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mainly tests (empty) and some theroy plots

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This commit is contained in:
Silas Oettinghaus
2026-03-12 17:26:13 +01:00
parent 2a724b833f
commit 4c78a1e0b1
90 changed files with 1507 additions and 404 deletions
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192
Functions/Theory/Dissertation/dispersion_around_zdw.m
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@@ -9,31 +9,35 @@
% S0 - dispersion slope at ZDW [ps/(nm^2·km)]
% ------------------------------------------------------------
lambda0_nm = 1310;
S0 = [0.07,0.09]';
%% ZDW 1
lambda0_nm = [1310];
S0 = [0.075,0.095]';
% wavelength axis around ZDW
lambda_nm = linspace(lambda0_nm-60, lambda0_nm+60, 400);
lambda_nm = linspace(1240, 1360, 400);
% exact dispersion model
D_exact = (S0./4) .* ( ...
D_exact_1 = (S0./4) .* ( ...
lambda_nm - (lambda0_nm^4)./(lambda_nm.^3) );
% linearized model around ZDW
D_lin = S0 .* (lambda_nm - lambda0_nm);
%% ZDW 2
lambda0_nm = [1320];
% exact dispersion model
D_exact_2 = (S0./4) .* ( ...
lambda_nm - (lambda0_nm^4)./(lambda_nm.^3) );
% plot
%% plot
figure('Color','w'); hold on;
cols = [0.6510 0.8078 0.8902;...
0.1216 0.4706 0.7059;...
0.6980 0.8745 0.5412;...
0.2000 0.6275 0.1725];
% plot(lambda_nm, D_lin(1,:), '-', 'LineWidth',2, 'DisplayName','Linearized model','Color',[cols(1,:)]);
plot(lambda_nm, D_exact(1,:), 'LineWidth',2, 'DisplayName',sprintf('S_0 = 0.07'),'Color',[cols(2,:)]);
% plot(lambda_nm, D_lin(2,:), '-', 'LineWidth',2, 'HandleVisibility','off','Color',[cols(3,:)]);
plot(lambda_nm, D_exact(2,:), 'LineWidth',2, 'HandleVisibility','on', 'DisplayName',sprintf('S_0 = 0.09'),'Color',[cols(4,:)]);
plot(lambda_nm, D_exact_1(1,:), 'LineWidth',2, 'DisplayName',sprintf('$S_0=\SI{0.09}{\Dslope}$'),'Color',[0,0,0]);
plot(lambda_nm, D_exact_1(2,:), 'LineWidth',2, 'DisplayName',sprintf('$S_0=\SI{0.09}{\Dslope}$'),'Color',[0,0,0]);
% plot(lambda_nm, D_exact_2(1,:), 'LineWidth',2, 'HandleVisibility','on', 'DisplayName',sprintf('$S_0=\SI{0.09}{\Dslope}$'),'Color',[0,0,0]);
% plot(lambda_nm, D_exact_2(2,:), 'LineWidth',2, 'HandleVisibility','on', 'DisplayName',sprintf('$S_0=\SI{0.09}{\Dslope}$'),'Color',[0,0,0]);
xlabel('Wavelength $\lambda$ [nm]','Interpreter','latex');
ylabel('D($\lambda$) [ps/(nm km)]','Interpreter','latex');
@@ -44,3 +48,167 @@ xlim([min(lambda_nm) max(lambda_nm)])
ylim([-5 5]);
% mat2tikz_improved('C:\Users\Silas\Documents\6971e0b65b380ca6d71c837f\02_IMDD_System\tikz\dispersion\dispersion')
%%
%% plot_dispersion_dual_ZDW
% Visualizes two ZDW fiber types, each with an S0 tolerance range.
%% plot_dispersion_minimal_for_tikz
% Minimal setup for clean TikZ tuning
% 1. Parameters
lambda_nm = linspace(1240, 1360, 400);
S0_range = [0.075, 0.095];
ZDW_range = [1300, 1325];
% 2. Calculate Envelope and Nominal
[S_mesh, Z_mesh] = meshgrid(S0_range, ZDW_range);
D_all = zeros(length(lambda_nm), 4);
for i = 1:4
D_all(:,i) = (S_mesh(i)/4) .* (lambda_nm - (Z_mesh(i)^4)./(lambda_nm.^3));
end
D_env_min = min(D_all, [], 2);
D_env_max = max(D_all, [], 2);
D_nominal = (mean(S0_range)/4) .* (lambda_nm - (mean(ZDW_range)^4)./(lambda_nm.^3));
% 3. Plotting
figure('Color','w'); hold on;
env_col = [0.1216, 0.4706, 0.7059];
% Shaded area
[hl, hp] = boundedline(lambda_nm, (D_env_min+D_env_max)/2, ...
(D_env_max-D_env_min)/2, 'alpha', 'cmap', env_col);
set(hl, 'YData', D_nominal, 'Color', 'k', 'LineWidth', 1.5);
% Generate the outline and capture the handle
ho = outlinebounds(hl, hp);
% Change properties
set(ho, 'Color', 'k', ... % Make it black
'LineStyle', '--', ... % Make it dashed
'LineWidth', 0.5, ... % Make it thin
'HandleVisibility', 'off'); % Hide from legend
% 4. "Minimal" Measurement Lines (Tweak these in TikZ later)
% Vertical measurement at 1290nm
lambda_v = 1290;
[~, idx] = min(abs(lambda_nm - lambda_v));
y_bot = D_env_min(idx);
y_top = D_env_max(idx);
% Simple line - in TikZ this will be a single \draw command
line([lambda_v, lambda_v], [y_bot, y_top], 'Color', 'r', 'LineWidth', 1.2, 'Tag', 'VertArrow');
text(lambda_v + 2, (y_top+y_bot)/2, '$\Delta D$', 'Color', 'r', 'Interpreter', 'latex');
% Horizontal measurement at D=0
z1 = ZDW_range(1);
z2 = ZDW_range(2);
line([z1, z2], [0, 0], 'Color', [0.1 0.5 0.1], 'LineWidth', 1.2, 'Tag', 'HorizArrow');
text(mean(ZDW_range), 0.5, '$\Delta \lambda_0$', 'Color', [0.1 0.5 0.1], ...
