 Open Access
 Total Downloads : 646
 Authors : Mr. Atul Singh Kushwah, Mr. Mayank Mittal
 Paper ID : IJERTV3IS10881
 Volume & Issue : Volume 03, Issue 01 (January 2014)
 Published (First Online): 27012014
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Performance Estimation of 2*2 MIMOMCCDMA Using Convolution Code in Different Modulation Technique
Mr. Atul Singh Kushwah Mr. Mayank Mittal
Mtech Digital Communication Asst. Prof.
Patel College of Science and Technology Patel College of Science and Technology Indore(M.P) Indore(M.P)
ABSTRACT
In this paper we estimate the performance of 2*2 MIMOMCCDMA system using convolution code in MATLAB which highly optimizes 3G and 4G wireless communication system by reducing BER. CDMA (Code Division for Multiple Access) is a multiuser system or spread spectrum system for which spreading of sequence is done by PN(Pseudorandom Noise) sequence generator at the transmitter and convolution encoding scheme is used in encoder of CDMA as FEC (Forward Error Correction) code to reduce BER (Bit Error Rate). Now this system is combined with OFDM (Orthogonal Frequency Division Multiplexing) which is multicarrier system in which single broadband frequency selective carrier is converted into parallel narrowband flat fading multiple sub carriers to optimize the performance of system. This combination of system called MCCDMA (multicarrier Code Division Multiple Access) system. Now this system further improved by combination of 2*2 MIMO (Multiple Input Multiple Output) system which utilizes ZF (Zero Forcing) decoder at the receiver to reduce BER and also Â½ rate convolutionally encoded Alamouti STBC (Space Time Block Code) block code as transmit diversity of MIMO for multiple transmission of data through multiple transmit antenna. By using MIMOOFDM [3] ZF equalizer at transmitter is not required because MIMO OFDM combination remove the probability of ISI at the transmitter. Resultant system with the combination of OFDMCDMA and MIMOOFDM forms MIMOMCCDMA which is highly optimized system for 3G and 4G wireless communication system. Now after forming MIMO MCCDMA using convolution code in MATLAB
[1] we analyze system performance in different modulation schemes like, QPSK, 8PSK, 8QAM, 16QAM, 32QAM and 64QAM in Rayleigh fading channel.Keywords: OFDM,CDMA,MIMO,MIMO
MCCDMA 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 minimum error rate. MCCDMA is combination of CDMA and OFDM. CDMA is multiple access system and OFDM is multiple access system in frequency selective channel that is in OFDM, the frequency selective channel is converted into a group of N narrowband flatfading channel, one channel across each sub carriers. The combination of both the technique results improved efficiency of the wireless communication system which results high speed and low probability of error.
This experience is further improved by combination of MIMO with MCCDMA by which throughput of the wireless system is increased. MIMO is multiple antenna system in which multiple receive diversity and multiple transmit diversity is used for synchronization of system to reduce ISI. To minimize mean square error ZF equalizer is used. And for transmit diversity half rate convolutionally encoded Alamouti STBC code is used. And finally combined MIMOMCCDMA
[5] is formed by all above operations using MATLAB and this MIMOMCCDMA is then encoded using convolution code as encoder in MC CDMA. This MIMOMCCDMA usingconvolution code then analyzed using QPSK, 8 QAM and 64QAM modulation techniques.

