plz explain this code

%Normalizing Signals
x1=x1./sqrt(P1);
x2=x2./sqrt(P2);
x3=x3./sqrt(P3);
x4=x4./sqrt(P4);

SNR=input('Enter The desired value of SNR in dBs:');
N0=10^(-SNR/10);

%generating noise signal of desired SNR
noise=sqrt(N0/l).*randn(1,l);

%Received Signal

r1=x1+noise;
r2=x2+noise;
r3=x3+noise;
r4=x4+noise;

%Matched Filter
M1=filter(fliplr(x1),1,r1);
M2=filter(fliplr(x2),1,r2);
M3=filter(fliplr(x3),1,r3);
M4=filter(fliplr(x4),1,r4);
figure;subplot(411);
plot(M1);
subplot(412);
plot(M2);
subplot(413);
plot(M3);
subplot(414);
plot(M4)

%correlation
X1=xcorr(r1,x1);
X2=xcorr(r2,x2);
X3=xcorr(r3,x3);
X4=xcorr(r4,x4);

figure;subplot(411);
plot(X1);
subplot(412);
plot(X2);
subplot(413);
plot(X3);
subplot(414);
 plot(X4)

[Image Analyst]

http://www.mathworks.com/matlabcentral/answers/13205-tutorial-how-to-format-your-question-with-markup

[Adam]

There are comments in the code. Which part do you need explaining. If you need every single statement explained you need to read up some basic Matlab documentation, if there are specific parts you don't understand please be clear which they are.

[muhammad asif]

The code after the matched filter statement is not understandable for me.

Answer 1

[Hari]

Hi Muhammad,

I understand that you are trying to comprehend the MATLAB code related to signal processing, specifically the part after the matched filter statement involving correlation.

Here is the explaination:

Correlation Calculation:

The code uses the "xcorr" function to calculate the cross-correlation between each received signal "r" and its corresponding original signal "x". This is done to measure the similarity between the received signal and the original signal.

X1 = xcorr(r1, x1);
X2 = xcorr(r2, x2);
X3 = xcorr(r3, x3);
X4 = xcorr(r4, x4);

Purpose of Correlation:

Cross-correlation is used to detect the presence of a known signal within a noisy received signal. It helps in identifying the time delay or phase shift between the two signals.

In this context, it is used to assess how well the received signals "r1", "r2", "r3", and "r4" match the original signals "x1", "x2", "x3", and "x4".

Plotting Correlation Results:

The code plots the correlation results for each signal pair using the "subplot" function. This allows for a visual comparison of the correlation results for each signal.

figure;
subplot(411);
plot(X1);
subplot(412);
plot(X2);
subplot(413);
plot(X3);
subplot(414);
plot(X4);

Interpretation of Plots:

The plots show the correlation values over time. Peaks in the plots indicate strong correlation, suggesting that the corresponding received signal contains a significant component of the original signal.

This visualization helps in understanding how well the matched filter has performed in extracting the signal from noise.

Utility of Matched Filtering and Correlation:

Matched filtering is used to maximize the signal-to-noise ratio (SNR) and is followed by correlation to confirm the presence and alignment of the signal. Together, they are powerful tools in digital communication for signal detection and synchronization.

Refer to the documentation of xcorr for more details: https://www.mathworks.com/help/signal/ref/xcorr.html

Refer to the documentation of filter for more details: https://www.mathworks.com/help/matlab/ref/filter.html

Hope this helps!