'Interpreter', 'latex', 'HorizontalAlignment', 'center');
% 5. Aesthetics
xlabel('Wavelength $\lambda$ [nm]', 'Interpreter', 'latex');
ylabel('$D(\lambda)$ [ps/(nm km)]', 'Interpreter', 'latex');
grid on; box on;
xlim([1240 1360]); ylim([-5 5]);
line(xlim, [0 0], 'Color', [0.4 0.4 0.4], 'LineStyle', '--', 'HandleVisibility', 'off');
% Legend using the handles we have
legend([hp, hl], {'Worst-case Envelope', 'Nominal Realization'}, ...
'Location', 'northwest', 'Interpreter', 'latex');
% To export, run:
% matlab2tikz('dispersion.tex', 'standalone', true);
%% plot_dispersion_for_tikz
% Optimized for matlab2tikz compatibility with manual arrows
%% plot_dispersion_statistical_envelopes
% Visualizes hard spec limits vs. 98% statistical distribution
%% plot_dispersion_final_for_tikz
lambda_nm = linspace(1240, 1360, 400);
% 1. Statistical & Specification Parameters
p01_L = norminv(0.01, 1317, 2);
p99_L = norminv(0.99, 1317, 2);
p01_S = norminv(0.01, 0.0872, 0.0012);
p99_S = norminv(0.99, 0.0872, 0.0012);
% Scenarios: [ZDW_min, ZDW_max], [S0_min, S0_max], [Color RGB]
scenarios = { ...
[1303, 1325], [0.075, 0.0925], [0.6510, 0.8078, 0.8902]; ... % 1. Wide Spec (Gray)
[p01_L, p99_L], [p01_S, p99_S], [0.6, 0.6, 0.6] ... % 2. 98% Stats (Blue)
};
figure('Color','w'); hold on;
hp_handles = [];
% 2. Calculate and Plot Envelopes
for k = 1:size(scenarios, 1)
Zr = scenarios{k,1};
Sr = scenarios{k,2};
col = scenarios{k,3};
[S_mesh, Z_mesh] = meshgrid(Sr, Zr);
D_all = zeros(length(lambda_nm), 4);
for i = 1:4
D_all(:,i) = (S_mesh(i)/4) .* (lambda_nm - (Z_mesh(i)^4)./(lambda_nm.^3));
end
D_min_env = min(D_all, [], 2);
D_max_env = max(D_all, [], 2);
[hl, hp] = boundedline(lambda_nm, (D_min_env+D_max_env)/2, (D_max_env-D_min_env)/2, ...
'cmap','alpha', col);
hp_handles(k) = hp;
set(hl, 'Visible', 'off');
if k == 1
D_wide_min = D_min_env;
D_wide_max = D_max_env;
% ho = outlinebounds(hl, hp);
% % Change properties
% set(ho, 'Color', 'k', ... % Make it black
% 'LineStyle', '--', ... % Make it dashed
% 'LineWidth', 1, ... % Make it thin
% 'HandleVisibility', 'off'); % Hide from legend
% Capture Wide Spec (k=1) bounds for the TikZ measurement lines
hp.FaceAlpha = 0.5;
else
hp.FaceAlpha = 0.8;
end
end
% 3. Nominal Line
D_nom = (0.0872/4) .* (lambda_nm - (1317^4)./(lambda_nm.^3));
h_nom = plot(lambda_nm, D_nom, 'k', 'LineWidth', 1,'LineStyle','-');
% 4. Minimal Lines for TikZ (Measuring Wide Spec)
lambda_v = 1290;
[~, idx_v] = min(abs(lambda_nm - lambda_v));
% Vertical line showing full Wide Spec dispersion range at 1290nm
line([lambda_v, lambda_v], [D_wide_min(idx_v), D_wide_max(idx_v)], 'Color', 'k', 'Tag', 'VertArrow','LineWidth', 1);
% Horizontal line showing Wide Spec ZDW range at D=0
% line([1303, 1325], [0, 0], 'Color', 'k', 'Tag', 'HorizArrow','LineWidth', 1);
% 5. Aesthetics & Legend
xlabel('Wavelength $\lambda$ [nm]', 'Interpreter', 'latex');
ylabel('$D(\lambda)$ [ps/(nm km)]', 'Interpreter', 'latex');
grid on; box on;
xlim([1240 1360]); ylim([-5 5]);
leg_labels = { ...
'$\lambda_0 \in [1303, 1325], S_0 \in [0.075, 0.0925]$', ...
'$\lambda_0 \in [1312, 1322], S_0 \in [0.084, 0.090]$', ...
'$\lambda_0 = 1317, S_0 = 0.0872$'};
legend([hp_handles, h_nom], leg_labels, 'Location', 'northwest', 'Interpreter', 'latex', 'FontSize', 8);
% Export command (uncomment to use)
% mat2tikz_improved('C:\Users\Silas\Documents\6971e0b65b380ca6d71c837f\02_IMDD_System\tikz\dispersion\dispersion_slope.tikz')

183
Functions/Theory/Dissertation/dispersion_contour_bandwidth_lambda.m Normal file
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@@ -0,0 +1,183 @@
%% ------------------------------------------------------------
% Contour plot: λ_null as function of bandwidth (f_target) and reach (L)
% ------------------------------------------------------------
% Parameters
lambda0 = 1310e-9; % [m]
S0 = 0.09; % [ps/(nm²·km)]
c = physconst('lightspeed');
% Sweep dimensions
f_targets = linspace(50e9, 130e9, 200); % [Hz] (x-axis)
L_values = linspace(0.5e3, 15e3, 200); % [m] (y-axis)
lambda_surface = zeros(numel(L_values), numel(f_targets));
Dacc_surface = zeros(numel(L_values), numel(f_targets));
% Outer loop over fiber length (since L must be scalar)
for iL = 1:numel(L_values)
L = L_values(iL);
[lambda_vec, Dacc_vec] = lambda_for_first_null_full(f_targets, L, lambda0, S0);
% Store the 1x absolute offset |lambda - lambda0|
lambda_surface(iL, :) = abs(lambda0 - lambda_vec);
Dacc_surface(iL, :) = Dacc_vec;
end
% Convert for plotting
lambda_surface_nm = lambda_surface * 1e9; % [nm]
L_km = L_values / 1000; % [km]
f_GHz = f_targets / 1e9; % [GHz]
%% Contour plot
figure('Color','w');
hold on;
% Define wavelength contour levels [nm]
% Focus on a clean range of 1x offset values
lambda_levels = unique([50:-10:30, 30:-5:5]);
% Contour plot
[C,h] = contour(f_GHz, L_km, lambda_surface_nm, lambda_levels, ...
