Normalizing Path Loss Model to Test Data
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Looking for advice on methods of normalizing data. I have collected network level data and calculated the signal loss ('LOSS' in code below, measured in dB). I would like to compare this data to the free space path loss propagation model, as calculated in the code. To do this, I have tried to normalise the data between 0 and 1, however I am not getting the results I expected when I do this.
% Distance and path loss from test data
DIST = data.DIST;
LOSS = data.LOSS;
% distance vector
d = DIST;
% frequency
f = 1800e6;
% Calculate free space path loss model
FreeSpaceLoss = 20*log10(((4*pi*d*f)/3e8).^2);
% Normalise model
freeSpaceNorm = abs(FreeSpaceLoss)./max(abs(FreeSpaceLoss));
% Normalise test data
dataNorm = abs(LOSS)./max(abs(LOSS));
% Plotting data over normalised models
figure, hold on
plot(DIST, freeSpaceNorm, 'LineWidth',1.5)
plot(DIST, dataNorm, '.', 'MarkerSize', 7)
xlabel('Distance (m)')
ylabel('Loss (dB)')
title('PL Models vs Data')
legend('fspl', 'test data')
hold off;
I get the resulting plot, but the path loss model does not start at zero. I may be missing something small, or misunderstanding why this is happening - just looking for any suggestions on the best way to compare the two datasets.
Thanks in Advance.
3 Comments
Mathieu NOE
on 6 Apr 2021
hello
whatever the units or the scaling , it has to be coherent between measurements and theory...
now remember to remove zero valued data if you intend to use log scaling
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