silas/imdd_silas
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Faster DP_fiber with GPU processing... Run test\gpu_cpu_comparison.m to see difference on your setup

Browse Source
This commit is contained in:
silas (home)
2026-02-01 13:53:20 +01:00
parent 67689bb70f
commit fba7cbdba2
15 changed files with 1281 additions and 217 deletions
Download Patch File Download Diff File Expand all files Collapse all files

45
Classes/02_optical/Optical_Multiplex.m
View File

@@ -1,10 +1,10 @@
classdef Optical_Multiplex < handle
% Takes a cell array of signals
% returns a total field signal
% WDM spacing is given in wavelength plan OR via delta_F
% returns a total field signal
% WDM spacing is given in wavelength plan OR via delta_F
% The grid is stored in the output signal -> the demux will ideally
% look this up and use this as the demux frequencies...
% look this up and use this as the demux frequencies...
% signal_cell = {Opt_sig_1, Opt_sig_2};
% Opt_sig_wdm = Optical_Multiplex("fs_in",Opt_sig.fs,"fs_out",4*Opt_sig.fs,...
@@ -117,7 +117,7 @@ classdef Optical_Multiplex < handle
% adapt frequency shifts to match the FFT grid! Find nearest grid point
[glitch(o),pos] = min(abs( freqaxis-obj.df_T(o) ));
obj.df_T(o) = freqaxis(pos);
polrots = [polrots, data_in{o}.polrot];
end
@@ -139,25 +139,36 @@ classdef Optical_Multiplex < handle
x_envelopes = NaN([blocklen_out N]);
y_envelopes = x_envelopes;
% ---- OPTIMIZED: Pre-compute all LO phases as [blocklen_out × N] matrix ----
time_idx = (0:blocklen_out-1).'; % [blocklen_out × 1]
lo_phases = mod(2*pi * time_idx * obj.df_T / obj.fs_out, 2*pi); % [blocklen_out × N]
lo_all = cos(lo_phases) + 1i*sin(lo_phases); % [blocklen_out × N]
% Collect all resampled signals first (still requires loop due to cell array)
x_signals = zeros(blocklen_out, N);
y_signals = zeros(blocklen_out, N);
for o = 1:N
pha = mod(2*pi*(0:blocklen_out-1)*obj.df_T(o)/obj.fs_out,2*pi).';
lo = cos(pha)+1i*sin(pha);
data_in_resampled = data_in{o}.resample("fs_out",obj.fs_out);
res_env = ifft(fft(data_in_resampled.signal(:,1)).*H);
x_envelopes(:,o) = att.*res_env.*lo;
res_env = ifft(fft(data_in_resampled.signal(:,2)).*H);
y_envelopes(:,o) = att.*res_env.*lo;
data_in_resampled = data_in{o}.resample("fs_out", obj.fs_out);
x_signals(:, o) = data_in_resampled.signal(:, 1);
y_signals(:, o) = data_in_resampled.signal(:, 2);
end
% ---- VECTORIZED: Batched FFT/IFFT for all channels ----
% Apply filter to all channels at once
x_filtered = ifft(fft(x_signals) .* H); % [blocklen_out × N]
y_filtered = ifft(fft(y_signals) .* H); % [blocklen_out × N]
% Apply attenuation and LO shift to all channels
x_envelopes = att .* x_filtered .* lo_all; % [blocklen_out × N]
y_envelopes = att .* y_filtered .* lo_all; % [blocklen_out × N]
data_out = data_in_resampled;
data_out.signal = [sum(x_envelopes,2), sum(y_envelopes,2)];
data_out.lambda = obj.lambda_T;
data_out.polrot = polrots;
end
end