'LineWidth', 1.2, ...
'ShowText', 'off');
% Colormap: Modern Blue palette with light colors removed for visibility
cmap_full = cbrewer2('Blues', 40);
colormap(cmap_full(10:end, :));
clim([min(lambda_levels) max(lambda_levels)]);
cb = colorbar;
ylabel(cb, '$\Delta \lambda$ [nm]', 'Interpreter', 'latex');
% --- MANUAL TEXTBOX ANNOTATIONS ---
% Find placement along the first-null curve for each level
for i = 1:length(lambda_levels)
lvl = lambda_levels(i);
% Re-calculate the specific (f, L) curve for this delta-lambda
lambda_target = lambda0 - (lvl * 1e-9);
LHS = -( (S0*1e3) / 4 ) * (lambda_target - (lambda0^4)/(lambda_target^3)) * lambda_target^2;
const_val = (c*0.5) / LHS;
f_curve_GHz = linspace(min(f_GHz), max(f_GHz), 500);
L_curve_km = const_val ./ (f_curve_GHz * 1e9).^2 / 1000;
% Filter for points within the plot axes
in_bounds = find(L_curve_km >= min(L_km)*1.1 & L_curve_km <= max(L_km)*0.9 & ...
f_curve_GHz >= min(f_GHz)*1.1 & f_curve_GHz <= max(f_GHz)*0.9);
if ~isempty(in_bounds)
% Specific alternating pattern for weight to minimize overlapping
if i < 9
weight = 0.05;
else
weight = 0.05 + 0.1 * mod(i, 2);
end
idx = in_bounds(max(1, min(length(in_bounds), round(length(in_bounds) * weight))));
text(f_curve_GHz(idx), L_curve_km(idx), sprintf('%g nm', lvl), ...
'Color', 'k', 'BackgroundColor', 'w', 'Margin', 1.5, ...
'HorizontalAlignment', 'center', 'VerticalAlignment', 'middle', ...
'EdgeColor', 'k', 'FontSize', 9);
end
end
% Axis formatting
xlabel('Signal Bandwidth [GHz]', 'FontSize', 11);
ylabel('Fiber length [km]', 'FontSize', 11);
xticks(min(f_GHz):10:max(f_GHz));
yticks(min(L_km):2.5:max(L_km));
grid on; box on;
axis([min(f_GHz) max(f_GHz) min(L_km) max(L_km)]);
%% Optional: overlay accumulated-dispersion contours
if 0
hold on;
min_D = min(Dacc_surface(:), [], 'omitnan');
max_D = max(Dacc_surface(:), [], 'omitnan');
% Calculate 3 integer levels well within the data range
D_levels = unique(round(linspace(min_D*0.8, max_D*0.8, 3)));
[CS, h] = contour(f_GHz, L_km, Dacc_surface, D_levels, 'k--', 'LineWidth', 0.8);
clabel(CS, h, 'Color','k', 'FontSize',8);
end
%% Export
% Hier erzwingen wir die rote Colormap für pgfplots, damit mat2tikz es nicht blau exportiert!
mat2tikz_improved("C:/Users/Silas/Documents/6971e0b65b380ca6d71c837f/02_IMDD_System/tikz/dispersion/dispersion_power_fading_contour2.tikz");
function [lambda_vec, Dacc_vec] = lambda_for_first_null_full(f_target, L, lambda0, S0)
% lambda_for_first_null_full (stable, single-branch + validity checks)
% --------------------------------------------------------------------
% Computes the wavelength(s) at which the first IM/DD fading null
% occurs at frequency/ies f_target using the full dispersion model:
%
% D(lambda) = (S0/4)*(lambda - lambda0^4 / lambda^3)
%
% Restricted to the NORMAL-dispersion branch (λ < λ0),
% and valid only in the O-band (1260–1360 nm).