OFDM System Model
Figure 1 represents the transceiver block diagram for the OFDM system[8]. In transmitter, the information bits are modulated by P symbols. Hence the symbol vector can be represented as,
d = [d1 d2 _ _ _ dP ]T CP*1 ..(1) where C designate a set of complex numbers. The symbols are serialtoparallel (S/P) converted and then mapped into Ns parallel orthogonal sub carriers and transformed into the time domain by the inverse Fast Fourier transform (IFFT). Then the output of samples of the IFFT are parallelto serial (P/S) converted to form the baseband signal then cyclic prefix (CP) is added before transmission over the multipath radio channel.
Cyclic prefix is added to the transmitted signal in order to reduce ISI which arises between OFDM symbols from large multipath delay spreads.
Cyclic Prefix is nothing but a cyclically extended
any positive real number. This shows the realistic channel environments.
Figure 1: OFDM system block diagram.
On receiving the signal, the CP is removed and the Fast Fourier transform of Ns size is performed.
The estimates of the transmitted symbol are obtained by performing zero forcing (ZF) equalization on each subcarrier and which is given by
yi = H 1r
i i
guard interval, where each OFDM symbol is
= H 1h d + H 1n = d (5)
i i i i i i + i
precede by a periodic extension. The total symbol duration is
Ttotal = Tg + Td(2) where Tg is the guard interval and Td is the symbol duration. If the guard interval is longer than the multipath delay then ISI can be avoided. Cyclic prefix converts the linear convolution into a cyclic convolution. That is the multipath fading effect on the transmitted symbols is reduced to an elementwise multiplication between the transmitted data constellations d with the channel frequency response H.
The frequency response of the channel is known by the Fourier Transform (FT) of the channel impulse response h. As a result, the orthogonality of the Subcarriers is improved. The channel is considered to be frequency selective fading corrupted by additive white Gaussian noise (AWGN).
The received signal model after the FFT at ith subcarrier, can be characterised as
ri = Hidi + ni ….(3) where Hi represents fading coefficient of the channel and ni noise signal at the ith subcarrier.
Frequency response at the ith subcarrier (i =1, 2,
..Ns) is calculated by
Hi ..(4)
where r1 is the path arrival time normalized to the OFDM subcarrier spacing, such that r1Td is the delay and 1/Td is the OFDM subcarrier spacing. Equation (4) represents the path arrival time can be
where H 1 denotes the inverse of Hi. In general, equalization techniques are used to reduce ISI created in frequency selective channels.
i

Direct Sequence Code Division Multiple Access (DSCDMA)
Direct sequence code division multiple access (DSCDMA) employ direct sequence spread spectrum (DSSS) technique to permit multiple users sharing the same bandwidth at the same time. DSSS spreads the arriving data stream with a pseudorandom (PN) code over a bandwidth much larger than the signal bandwidth. So, the transmit power remains constant and the bandwidth of the spreading signal is large, the power spectral density of the transmitted signal below the noise power spectral density.
The user to detect own transmitted data to ensure secure communications. The transceiver of a DS CDMA system for a single user is shown in Fig.2. Consider P no. of BPSK modulated symbols with a symbol rate of Rd =1/Td represented by
d = [d1 d2 . dP]T CP*1 .(6)
In transmitter, the symbols are spreaded by wideband PN spreading code to figure the transmitted baseband signal as
x = dc ..(7)
where c = [c1 c2 . cG] C1*G is the PN spreading sequence with chip rate Rc = 1/Tc and G is the length of the spreading code. The bandwidth
of the spreading sequence, Bc 1/Tc, is approximately Td/Tc times greater than the bandwidthof the input symbols Bd. The spreading factor of the system is given by SF=Bc/Bd = Td/Tc which is equal to G. The baseband signal now transmitted over the AWGN channel and the received baseband signal after demodulation is given by
r = dc + n ….(8)
where n is the noise vector. The symbol decision statistic is generated by despreading the received signal with the PN spreading sequence. This is given by
D= rcT ….(9)
Figure 2: Block diagram of DSCDMA system.
1.3 Multi Carrier Code Division Multiple Access (MCCDMA)
MCCDMA [2,6,7] is a combination of system of OFDM and CDMA technologies. This technique allows the multiple users to access the wireless channel simultaneously by modulating and spreading their input data signals in frequency domain using different spreading sequences. MC CDMA combines the multipath fading of OFDM with the multiuser access of CDMA.

