CONFIGURATIONS ON WCDMA SYSTEM

PERFORMANCE ANALYSIS ON MODULATION TECHNIQUES IN

WCDMA SYSTEM WITH DIFFERENT CHANNEL CONDITIONS

CONFIGURATIONS ON WCDMA SYSTEM

4.1     Introduction

We begin our research thesis on first reviewing the high speed data rate modulation schemes, DSSS W-CDMA and fading effects on the channels. Then, we develop a generic model of DSSS W-CDMA and is being simulated by MatLab modulation schemes 16-QAM and QPSK. Both modulation techniques are chosen in this thesis because there are the most important candidates to deliver higher data rate for High Speed Downlink Packet Access (HSDPA), an extension of 3G networks [25]. The simulation is done under AWGN noise and multipath fading channel using MATLAB 7.1.

As it is shown in figure 4.1, the user data is assumed to be Bernoulli distributed and can be represented as bn(t). Each user data is then multiplied with independent or different PN code produced by a PN generator using XOR logical operator. The multiplied signal of each user is represented as sn(t) after the signal is modulated by either 16-QAM or QPSK. Each signal is added before it is subjected to the channel. At the receiver, the signal sk(t) is demodulated before the user data is separated from PN code by XOR logical operator. Finally, when the necessary simulations are done, tables and graphs of BER as a function of SNR for various parameters are plotted. Analysis, observations and results will be scaled on plots based on the simulation results.

Rayleigh fading and AWGN noise (LOS) are selected to symbolize fading effect in the channel because we want to make a comparison of W-CDMA system models in two extreme channel conditions. There are many fading effects that can be categorized as large-scale and small-scale fading. Rayleigh fading represents the worst case of multipath fading where it represents small-scale fading due to small changes in position with respect to time that is Doppler Effect. On the other hand, AWGN represents the thermal noise generated by electrical instruments.

4.2     Simulation Methodology

As computer based simulations are the most fitting, powerful and proficient means to stand for the actual or real time scenarios of mobile radio system. Thus, MATLAB 7.1 has been used to simulate W-CDMA model based on associated parameters, theories and formulae. So we use the MatLab 7.4 for simulation using m files. Throughout this project, we set the bit rate of 384Kbps for the signal generator.

There will be three W-CDMA wireless cellular system models that will be used in this research. The models are

1. W-CDMA system in AWGN channel

2. W-CDMA system in AWGN and Multipath Rayleigh Fading.

3. Multi-user W-CDMA system in AWGN and Multipath Rayleigh Fading (static and mobile)

 

4.3     Generation of Spreading Code

In CDMA, the choice of code sequenc1e is very important in respect to multiuser and multipath interference encountered by the signal in the channel. To combat these interferences, the code has to have the following properties:

1.      Each code sequence generated from a set of code-generation functions must be periodic with a constant length.

2.      Each code sequence generated from a set of code-generation functions must be easy to distinguish from its shifted code.

3.      Each code sequence generated from a set of code-generation functions must be easy to distinguish from other code sequences.

The first and second requirements are important with respect to the multipath propagation effects that occur in mobile outdoor and indoor radio environments. However, the third requirement is important with respect to the multiple access capability of communication systems. Thus, to ensure a distinction level of codes for requirements 1 and 2, an autocorrelation function and a cross-correlation function are used respectively

Autocorr.m is the MATLAB function that shows the autocorrelation function. The argument of this function is the number of periods of the code for which the autocorrelation function is to be obtained. Autocorrelation function is used to measure the distinction level and it is defined as follows:

For instance, to obtain an autocorrelation function of a code, the following command can be typed in the command window.

               X(t)=[1, 1, 1, -1, -1, 1, -1]

               >> X=[1, 1, 1, -1, -1, 1, -1];

               >> =autocorr(X);

In this case, three-stage M-sequence with a code length of 7 is used. The autocorrelation function is 7 at t=k.T(k=1, 2, 3,…) and -1 at other points. The value of correlation function R can be obtained by typing R_XX.