%
% Inputs:
% f_target - scalar or vector of target null frequencies [Hz]
% L - fiber length [m]
% lambda0 - zero-dispersion wavelength (ZDW) [m]
% S0 - dispersion slope at ZDW [ps/(nm²·km)]
%
% Outputs:
% lambda_vec - wavelength(s) [m] where first null occurs (clamped to O-band)
% Dacc_vec - accumulated dispersion(s) [ps/nm] (NaN if out of valid range)
% --------------------------------------------------------------------
c = physconst('lightspeed');
S0_si = S0 * 1e3; % ps/(nm²·km) -> s/(m³)
% Define O-band boundaries (in meters)
lambda_min = 1255e-9;
lambda_max = 1361e-9;
% Force column vector
f_target = f_target(:);
N = numel(f_target);
lambda_vec = NaN(N,1);
Dacc_vec = NaN(N,1);
for k = 1:N
RHS = c * 0.5 / (f_target(k)^2 * L);
% Normal-dispersion branch (λ < λ0)
fun = @(lambda) -(S0_si/4).*(lambda - (lambda0^4)./(lambda.^3)).*lambda.^2 - RHS;
% Limit the search to [λ_min, λ0)
try
lambda_sol = fzero(fun, [lambda_min, lambda0 * 0.999]);
catch
% If the zero is not within bounds, skip this point
lambda_sol = NaN;
end
% Validate solution
if isnan(lambda_sol) || lambda_sol < lambda_min || lambda_sol > lambda_max
lambda_vec(k) = NaN;
Dacc_vec(k) = NaN;
continue
end
% Compute D(lambda) and accumulated dispersion
D_lambda = (S0_si/4) * (lambda_sol - (lambda0^4)/(lambda_sol^3)) / 1e-6; % ps/(nm·km)
Dacc_val = D_lambda * (L/1000); % ps/nm
% Sanity bound on dispersion (avoid unphysical > ±100 ps/nm)
if abs(Dacc_val) > 100
lambda_vec(k) = NaN;
Dacc_vec(k) = NaN;
else
lambda_vec(k) = lambda_sol;
Dacc_vec(k) = Dacc_val;
end
end
end

142
Functions/Theory/Dissertation/dispersion_contour_bivariate.m Normal file
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@@ -0,0 +1,142 @@
% Festen Betriebsparameter
lambda = 1290; % nm
L = 1; % km
mu_zwd = 1317; % nm
sigma_zwd = 2; % nm
mu_s0 = 0.0872; % ps / nm2 km
sigma_s0 = 0.0012; % ps / nm2 km
rho = -0.5; % Korrelation
% Gitter für lambda0 und S0
lambda0_vec = linspace(mu_zwd-10, mu_zwd+10, 100);
S0_vec = linspace(mu_s0-0.01, mu_s0+0.01, 100);
% Korrigierte meshgrid Reihenfolge
[S0, Lambda0] = meshgrid(S0_vec, lambda0_vec);
% Dispersion berechnen
D = (S0./4) .* ( lambda - (Lambda0.^4)./(lambda^3) ) * L;
% 2D Verteilung (Bivariate Gauss) berechnen
Z_x = (S0 - mu_s0) / sigma_s0;
Z_y = (Lambda0 - mu_zwd) / sigma_zwd;
PDF_2D = exp(-1 / (2 * (1 - rho^2)) * (Z_x.^2 - 2 * rho .* Z_x .* Z_y + Z_y.^2));
%% Plot zusammenbauen
figure('Color','w');
hold on % EINZIGES hold on für den gesamten Plot!
% --- 1. ZUERST: 2D Verteilung (Bivariate Gauss) "ganz unten" ---
numLevels_2D = 6;
colors_2D = cbrewer2('Greys', numLevels_2D+0);
colors_2D = colors_2D(1:numLevels_2D,:);
% Nur die Anzahl der Level übergeben!
[C2, h2] = contourf(S0, Lambda0, PDF_2D, numLevels_2D);
h2.HandleVisibility='off';
% Colormap für die rote Fläche setzen
colormap(gcf, colors_2D(1:end,:));
try
clim([min(PDF_2D(:)), max(PDF_2D(:))]);
catch
caxis([min(PDF_2D(:)), max(PDF_2D(:))]);
end
h2.EdgeColor = 'none'; % Keine schwarzen Ränder
% --- 2. DARÜBER: Dispersions-Konturlinien ---
numLevels_D = 9;
% Erzeuge glatte, auf 1 Nachkommastelle gerundete Werte
levels_D = round(linspace(min(D(:)), max(D(:)), numLevels_D), 1);
levels_D = unique(levels_D);
cmap_bg = flip(cbrewer2('Blues', length(levels_D)+3));
for i = 1:length(levels_D)
% Konturlinien zeichnen (explizit Schwarz)
[C,h] = contour(S0, Lambda0, D, [levels_D(i), levels_D(i)], ...
'Color', cmap_bg(i,:),...
'LineWidth', 1, ...
'ShowText', 'off','handlevisibility','off');
if i == 1
h.HandleVisibility='on';
h.DisplayName='$D(\lambda=1290)$';
end
end
% --- 3. MANUELLE TEXTBOXEN AUF DEN LINIEN ---
% Wähle eine feste S0-Position für alle Beschriftungen (z.B. bei 0.082)
S0_label = 0.095;
for i = 1:length(levels_D)
% Berechne exakte ZDW (Y-Koordinate) durch Umstellen der D-Formel:
% Lambda0 = (lambda^3 * (lambda - 4*D / (S0 * L)))^(1/4)
zdw_label = (lambda^3 * (lambda - 4*levels_D(i) / (S0_label * L)))^0.25;
% Nur zeichnen, wenn der Punkt auch im sichtbaren Plot-Bereich liegt
if zdw_label >= min(lambda0_vec) && zdw_label <= max(lambda0_vec)
text(S0_label, zdw_label, sprintf('%0.1f', levels_D(i)), ...
'Color', 'k', ...
'BackgroundColor', 'w', ... % Weiße Box überdeckt die schwarze Linie!
'Margin', 2, ... % Abstand der Box zum Text
'HorizontalAlignment', 'center', ...
'VerticalAlignment', 'middle', ...
'FontSize', 10);
end
end
% --- 3. GANZ OBEN: Randverteilungen (1D Gauss) an den Achsen ---
% S0 Verteilung (unten)
s0_vals = linspace(min(S0_vec), max(S0_vec), 500);
gauss_s0 = exp(-0.5*((s0_vals - mu_s0)/sigma_s0).^2);
scale_s0 = 4;
y_s0_base = min(lambda0_vec);
plot(s0_vals, y_s0_base + gauss_s0 * scale_s0, 'LineWidth', 1,'LineStyle','--','DisplayName','$S_0$','Color',[0,0,0],'HandleVisibility','off');
% ANNOTATION S0: Automatisch platziert leicht über dem Peak
str_s0 = sprintf('\\mu_{S0} = %.4f\n\\sigma_{S0} = %.4f', mu_s0, sigma_s0);
text(0.0915,1308, str_s0, ...
