Bench Inspection Day!

Finally, the time has come, where all our hard work spent on the project is showcased for all to see. In the form of a poster, lab book and this blog (along with a few real time practical simulations), we present our findings of how DSSS in 3G communications is possible. Our main focus with this project was the CDMA modulation technique, which is widely used in industry. Without this modulation technique it would be difficult to transmit the Data securely and efficiently.

A lot has been learnt about how Matlab can be used to simulate physical systems, which can be vital in helping with the design process and to ensure the system works before implementing it. 

In the 6 weeks we have been given to produce a project, it has been difficult at times but I think the results we have obtained have been significant in showing how a DSSS system functions.

CDMA code for 4 Users

The text below is the final code a simulation that can extract the original data of 4 separate users from the interference pattern of 64 simultaneous users. This is code was used for the previous post.

%This script runs a simulation of a 64 simultaneous user system where 4 
%users need to be extracted by using their unique spreading code. The
%script then plots graphs of original data, the spreading code for each of
%the 4 users, the interference pattern and the recovered signal. All
%contained within a figure.


%Declares the variables
Length =20;
Users =64; 
Index1 =7;
Index2 =20;
Index3 =30;
Index4 =43;
spread = 0;
V = [1,-1];
L = Users * Length;
%Generates a random noise pattern
noise =  V(randi([1,numel(V)],L,1));

W = [1];

%A loop that generates the Walsh-Hadamard matrix.
for n = 1 : log2(Users);
    W = [ W W ; W -W];
end

%A loop that spreads user signal with spreading code unique to each user
%and then combines the spreading signals of each user into an interference
%pattern
for x=0 : (Users-1);

    %Generates spreading code for each user based on their user number.
    code = W(x+1, :);

    %Generates a random set of data for each user to transmit.
    D1 = UserData(Length);

    %This will multiply the spreading code by individual bit of User Data.
    spread1=[code*D1(1),code*D1(2),code*D1(3),code*D1(4),code*D1(5),code*D1(6),code*D1(7),code*D1(8),code*D1(9),code*D1(10),code*D1(11),code*D1(12),code*D1(13),code*D1(14),code*D1(15),code*D1(16),code*D1(17),code*D1(18),code*D1(19),code*D1(20)];

    %Checks for user one.
    if x == Index1
        UserD1 = D1; 
        SpreadC1 = code;
    end
    %Checks for user two.
      if x == Index2
                UserD2 = D1;
                SpreadC2 = code;
      end 
      %Checks for user three.
           if x == Index3
                    UserD3 = D1;  
                    SpreadC3 = code;
           end     
           %Checks for user four.
               if x == Index4
                        UserD4 = D1; 
                        SpreadC4 = code;
               end

    %Adds spread signal to the interference pattern after each loop.           
    spread=spread+spread1;     
end

%Adds noise to the interference pattern.
spread=spread+noise

%Extracts the two pieces of user data from the interference pattern.
c1=despread(SpreadC1,spread);
c2=despread(SpreadC2,spread);
c3=despread(SpreadC3,spread);
c4=despread(SpreadC4,spread);

%These all declare the axis for the graphs.
Plot1X1=size(UserD1);
X1= [1:(Plot1X1(2))];

Plot1X2=size(UserD2);
X2= [1:(Plot1X2(2))];

Plot1X3=size(UserD3);
X3= [1:(Plot1X3(2))];

Plot1X4=size(UserD4);
X4= [1:(Plot1X4(2))];

Plot3X1=size(SpreadC1);
Plot3X1=Plot3X1(1)*Plot3X1(2);
X5= [1:(Plot3X1)];

Plot3X2=size(SpreadC2);
Plot3X2=Plot3X2(1)*Plot3X2(2);
X6= [1:(Plot3X2)];

Plot3X3=size(SpreadC3);
Plot3X3=Plot3X3(1)*Plot3X3(2);
X7= [1:(Plot3X3)];