On the other hand, cross-corr.m is used to calculate the value of cross-correlation function between two distinct codes X(t) and Y(t).

The arguments of this function are the name of the sequence and the number of periods of the code for which the autocorrelation function is to be obtained. The following function will be typed to calculate the cross-correlation function of codes X(t) and Y(t).

                >> X=[1, 1, 1, -1, -1, 1, -1];

    >> Y=[1, -1, 1, -1, 1, 1, -1, 1];

    >> =crosscorr(X,Y);

Also, in this case, three-stage M-sequence and a random sequence with a code length of 7 will be used. To calculate the cross-correlation function,is typed. 

Thus, the spreading code can be calculated by using these autocorrelation and cross-correlation functions.

4.4     Code Generation by LFSR (Linear Feedback Shift Register)

In this research feedback shift register will be used to generate code sequences in WCDMA. A shift register contains a number of cells (numbered 1 to r) and each cell is a storage unit that, under the control of a clock pulse, moves its contents to its output while reading its new contents from its input. In a standard configuration of a feedback register, the input of cell m will be a function of the output of cell m-1 and the output of cell r (the last cell of the shift register) forms the desired code sequence.

 In linear feedback shift registers (linear FSRs), the function combining the outputs of cell m-1 and cell r with the input of cell m is linear. Figure shows a single linear binary shift register, which can generate a sequence from generation polynomial h(x) = x5 + x2 + 1. In general, the configuration of a linear binary shift register of n sections is described by a generator polynomial, which is a binary polynomial of degree n. n, in this case, is the number of sections of the shift register.

4.5     Generation of M-Sequence

M-sequence is a sequence generated by a single LSR where a sequence of possible period, (N_c=2ⁿ-1), is generated by an n-stage binary shift register with linear feedback. To generate an M-sequence, the generator polynomial must be a generation polynomial of degree n. Thus, the periodic autocorrelation function of an M-sequence is given by

If n  0 mod 4, there exist pairs of maximum-length sequence with a three-valued cross-correlation function, where the two values are {-t(n), t(n)-2} with

The m file is given as mseq.m. The number of registers, the initial values of the registers and the position of the feedback taps are given as argument in mseq.m. For instance, suppose the number of register is 3, the initial values of the registers are [1, 1, 1] and the position of the feedback tap is in the first and third taps. The generation polynomial can be expressed as h(x) = x³+ x+1. This configuration can be visualized using shift register as it is shown in figure. M-sequence can be generated by using the following command.

>> m1=mseq(3, [1,3], [1,1,1])

As a result, a three-stage M-sequence [1, 1, 1, 0, 1, 0, 0] is generated as a vector. A fourth argument, which denotes the number of output, is available in mesq.m. For a given number of N output, N one-chip shifted M-sequence. For example, another three stage M-sequence is generated by the following command:

    >> m2=mseq(3, [2, 3], [1, 1, 1], 3)

This command yields and output of

                Ans=

1 1 1 0 1 0 1 0

0 1 1 1 0 1 0 1

1 0 1 1 1 0 1 0

The shifting of the number of chips given by the users for the vector or matrix is performed by the function in file shift.m.

The characteristics of the M-sequences can be evaluated by using functions autocorr.m and crosscorr.m. The following commands are used to convert the generated code sequences consisting 0 and 1 to code sequences consisting -1 and 1.

    >> m1=m2*2-1;

    >> m2=m2*2-1;

The correlation function of three-stage M-sequence m1 can be calculated by typing the following command.

    >> autocorr(m1);

The autocorrelation value obtained is [7, -1, -1, -1, -1, -1, -1]. Thus it satisfies equation (). Next, the following command is used to find the cross-correlation function between m1 and m2(1,:).

>> crosscorr(m1,m2(1,:1));

      [3, -1, 3, -1, -1, -5, 3] is the cross-correlation value obtained from this command. This result takes three values namely [-1, -t(n), t(n)-2] where t(n)=5 taken from equation (). Thus, m1 and m2(1,:) have the characteristics of a preferred pair.