'Interpreter', 'tex', ...
'HorizontalAlignment', 'left', ... % Entspricht 'right' in TikZ
'VerticalAlignment', 'middle', ...
'BackgroundColor', 'w', ... % Entspricht 'fill=white'
'EdgeColor', 'k', ... % Entspricht 'draw=black'
'Margin', 1, ... % Entspricht 'inner sep=1pt'
'FontSize', 10);
% ZDW Verteilung (links)
% ZDW Verteilung (links)
zwd_vals = linspace(min(lambda0_vec), max(lambda0_vec), 500);
gauss_zwd = exp(-0.5*((zwd_vals - mu_zwd)/sigma_zwd).^2);
scale_zwd = 0.005;
x_zwd_base = min(S0_vec);
plot(x_zwd_base + gauss_zwd * scale_zwd, zwd_vals, 'LineWidth', 1,'LineStyle','--','DisplayName','ZDW','Color',[0,0,0],'HandleVisibility','off');
% ANNOTATION ZDW: Automatisch platziert leicht rechts neben dem Peak
str_zwd = sprintf('\\mu_{ZDW} = %.1f\n\\sigma_{ZDW} = %.1f', mu_zwd, sigma_zwd);
text(0.079,1312, str_zwd, ...
'Interpreter', 'tex', ...
'HorizontalAlignment', 'left', ... % Entspricht 'right' in TikZ
'VerticalAlignment', 'middle', ...
'BackgroundColor', 'w', ... % Entspricht 'fill=white'
'EdgeColor', 'k', ... % Entspricht 'draw=black'
'Margin', 1, ... % Entspricht 'inner sep=1pt'
'FontSize', 10);
% Hilfslinien für die Mittelwerte
% xline(mu_s0,'LineWidth',0.5,'HandleVisibility','off','LineStyle',':');
% yline(mu_zwd,'LineWidth',0.5,'HandleVisibility','off','LineStyle',':');
% --- Achsenbeschriftung, Titel & Formatierung ---
xlabel('$S_0$ [$\nicefrac{\text{ps}}{(\text{nm}^2\text{ km})}$]', 'FontSize', 12);
ylabel('ZDW [nm]', 'FontSize', 12);
% title(sprintf('Dispersion: %d km; %d nm', L, lambda), 'FontSize', 14);
grid on
% Exakte Begrenzung, damit die Randverteilungen bündig anliegen
axis([min(S0_vec) max(S0_vec) min(lambda0_vec) max(lambda0_vec)]);
% legend;
hold off % EINZIGES hold off ganz am Ende!
%% Export
% Hier erzwingen wir die rote Colormap für pgfplots, damit mat2tikz es nicht blau exportiert!
mat2tikz_improved("C:/Users/Silas/Documents/6971e0b65b380ca6d71c837f/02_IMDD_System/tikz/dispersion/dispersion_contour_bi2.tikz");

124
Functions/Theory/Dissertation/photo_diode.m
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@@ -1,124 +0,0 @@
%% ============================================================
% SNR vs Optical Input Power (dBm) — Shot vs Thermal vs Combined
% - Generate optical field as sine with target RMS power (verified)
% - Magnitude-square detection -> optical power
% - Photocurrent = Rd * P
% - Add shot noise + thermal noise (white, PSD-based)
% - Compute SNR for: shot-only, thermal-only, combined
% ============================================================
% Constants
k = Constant.Boltzmann;
q = Constant.ElementaryCharge;
% Receiver / PD parameters
T = 20 + 273.15; % K
R = 50; % Ohm (front-end/load)
Rd = 0.7; % A/W (responsivity)
Be = 100e9; % Hz (electrical noise bandwidth)
Id = 0; % A (dark current, optional)
% Sampling for time-domain demo (needs to be >> Be)
fs = 1e12; % Hz
N = 2^14; % samples
t = (0:N-1).'/fs;
% Choose an electrical tone within bandwidth (arbitrary for demo)
f0 = 10e9; % Hz
% Thermal current PSD (two-sided) and variance in Be
Si_th = 4*k*T/R; % A^2/Hz (two-sided)
sigma2_th = Si_th * Be; % A^2
sigma_th = sqrt(sigma2_th); % A_rms
% Optical input power sweep (in dBm)
P_dBm = linspace(-40, 10, 300);
P_W = 10.^((P_dBm - 30)/10); % W
% Pre-allocate results
SNR_shot_dB = zeros(size(P_dBm));
SNR_th_dB = zeros(size(P_dBm));
SNR_tot_dB = zeros(size(P_dBm));
% --- Main loop over optical input power
for ii = 1:numel(P_W)
Pavg = P_W(ii);
% Optical field with RMS power = Pavg:
% Let x(t) be the optical field amplitude such that |x|^2 has mean Pavg.
% Use a sinusoid: x(t) = A*sin(2*pi*f0*t), then mean(|x|^2)=A^2/2.