Plot3X4=size(SpreadC4);
Plot3X4=Plot3X4(1)*Plot3X4(2);
X8= [1:(Plot3X4)];

Plot5X1=size(spread);
Plot5X1=Plot5X1(1)*Plot5X1(2);
X9= [1:(Plot5X1)];

Plot7X1=length(c1);
X10=[1:Plot7X1];

Plot7X2=length(c2);
X11=[1:Plot7X2];

Plot7X3=length(c3);
X12=[1:Plot7X3];

Plot7X4=length(c4);
X13=[1:Plot7X4];


%The following code plots 13 graphs in the form of a square wave plotted in 
%a single figure.
h1=subplot(5,4,1);plot(X1,UserD1)
h1=stairs(X1,UserD1);
grid 'on';
title('Data set 1 to be transmitted.')

h2=subplot(5,4,2);plot(X2,UserD2)
h2=stairs(X2,UserD2);
grid 'on';
title('Data set 2 to be transmitted.')

h3=subplot(5,4,3);plot(X3,UserD3)
h3=stairs(X3,UserD3);
grid 'on';
title('Data set 3 to be transmitted.')

h4=subplot(5,4,4);plot(X4,UserD4)
h4=stairs(X4,UserD4);
grid 'on';
title('Data set 4 to be transmitted.')

h5=subplot(5,4,5:6);plot(X5,SpreadC1)
h5=stairs(X5,SpreadC1);
grid 'on';
title('Spread data 1.')

h6=subplot(5,4,7:8);plot(X6,SpreadC2)
h6=stairs(X6,SpreadC2);
grid 'on';
title('Spread data 2.')

h7=subplot(5,4,9:10);plot(X7,SpreadC3)
h7=stairs(X7,SpreadC3);
grid 'on';
title('Spread data 3.')

h8=subplot(5,4,11:12);plot(X8,SpreadC4)
h8=stairs(X8,SpreadC4);
grid 'on';
title('Spread data 4.')

h9=subplot(5,4,13:16);plot(X9,spread)
h9=stairs(X9,spread);
grid 'on';
title('Transmitted signal including interference')

h10=subplot(5,4,17);plot(X10,c1)
h10=stairs(X10,c1);
grid 'on';
title('Despread Signal 1')

h11=subplot(5,4,18);plot(X11,c2)
h11=stairs(X11,c2);
grid 'on';
title('Despread Signal 2')

h12=subplot(5,4,19);plot(X12,c3)
h12=stairs(X12,c3);
grid 'on';
title('Despread Signal 3')

h13=subplot(5,4,20);plot(X13,c4)
h13=stairs(X13,c4);
grid 'on';
title('Despread Signal 4')

Clean system versus noise added system

The system above is clean (without added noise). This just shows that at least 4 different users can be extracted from the interference pattern comprised of 64 unique spread signals. Its important to test if the system is capable of spreading and despreading the signal before integrity testing can be done. Each signal is colour coordinated with their spreading signal and despread data code, making the evaluation easier.



The same users would like to transmit a different set of data, but this time the system is being affected by some form of noise. The noise used was a random 1 & -1 sequence with the same length as the interference pattern and added to it before the data is despread. The data does keep its original shape however it has a slightly distorted to it but only slightly and as we have noticed, the same noise becomes less effective as the length of the spreading code increases, which boasts improved performance.

Meet the Team Working Hard

On the left is Matthew Davies. He is currently showing of one of his new poses, which he will be using in his new modelling job "Just For Gingers" magazine. Whilst apparently working.













On the right is Hamish Smellie, with his unprecedented 'knowledge' of 3G communication. Currently sporting the hard working student look, his body language shows he is uncomfortable with this.

Hadamard Sequence

There is a very simple way to create a set Hadamard codes, it is called the the recursive technique.

Below is an example of how four sequences of code are created from the two sets using the above formula.

So above shows four sequences of code and three lines of code are used for characteristics, synchronization and paging. Which means that a four chip spreading code, like above, will only have one available slot for a user.