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Bits2Pulses.m~
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Bits2Pulses.m~
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function [Pmean, OSigmaNoise, RZBit0, RZBit1Gauss]=Bits2Pulses(Carrier, gauss, InBitString, OSNR, RZPulsesTrain, RZPulsesTrainTime, transmitters, LOff)
[RZBit0, RZBit1Gauss]=RZPulsesTrainGenerator(RZPulsesTrain, PulsesTrainTime, Carrier, gauss, InBitString, TrainLength, transmitters);
% OPTICAL NOISE (AWGN)
% %ADDITIVE WHITE GAUSSIAN NOISE [SANTIAGO'S GENERATOR]
%
% SigmaNoise: [V/m*10-3] width of amplitude gaussian distribution A(t) related to mean noise power as follows:
%
% white noise autocorrelation = \sigma^2 * delta(t) [http://en.wikipedia.org/wiki/White_noise]
% Wiener-Khinchin: \phi(\omega) [Power Spectral Density]= Fourier(white noise autocorrelation ) [http://en.wikipedia.org/wiki/Wiener–Khinchin_theorem]
% -> \phi(\omega)= \sigma^2 * Fourier(\delta(t))
% P [Power]= \int d\omega \phi(\omega) [http://en.wikipedia.org/wiki/Spectral_density]
% -> P= \sigma^2 * Fourier(\delta(t))
% gaussian: Fourier(\delta)=\frac{1}{\sqrt(2*\pi)}
% -> P =\sigma^2
%
%
% SigmaNoise is calculated from SNR assuming the following:
% SNR=Power_signal/Power_noise [http://en.wikipedia.org/wiki/Signal-to-noise_ratio]
% Power_noise=SigmaNoise^2
% Power_signal= 0.5*(Power_'0bit' + Power_'1bit') assuming equal number of '1' and '0' bits [Andres, Nacho]
% Power_'0bit'= LOff^2, amplitude for bit '0' due to system extinction ratio
% Power_'1bit'= P1Mean= \frac{1}{(*Carrier).Bit_Slot} \int_0^{(*Carrier).Bit_Slot} dt Pulse1(t)^2 integrated in bit slot
% OR? Power_'1bit'= \frac{1}{LimSup-LimInf} \int_LimInf^{LimSup} dt Pulse1(t)^2 integrated in detection window
% -> SigmaNoise=\sqrt(\frac{LOff^2 + P1Mean}{2 SNR})
% mean signal power -> gaussian sigma noise
Pmean= 0;
for i= 0: Carrier.NPtos_Bit
Pmean= Pmean+ RZBit1Gauss(i)* RZBit1Gauss(i);
end
Pmean= 0.5* ( (Pmean/ Carrier.NPtos_Bit )+ LOff* LOff);
OSigmaNoise= sqrt( Pmean/ OSNR); % amplitude of noise (Agrawal 4.4.11) [Santiago I.]
% printf("OSNR= %e\tPmean= %e\tOSigmaNoise= %e\n",OSNR ,*Pmean, *OSigmaNoise); % TEST
% float ONoiseVector[(*Carrier).NPtos_Tot];
% GenRuidoRapido(ONoiseVector, OSigmaNoise, Carrier.NPtos_Tot); % NoiseVector amplitude
ONoiseVector= wgn(Carrier.NPtos_Tot,1,OSigmaNoise,'linear','real'); % MATLAB GAUSSIAN NOISE
%for(i=0; i<(*Carrier).NPtos_Tot; i++) printf("%.3e\t%.3e\n",PulsesTrainTime[i],RZPulsesTrain[i]); % TEST
%for(i=0; i<(*Carrier).NPtos_Tot; i++) printf("%.3e\t\n",(float) NoiseVector[i]); % TEST
%WriteFile("ONoiseVector.dat", RZPulsesTrainTime, ONoiseVector, (*Carrier).NPtos_Tot); % TEST train to file
%printf("%li\n",(*Carrier).NPtos_Tot); % TEST
% Noise addition
for i=0:Carrier.NPtos_Tot
RZPulsesTrain(i)= RZPulsesTrain(i)+ ONoiseVector(i); % Noise addition
end