation in Rayleigh Channel International Conference on Computing and Control Engineering (ICCCE 2012), 12 & 13 April, 2012.

4. Manjinder Singh, Karamjeet Kaur BER Performance of MC- CDMA Using Walsh Code with MSK Modulation on AWGN and Rayleigh Channel, International Journal of Engineering Trends and Technology (IJETT) – Volume4 Issue7- July 2011.

5. Karmjeet Singh, Rajbir Kaur Performance Estimation of MIMO Multi-Carrier CDMA with QPSK Modulation in Rayleigh Channel , International Journal of Engineering Research and Applications, Vol. 3, Issue 4, Jul-Aug 2013, pp.2562-2565.

6. Shaikh Enayet Ullah and Md. Mahbubar Rahman BER Performance Estimation of a FEC Encoded Multi-user MIMO MCCDMA Wireless Communication System, International Journal of Hybrid Information Technology, Vol. 4 No. 3, July, 2011.

7. Mousumi Haque, Most. Farjana Sharmin and Shaikh Enayet Ullah, Secured data transmission in a V-Blast encoded MIMO MCCDMA wireless communication system, International

   Journal of Information & Network Security (IJINS), Vol.2, No.3, June 2013, pp. 245~252.

8. Antonis Phasouliotis MULTICARRIER CDMA SYSTEMS WITH MIMO TECHNOLOGY, A thesis submitted to the University of Manchester for the degree of Doctor of Philosophy in the Faculty of Engineering and Physical Sciences, 2010.