3.1 System Model of MCCDMA
The MCCDMA [4,10] system model for Nu users is shown in Figure 3. The message data are grouped into Nu frames and then each frame is modulated to P symbols. So the symbol matrix for user nu (nu = 1, 2,..Nu) can be indicated as dnu =[dnu,1 dnu,2 dnu,P]T CP*1. The symbols of each user are converted firstly serialtoparallel then spread with the corresponding specific user spreading sequence to form the chiplevel transmit matrix i.e.
in which C is the spreading code chip alphabet and G is the length of the spreading sequence. Each user is allocated by a distinct spreading code for orthogonality between the users to differentiate. The chips of the frames of each users are then combined and all parallel data sequences are mapped into Ns = P*G subcarriers and transformed into the time domain by the IFFT. The subcarrier is related to the pth symbol (p = 1, 2,., P) and the gth chip (g = 1,2,…,G) by
i(p, g) = (p – 1)G + g. (12)
It must be noted that the subcarrier index i, symbol index p, and chip index g are interconnected together by (12). Therefore the corresponding symbol and chip indexes for ith subcarrier are
p(i) = (i – 1)modG + 1 (13) and
g(i) +1(14) respectively where denotes the largest integer that is lesser than a. The transmitted ith multiplexed chip of all users can be determined as xi = nu,i= nu,g(i)dnu,p(i)(15)
Figure 3: Multiuser MCCDMA system.
The output from IFFT is added with CP before transmission over the wireless multipath fading channel. The channel is called as quasistatic frequency selective fading corrupted by AWGN with power spectral density of N0. The duration of CP is greater than the maximum delay spread of the channel to avoid ISI.
On receiving the signal, cyclic prefix is removed and the FFT of size Ns is performed. The received signal model after FFT can be characterized by
ri= Hixi + ni .(16)
The estimates of the transmitted chips of different subcarrier can be obtained by performing Zero Forcing equalization on each subcarrier as shown by
i
i
i
i i
i
i
i
i
yi=H 1r =H 1H x +H 1n =x + .(17)
snu =[snu,1 snu,2.. snu,PG]
= dnu cnu C1*PG ..(10) where denotes the Kronecker product and the signature sequence of user nu is expressed as
cnu =[cnu,1 cnu,2 cnu,G] C1*G ..(11)
The chip estimates are then despreaded by the desired user's spreading sequence can be expressed as
znu,p = nu,gy(i)=dnu,p+ nu,g i..(18)
The probable pth symbol detection for the nuth user is performed by slicing znu,p using the quantization operation Q(.) with respect to the type of constellation in use
d^nu,p = Q(znu,p) … (19)

Multiple Input Multiple Output (MIMO)
MIMO system is based on multiple transmitting and multiple receiving antennas to achieve very high data rates in rich multipath scattering environments without increasing the transmission bandwidth or the total transmitted power of the system. The pointtopoint MIMO channel of four transmit (Nt = 4) and four receive (Nr = 4) antennas is shown in Figure 4.
Figure 4: 4×4 MIMO channel.
MIMO techniques provide high data rates through spatial multiplexing and increase the spectral efficiency of the system which is rich in scattering environments by providing spatial diversity. The capacity of MIMO system is increases as the number of transmitreceive antenna pairs increases. So due to this it is called spatial multiplexing architectures. The received signal for the MIMO system is given as mathematically
=
CNr*Nt …(20)
The channel model in (20) can be simplified to equation can be represented as
r = Hd + n ….(21)
n
where d denotes the transmitted symbol of dimension Nt, n is the noise vector dimension Nr with zero mean and variance 2 and H indicates
the Nr*Nt complex matrix of channel coefficient gains hi,j from transmit antenna j to receive antenna i.

Spatial Multiplexing
Figure 5 shows the block diagram for a spatial multiplexing (SM) technique with parallel symbol mapping. Spatial multiplexing divides a single bit stream into Nt parallel sub streams which are mapped into symbol streams by appropriate constellation before simultaneous transmission over the wireless channel. The Nt sub streams forms the vertical vector
d = [d1 d2 .. dNt]T CNr*1 ..(22) which contains the mapped symbols. This process illustrates the encoding of the input serial data into a vertical vector which is referred to as vertical encoding. As parallel transmit antennas Nt are used for spatial multiplexing, the transmission rate is Nt times greater than systems with a single transmit antenna.