A = sqrt(2*Pavg); % -> mean(|x|^2) = Pavg
x = A * sin(2*pi*f0*t); % "optical field" (real for simplicity)
% Verify RMS/mean power numerically (optional)
P_meas = mean(abs(x).^2); % should be ~ Pavg
a = P_meas - Pavg;
assert(a<1);
% Magnitude-square detection -> optical power waveform
Popt = abs(x).^2; % W (instantaneous)
% Photocurrent waveform (includes DC + 2f0 component for this demo)
I_sig = Rd * Popt; % A
% Define "signal power" as the mean-squared photocurrent due to signal
% (for this sine-squared waveform, it's OK for a demo SNR definition)
P_sig = mean(I_sig.^2);
% -------- Shot noise (white) --------
% Two-sided PSD: Si_shot = 2*q*(I_photo + I_dark)
% For this demo, use average current to set the white-noise level:
Ibar = mean(I_sig) + Id;
Si_shot = 2*q*Ibar; % A^2/Hz
sigma2_sh = Si_shot * Be; % A^2
sigma_sh = sqrt(sigma2_sh); % A_rms
n_sh = sigma_sh * randn(N,1); % time-domain shot noise
% -------- Thermal noise (white Gaussian) --------
n_th = sigma_th * randn(N,1); % time-domain thermal noise
% Noise powers (mean-square) in time domain
Pn_sh = mean(n_sh.^2);
Pn_th = mean(n_th.^2);
Pn_tot = mean((n_sh + n_th).^2); % ~ Pn_sh + Pn_th (independent)
% SNRs
SNR_shot = P_sig / Pn_sh;
SNR_th = P_sig / Pn_th;
SNR_tot = P_sig / Pn_tot;
SNR_shot_dB(ii) = 10*log10(SNR_shot);
SNR_th_dB(ii) = 10*log10(SNR_th);
SNR_tot_dB(ii) = 10*log10(SNR_tot);
% Optional sanity check (can comment out)
% if ii == 1
% fprintf('Check Pavg target/meas: %.3g W / %.3g W\n', Pavg, P_meas);
% end
end
% Plot comparison
figure(1); clf; hold on;
plot(P_dBm, SNR_shot_dB, 'LineWidth', 1.5);
plot(P_dBm, SNR_th_dB, 'LineWidth', 1.5);
plot(P_dBm, SNR_tot_dB, 'LineWidth', 1.5);
grid on;
xlabel('Optical input power [dBm]');
ylabel('SNR [dB]');
title('SNR vs Optical Input Power: Shot vs Thermal vs Combined');
legend('Shot-noise only','Thermal-noise only','Shot + Thermal','Location','best');
% Print example at 0 dBm
[~,idx0] = min(abs(P_dBm - 0));
fprintf('At 0 dBm:\n');
fprintf(' SNR (shot only) = %.2f dB\n', SNR_shot_dB(idx0));
fprintf(' SNR (thermal only) = %.2f dB\n', SNR_th_dB(idx0));
fprintf(' SNR (combined) = %.2f dB\n', SNR_tot_dB(idx0));
% Also print NEP based on thermal PSD (constant NEP_th)
NEP_th = sqrt(Si_th)/Rd; % W/sqrt(Hz)
fprintf('Thermal NEP = %.3g W/sqrt(Hz)\n', NEP_th);

25
Functions/Theory/power_fading.m → Functions/Theory/Dissertation/power_fading.m
View File

@@ -17,8 +17,8 @@ D_si = D_lambda * 1e-6; % → s/m²
b2 = -D_si * lambda^2 / (2*pi*c); % s²/m
%% Frequency grid
f_max = 150e9;
f = linspace(0, f_max, 4000); % [Hz]
f_max = 200e9;
f = linspace(0, f_max, 5000); % [Hz]
%% IM/DD transfer function (power fading)
phi = 2*pi^2 * b2 * f.^2 * L;
@@ -26,17 +26,22 @@ H = abs(cos(phi));
%% Plot
figure('Color','w');
plot(f/1e9, 10*log10(H), 'LineWidth', 1.8,'Color','black');
plot(f/1e9, 10*log10(H), 'LineWidth', 1,'Color','black');
grid on; box on;
xlabel('Frequency [GHz]');
ylabel('Magnitude [dB]');
% title(sprintf('IM/DD Power Fading: 10 km; 1275nm', lambda*1e9, L/1000),"Interpreter","latex");
ylim([-30 0]);
ylim([-20 0]);
%% Mark analytic first-null frequency
f_null = sqrt(c*(0.5)/(abs(D_si)*lambda^2*L));
xline(f_null/1e9, 'r--', 'LineWidth', 1.2, ...
'Label', sprintf('f_{null}=%.1f GHz', f_null/1e9), ...
'LabelOrientation', 'horizontal', 'LabelVerticalAlignment', 'bottom');
%% Mark analytic null frequencies up to order 5
max_order = 3;
for n = 0:max_order
f_null_n = sqrt( c*(2*n + 1)/(2*abs(D_si)*lambda^2*L) );
xline(f_null_n/1e9, '--', 'LineWidth', 1.2, ...
'Color', [0.1216, 0.4706, 0.7059]);
text(f_null_n/1e9, -15, sprintf('$f_{\\mathrm{null}, %d}=%.1f$ GHz', n, f_null_n/1e9), ...
'BackgroundColor', 'w', 'EdgeColor', 'k', 'Interpreter', 'latex', ...
'HorizontalAlignment', 'center', 'VerticalAlignment', 'middle');
end
mat2tikz_improved('C:\Users\Silas\Documents\6971e0b65b380ca6d71c837f\02_IMDD_System\tikz\dispersion\power_fading.tikz')
% mat2tikz_improved('C:\Users\Silas\Documents\6971e0b65b380ca6d71c837f\02_IMDD_System\tikz\dispersion\power_fading.tikz')

96
Functions/Theory/dispersion_contour.m
View File

@@ -1,55 +1,77 @@
% Gitter für lambda0 und S0
lambda0_vec = linspace(1260,1360,200);
S0_vec = linspace(0.06,0.1,200);
[Lambda0, S0] = meshgrid(lambda0_vec, S0_vec);
% Festen Betriebsparameter
lambda = 1293; % nm
lambda = 1290; % nm
L = 1; % km
% Dispersion berechnen (lineare Näherung)
D = S0 .* ( lambda - Lambda0 ) * L;
mu_zwd = 1317; % nm
sigma_zwd = 2; % nm
mu_s0 = 0.0872; % ps / nm2 km
sigma_s0 = 0.0012; % ps / nm2 km
rho = -0.5; % Korrelation
% Gitter für lambda0 und S0
lambda0_vec = linspace(mu_zwd-10, mu_zwd+10, 100);
S0_vec = linspace(mu_s0-0.01, mu_s0+0.01, 100);
% Korrigierte meshgrid Reihenfolge
[S0, Lambda0] = meshgrid(S0_vec, lambda0_vec);
% Dispersion berechnen
D = (S0./4) .* ( lambda - (Lambda0.^4)./(lambda^3) ) * L;
%% 2D-Konturplot nur mit Linien und Text
%% 2D-Konturplot
figure('Color','w');
hold on
% Konturlinien
numLevels = 10;
levels = linspace(min(D(:)), max(D(:)), numLevels);
[C,h] = contour(S0, Lambda0, D, levels, ...