		Adder

 

                           Figure 4.2:   Three-stage M-sequence

4.6     Configuration of Transmitter and Receiver

In this section, the system is configured based on synchronous WCDMA system. Each user employs their own sequence to spread the information data. In this downlink transmission, the information data are modulated by the modulation scheme. Then, the modulated data are spread by code sequence that is M-sequence. The spreaded data of all users in the system are transmitted to the mobile users at the same time. The mobile user detects the information data of each user by correlating the received signal with a code sequence allocated to each user. The performance of the WCDMA system is studied based on QPSK and 16-QAM modulation techniques that will be used in this simulation.

The main simulation file is dscdma.m. The parameters used in the simulation are defined as follows.

Table 4.1:       The parameter for the main file

Parameter Name	Value
Symbol rate, sr	192000
Number of modulation levels, ml	2
Bit rate, br	sr * m1
Number of symbols, nd	100
Eb/No, ebn0	10
Number of filter taps, irfn	21
Number of oversamples, IPOINT	8
Roll-off factor, alfs	0.5

The coefficients of the filter that evaluates the performance of QPSK and 16-QAM are defined as follows.

[xh] = hrollfcoef(irfn, IPOINT, sr, alfs,1); % T Filter Function

[xh2]= hrollfcoef(irfn, IPOINT, sr, alfs,0); % R Filter Function

In synchronous WCDMA, the number of code sequences that can be allocated to different users is equal to the number of code lengths. Therefore, the length of the code sequence must be larger than of code lengths. Thus, the length of the code sequence must be larger than the number of users. To generate a code, the number of registers, the position of the feedback tap and the initial value of the registers has to be specified. Thus, the following parameters are used.

Table 4.2:       The parameter for  the code sequence

Parameter Name	Value
Number of users	1
M-sequence	1
Number of stage	3
Position of taps for 1st	[1 3]
Position of taps for 2nd	[2 3]
Initial value of register for 1st	[1 1 1]
Initial value of register for 2nd	[1 1 1]

By using these parameters, a spread code is generated and the generated code is stored as variable code. Code is a matrix with a sequence of the number of users multiplied by the length of the code sequence. Then, the following commands are used to convert generated code sequence consisting 0 and 1 into a sequence of -1 and 1.

Code = code * 2 – 1;

Clen = length(code);

Subsequently, the parameters for the fading simulator are defined. When rfade is declared as 0 (LOS), the file that evaluates the BER performance in the AWGN channel. On the other hand, when rfade is 1(NLOS), the simulation evaluates the BER performance in a multipath Rayleigh fading environment.

Table 4.3:       The parameter for fading simulator

Parameter	Value
Rayleigh fading, rfade	0
Delay time, itau	[0,8]
Attenuation level,dlvl1	[0.0,40.0]
Number of wave to generate fading,n0	[6 ,7]
Initial phase of delayed wave,th1	[0.0, 0.0]
Set fading counter, itnd1	[3001,4004]
Number of direct waves +delayed waves, now1	2
Frequency resolution, tstp	1/sr/IPOINT/clen
Doppler frequency (HZ),fd	160
Flat Rayleigh environment, flat	1

Consequently, the number of simulation loops is set. The variables that count the number of transmitted data bits and the number of errors are initiated.

Table 4.4:       The parameter for simulation loop

Parameter	Value
Simulation number of times, nloop	1000
Number of errors, noe	0
Number of data, nod	0

The transmitted data in the in-phase channel and quadrature phase modulated by QPSK or 16-QAM are multiplied by the code sequence used to spread the transmitted data. The spread data are then oversampled and filtered by a roll-off filter and transmitted to a communication channel. The format used to input these new functions does not depend on the vector or matrix. The files that perform these simulations are compoversamp2.m and compconv2.m.

Data = rand(user,nd*m1) 0.5;

[ich, qch] = qpskmod(data,user,nd,m1);                   % QPSK modulation

[ich1,qch1] = spread(ich,qch,code); % Spreading

[ich2,qch2] = compoversamp2(ich1,qch1,IPOINT);  % Oversampling

[ich3,qch3] = compconv2(ich2,qch2,xh);                 % T filter

It follows with the synthesis of transmitted signals from users.