Linear Detection (Nulling)
Spatial interference can be suppressed by Linear filtering (or nulling) which arises when multiple antennas transmit multiple sub streams simultaneously called coantenna interference (CAI). By nulling, we considered one received desired signal while other symbols are suppressed. This procedure is repeated for each of the received sub streams. For this two different linear filters are used for the purpose of this research and these include the ZF and the minimum mean square error (MMSE) filters. In this paper we are considering on ZF receiver. Provided that the number of transmit antenna should not greater than the number of receive antenna (Nt Nr), their transform matrices are given by
GZF = H+ = (HHH)1HH …(23) GMMSE =[ HHH + N0INt]1 HH ..(24)
respectively, where H+ and HH represent the pseudo inverse and Hermitian matrices of H respectively, and INt stands for the Nt * Nt identity matrix. The decision statistics of the transmitted symbols is given as
y = Gr = Gd + Gn ….(25) where G represents the ZF or MMSE spatial suppression matrix given by (23) or (24) respectively.


Spatial Diversity
Transmitting and receiving multiple copies of the same data streams under independent fading paths using multiple transmit and multiple receive antennas is an alternative approach to spatial
multiplexing to achieve transmit and/or receive diversity. By which detection of signals in deep fades is avoided so spatial diversity increases the system performance. This method is called space time coding (STC) and it is shown in Fig.6.
Figure 5: Spatial multiplexing architecture.
D = …(26)
The transmission matrix is orthogonal i.e. DDH =
=( 2+ 2)I ….(27) The first and second received signals are given by r(1) = pd0 + pd1 + n(1)…. (28) r(2) = pd1* + pd0* + n(2)………..(29)
where p and p denote the channel gain coefficients from transmit antenna 1 and 2 to receive antennas respectively and it is assumed that p and p are constant over two successive symbol periods. In addition, n(1) and n(2) represent the AWGN oise components with zero mean and variance N0. The received signal matrix are as follows
r =
Figure 6: Spacetime coding (STC).
There are two main STC schemes for spatial diversity and these are: (i) spacetime trellis code (STTC) and (ii) spacetime block code (STBC). STBC bring out spatial correlation into the signals transmitted from different antennas, in order to give spatial diversity and coding gain without offering extra bandwidth. However, STTC require trellis decoding which is a high complexity detection process that is exponentially as a function of the transmit antennas and the transmission rate. Here, this work is focused on the STBC, which is explained in the following section.

SpaceTime Block Code (STBC)


A low complexity system that achieves transmit diversity was proposed by Alamouti for 2 transmit antennas. This scheme is noted as STBC and is later generalized to an arbitrary number of antennas. In the Alamouti's transmission scheme, let us consider two symbols d0 and d1 in two consecutive symbol periods transmitted over two successive transmissions. In first transmission, d0 and d1 are transmitted simultaneously at time t from the two transmit antennas. During the second transmission, different symbols d1* and d0* are transmitted at time t + Td where Td denotes the symbol period. So, the transmission matrix is represented by
+ =Hd+n.(30)
Similar to (27), the orthogonal channel matrix H is such that
HHH
=..(31)
The transmitted signal can be separated by pre multiplying the received signal in (30) with HH as given by
y = HH r = d+ HH n
=( d+.(32)
The modified noise is an AWGN with zero mean but with power equal to NoI. Maximum likelihood (ML) symbolbysymbol detection can be used to obtain the estimated data. The above analysis has shown that the Alamouti's STBC scheme achieves a rate of 1 (R=1) also called full rate convolution code as it transmits two symbols in two symbol periods.