'LineWidth',1.5, ...
'ShowText','on', ...
'LabelFormat','%0.1f');
% --- 1. Hintergrund: Dispersions-Konturlinien (Gerundet für TikZ) ---
numLevels_D = 10;
% Erzeuge glatte, auf 1 Nachkommastelle gerundete Werte
% cbrewer2-Colormap für die Linien
cmap = cbrewer2('div','RdYlGn', numLevels);
colormap(cmap);
levels_D = round(linspace(min(D(:)), max(D(:)), numLevels_D), 1);
levels_D = unique(levels_D); % Falls durch Rundung doppelte Werte entstehen
% Achsenlinien
% yline(1310, '--k','ZDW_{mean}','LabelVerticalAlignment','top','LabelHorizontalAlignment','center');
x0 = 0.09;
% xline(x0, '--k','S_{0}','LabelHorizontalAlignment','left');
% Colormap in der Länge der verbliebenen Level erstellen
cmap_bg = cbrewer2('Blues', length(levels_D));
for i = 1:length(levels_D)
% WICHTIG: Das Level als [Wert, Wert] übergeben!
[C,h] = contour(S0, Lambda0, D, [levels_D(i), levels_D(i)], ...
'Color', cmap_bg(i,:),...
'LineWidth', 1.5, ...
'ShowText', 'on', ...
'LabelFormat', '%0.1f');
h.LabelColor = [0,0,0];
end
% --- NEU: Parameter für die Verteilungen ---
mu_zwd = 1317; % nm
sigma_zwd = 2; % nm
mu_s0 = 0.0872; % ps / nm2 km
sigma_s0 = 0.0012; % ps / nm2 km
rho = -0.5; % Korrelation
% --- 3. Randverteilungen (1D Gauss) an den Achsen ---
s0_vals = linspace(min(S0_vec), max(S0_vec), 500);
gauss_s0 = exp(-0.5*((s0_vals - mu_s0)/sigma_s0).^2);
scale_s0 = 4; % Skalierung für die Höhe in der Ansicht
plot(s0_vals, min(lambda0_vec) + gauss_s0 * scale_s0, 'k', 'LineWidth', 2);
% Gaussian auf der x-Linie (S0 = 0.09)
mu_zwd = 1310; % nm
sigma_zwd = 2; % nm
zwd_vals = linspace(min(lambda0_vec), max(lambda0_vec), 500);
% PDF berechnen
gauss_pdf = (1/(sigma_zwd*sqrt(2*pi))) * exp(-0.5*((zwd_vals-mu_zwd)/sigma_zwd).^2);
% Normieren und auf eine sichtbare Breite skalieren
scale = 0.005; % passt die Maximal-Auslenkung in x-Richtung an
x_gauss = x0 + (gauss_pdf/max(gauss_pdf)) * scale;
gauss_zwd = exp(-0.5*((zwd_vals - mu_zwd)/sigma_zwd).^2);
scale_zwd = 0.005; % Skalierung für die Auslenkung in der Ansicht
plot(min(S0_vec) + gauss_zwd * scale_zwd, zwd_vals, 'k', 'LineWidth', 2);
% Plot
% plot(x_gauss, zwd_vals, 'LineWidth',2);
% Hilfslinien für die Mittelwerte
xline(mu_s0, '--k', 'Alpha', 0.4);
yline(mu_zwd, '--k', 'Alpha', 0.4);
% Achsenbeschriftung & Titel
% Achsenbeschriftung & Titel
xlabel('S0 [ps / nm2 km]', 'FontSize', 12);
% --- Achsenbeschriftung, Titel & Formatierung ---
xlabel('S0 ', 'FontSize', 12);
ylabel('ZDW [nm]', 'FontSize', 12);
title (sprintf('Dispersion: %d km; %d nm', L, lambda), 'FontSize', 14);
% title(sprintf('Dispersion: %d km; %d nm', L, lambda), 'FontSize', 14);
axis([min(S0_vec) max(S0_vec) min(lambda0_vec) max(lambda0_vec)]);
grid on
hold off
%% Für den LaTeX Export
mat2tikz_improved("C:/Users/Silas/Documents/6971e0b65b380ca6d71c837f/02_IMDD_System/tikz/dispersion/dispersion_contour.tikz")

146
Functions/Theory/dispersion_contour_bandwidth_lambda.m
View File

@@ -1,146 +0,0 @@
%% ------------------------------------------------------------
% Contour plot: λ_null as function of bandwidth (f_target) and reach (L)
% ------------------------------------------------------------
% Parameters
lambda0 = 1310e-9; % [m]
S0 = 0.08; % [ps/(nm²·km)]
c = physconst('lightspeed');
% Sweep dimensions
f_targets = linspace(50e9, 120e9, 100); % [Hz] (x-axis)
L_values = linspace(0.5e3, 10e3, 100); % [m] (y-axis)
lambda_surface = zeros(numel(L_values), numel(f_targets));
Dacc_surface = zeros(numel(L_values), numel(f_targets));
% Outer loop over fiber length (since L must be scalar)
for iL = 1:numel(L_values)
L = L_values(iL);
[lambda_vec, Dacc_vec] = lambda_for_first_null_full(f_targets, L, lambda0, S0);
lambda_vec = 2*abs(lambda0 - lambda_vec);
if 0
fprintf('\n- %d km ------------------------------------\n',L);
fprintf(' f_null [GHz] lambda [nm] Dacc [ps/nm]\n');
fprintf('----------------------------------------------\n');
fprintf('%10.1f %8.2f %+8.3f\n',[f_targets(:)/1e9, lambda_vec(:)*1e9, Dacc_vec(:)].');
fprintf('----------------------------------------------\n\n');
end
lambda_surface(iL, :) = lambda_vec; % λ for each f_target
Dacc_surface(iL, :) = Dacc_vec; % corresponding accumulated dispersion
end
% Convert for plotting
lambda_surface_nm = lambda_surface * 1e9; % [nm]
L_km = L_values / 1000; % [km]
f_GHz = f_targets / 1e9; % [GHz]
%% Contour plot
figure('Color','w');
% Define wavelength contour levels [nm]
lambda_levels = [1260:10:1290, 1290:5:1300, 1300:2.5:1310];
lambda_levels = [100:-20:50, 50:-10:30,30:-5:0];
% Contour plot
contour(f_GHz, L_km, lambda_surface_nm, lambda_levels, ...