If user = = 1               % Number of users is 1

     ich4 = ich3;

     qch4 = qch3;

else                            % Number of user is plural

     ich4 = sum(ich3);

     qch4= sum(qch3);

end

Then, the synthesized signal is contaminated in a Rayleigh fading channel.

If rfade = = 0       % in AWGN

    Ich5 = ich4;

    qch5 = qch4;

else                  % Rayleigh fading channel

     [ich5,qch5]=sefade(ich4,qch4,itau,dlvl1,th1,n0,itnd1,..

     now1,length(ich4),tstp,fd,flat);

     itnd1 = itnd1 + itndel;    % fading counter

end

At the receiver, AWGN is added to the received data as it is represented in a simulation file comb2.m. Next, the contaminated signal is filtered by using root cosine roll-off filter.

spow = sum(rot90(ich3.^2 + qch3.^2)) / nd;       %attenuation Calculation

attn = sqrt(0.5 * spow * sr / br * 10^(-ebn0/10));

[ich6,qch6] = comb2(ich5,qch5,attn);               % Add AWGN

[ich7,qch7] = compconv2(ich6,qch6,xh2);         % filter

sampl = irfn * IPOINT + 1;

ich8 = ich7(:,sampl:IPOINT:IPOINT*nd*clen+sampl-1);     % Resampling

qch8 = qch7(:,sampl:IPOINT:IPOINT*nd*clen+sampl-1);

Now the resample data are the synthesized data of all the users. By correlating the synthesized data with the spread code used at the transmitter, the transmitted data of all the users are detected. The correlation is done by despread.m.

[ich9 qch9] = despread(ich8,qch8,code); % dispreading

Then, the correlated data is demodulated by a modulation technique. The total number of errors for all the users is calculated. Eventually, the BER is calculated.

noe2 = sum(sum(abs(data-demodata))); % QPSK demodulation

nod2 = user * nd * ml;

noe = noe + noe2;

nod = nod + nod2;

To simulate WCDMA system in multipath fading channel with Doppler shift, similar procedures are used. The Doppler shifts (Hz) are based on mobile terminal velocity of 60kmph, 90kmph and 120kmph.

4.7     Steps Taken to Realize the Simulation in main.m file

The simulations for QPSK and 16-QAM modulation techniques are done by simulating the value of EbNo at a fixed interval. For example, if the range of EbNo is from 0 to 10 with interval of 1, the value of BER will be obtained for EbNo at 1 interval. This means the simulation to get the value of BERs has to be done 11 times. The range of EbNo is determined by the behavior of the BER at that EbNo’s range.

To realize the simulation of WCDMA in AWGN channel, the value of rfade is initialize to 0. Otherwise, it can be assigned to 1. When rfade=1, the channel of WCDMA system is subjected to AWGN and multipath fading channel. The Doppler shift, on the other hand, is defined in fd. It represents the value of Doppler shift in Hertz (Hz).

Furthermore, the simulation of 16-QAM can be achieved by swapping the functions of modulator and demodulator from qpskmod and qpskdemod to qammod and qamdemod respectively.

4.8     Limitation

DS-CDMA is the main system model to study the performance of modulation techniques in multipath channel. There will be no error correction scheme (channel coding) used in this research. Also, there will be no equalization as well as interleaving employed in the W-CDMA system model. The receiver is assumed not a RAKE receiver neither MIMO receiver. The channel is subjected to AWGN noise and Rayleigh fading only.

Furthermore, the BER in LOS for this model is based on the Simplified Improved Gaussian Approximation (SIGA). On the other hand, BER for Rayleigh fading is based on either synchronous or asynchronous transmissions. For asynchronous transmission, the assumption is that the Multi Access Interference (MAI) on the flat Rayleigh fading channel has a Gaussian first-order distribution. However, characteristic function, Ф, is used in asynchronous transmission to determine the total MAI, I, and therefore the BER can be computed based on these variables.

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