MIMOMCCDMA Communication System Model
Communication system model of MIMOMC CDMA using convolution code used in this paper is shown in fig.7.
In this communication system we assuming random input provided by user to system model so this data source is considered as random input source through MATLAB. Now convolution encoding is done as FEC (Forward Error Correction) technique to reduce error probability. Now due to CDMA system spreading of sequence
is done using PN sequence generation so for this spreading of data, spreader is used. Now different modulation scheme is used like QPSK, 8QAM and 64QAM this is shown by modulator block. Previously described system is CDMA system which is already described in section 1.2 Direct Sequence Code Division Multiple Access (DS CDMA) now OFDM transmitter is used which is described in detail on section 1.1.OFDM System Model. Now MIMO encoder half rate convolutionally encoded STBC block code is used which will be described in section 1.4 Multiple Input Multiple Output (MIMO). Above process complete transmitter of MIMOMCCDMA using convolution code as shown in fig.7. Now signal is then transmitted through channel, here channel used is Rayleigh Fading Channel [9]. Now reverse process is done on receiver for recovery of transmitted signal and BER calculation is done for analysis of the system. In MIMO system two transmit antenna and two receive antenna is used.
Fig.7. Communication System Model OF MIMO MCCDMA using convolution code

Simulation Results and Discussion:
Table 1 shows the simulated model parameters of MIMOMCCDMA [1114] using convolution code in QPSK, 8PSK, 8QAM, 16QAM, 32
QAM and 64 QAM modulation technique. Performance analysis of MIMOMCCDMA using convolution code in above described modulation scheme is shown in fig.813.
Fig. 14 shows the comparative analysis of all above modulation schemes in MIMOMCCDMA using convolution code.
Table 2 shows the performance analysis of all modulation schemes in terms of BER and gain.
From table.2 and Fig.14 we can say that QPSK shows high gain (18.28dB) with respect to other modulation schemes and also have very low bit error rate w.r.t other modulation techniques. This is possible by using convolution code as encoding scheme in MIMOMCCDMA.
Table:1. Summary of simulated model parameters.
No. of bits transmitted
by user
1560
No. of transmitting and
receiving antennas
2*2
FEC Encoder
Convolution encoder
Channel Encoder
Â½ rate convolution
encoder
Modulation Schemes
QPSK, 8PSK, 8QAM, 16QAM, 32QAM and
64 QAM
Signal detection scheme
Zero forcing
Channel
Rayleigh Fading
Channel
Signal to Noise Ratio
10dB to 20 dB
CP Length
1280
OFDM Subcarriers
6400
Fig.8. Performance analysis of MIMOMCCDMA using convolution code in QPSK modulation scheme.
Fig.9. Performance analysis of MIMOMCCDMA using convolution code in 8QAM modulation scheme.
Fig.10. Performance analysis of MIMOMCCDMA using convolution code in 64QAM modulation scheme.
Fig.11. Performance analysis of MIMOMCCDMA using convolution code in 32QAM modulation
Fig.12. Performance analysis of MIMOMCCDMA using convolution code in 8PSK modulation
Fig.13. Performance analysis of MIMOMCCDMA using convolution code in 16QAM modulation
Fig.14. Performance analysis of MIMOMCCDMA using convolution code in 8QAM, 16QAM, 32 QAM, 64QAM, 8PSK and QPSK modulation scheme.
Table: 2. Performance analysis at 1dB SNR with respect to 64QAM modulation technique as shown in fig.14:
Modulation
BER at 1dB
Gain w.r.t 64QAM
QPSK
0.004615
18.28 dB
8QAM
0.06128
7.05 dB
8PSK
0.1292
3.812 dB
16QAM
0.1917
2.09 dB
32QAM
0.2749
0.533 dB
64QAM
0.3108
0 dB

Conclusion
Fig. 14 shows the comparative analysis of MIMO MCCDMA using convolution code in QPSK, 8 PSK, 8QAM, 16QAM, 32QAM and 64 QAM
modulation schemes. Table 2 shows the comparative analysis of different modulation schemes by which we can say that as modulation scheme order is lower results increase in BER. This paper aims to reduce bit error rate as reduced effectively in QPSK modulation scheme with gain of 18.28 dB with respect to 64QAM which shows the gain of QPSK is higher as compared to other modulation technique with very low probability of error because errors were removed at 0dB in QPSK.

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