'LineWidth', 1.5, ...
'ShowText', 'on', ...
'LabelFormat', '%.0f nm');
% Colormap and colorbar
colormap((cbrewer2('RdYlGn',100)));
colorbar;
clim([0 100]);
% Axis formatting
xlabel('Signal Bandwidth [GHz]');
ylabel('Fiber length [km]');
legend('$\Delta \lambda$')
% X-axis ticks at 56 : 16 : 150 GHz
xticks(56:8:150);
grid on; box on;
%% Optional: overlay accumulated-dispersion contours
if 0
hold on;
[CS, h] = contour(f_GHz, L_km, Dacc_surface, 10, 'k--', 'LineWidth', 0.8);
clabel(CS, h, 'Color','k', 'FontSize',8);
end
function [lambda_vec, Dacc_vec] = lambda_for_first_null_full(f_target, L, lambda0, S0)
% lambda_for_first_null_full (stable, single-branch + validity checks)
% --------------------------------------------------------------------
% Computes the wavelength(s) at which the first IM/DD fading null
% occurs at frequency/ies f_target using the full dispersion model:
%
% D(lambda) = (S0/4)*(lambda - lambda0^4 / lambda^3)
%
% Restricted to the NORMAL-dispersion branch (λ < λ0),
% and valid only in the O-band (1260–1360 nm).
%
% Inputs:
% f_target - scalar or vector of target null frequencies [Hz]
% L - fiber length [m]
% lambda0 - zero-dispersion wavelength (ZDW) [m]
% S0 - dispersion slope at ZDW [ps/(nm²·km)]
%
% Outputs:
% lambda_vec - wavelength(s) [m] where first null occurs (clamped to O-band)
% Dacc_vec - accumulated dispersion(s) [ps/nm] (NaN if out of valid range)
% --------------------------------------------------------------------
c = physconst('lightspeed');
S0_si = S0 * 1e3; % ps/(nm²·km) -> s/(m³)
% Define O-band boundaries (in meters)
lambda_min = 1255e-9;
lambda_max = 1361e-9;
% Force column vector
f_target = f_target(:);
N = numel(f_target);
lambda_vec = NaN(N,1);
Dacc_vec = NaN(N,1);
for k = 1:N
RHS = c * 0.5 / (f_target(k)^2 * L);
% Normal-dispersion branch (λ < λ0)
fun = @(lambda) -(S0_si/4).*(lambda - (lambda0^4)./(lambda.^3)).*lambda.^2 - RHS;
% Limit the search to [λ_min, λ0)
try
lambda_sol = fzero(fun, [lambda_min, lambda0 * 0.999]);
catch
% If the zero is not within bounds, skip this point
lambda_sol = NaN;
end
% Validate solution
if isnan(lambda_sol) || lambda_sol < lambda_min || lambda_sol > lambda_max
lambda_vec(k) = NaN;
Dacc_vec(k) = NaN;
continue
end
% Compute D(lambda) and accumulated dispersion
D_lambda = (S0_si/4) * (lambda_sol - (lambda0^4)/(lambda_sol^3)) / 1e-6; % ps/(nm·km)
Dacc_val = D_lambda * (L/1000); % ps/nm
% Sanity bound on dispersion (avoid unphysical > ±100 ps/nm)
if abs(Dacc_val) > 100
lambda_vec(k) = NaN;
Dacc_vec(k) = NaN;
else
lambda_vec(k) = lambda_sol;
Dacc_vec(k) = Dacc_val;
end
end
end

39
Functions/mat2tikz_improved.m
View File

@@ -5,19 +5,34 @@ arguments
end
cleanfigure;
matlab2tikz(char(filename), ...
'width','\fwidth', ...
'height','\fheight', ...
'showInfo',false, ...
'extraAxisOptions',{ ...
'legend style={font=\footnotesize}', ...
'xlabel style={font=\color{white!15!black},font=\small},',...
'ylabel style={font=\color{white!15!black},font=\small},',...
'width', '\fwidth', ...
'height', '\fheight', ...
'showInfo', false, ...
'extraAxisOptions', { ...
'xlabel style={font=\color{white!15!black}\small}', ...
'ylabel style={font=\color{white!15!black}\small}', ...
'scaled ticks=false', ...
'tick label style={/pgf/number format/fixed, /pgf/number format/1000 sep={}}', ...
'every axis/.append style={font=\scriptsize}', ...
'legend columns=1', ...
'every axis/.append style={font=\scriptsize}',...
'legend columns=1',...
'legend style={at={(0.02,0.98)},font=\footnotesize,draw=black!60,rounded corners=2pt,inner sep=1pt,fill=white,column sep=6pt,anchor= north west}',...
'legend style={at={(0.02,0.98)},draw=white!0!white,font=\scriptsize,inner sep=0.1pt,fill=white,column sep=1pt,anchor= north west}',...
'every axis/.append style={font=\scriptsize}',...
'legend style={at={(0.02,0.98)}, anchor=north west, font=\scriptsize, draw=black!100, rounded corners=2pt, inner sep=1pt, fill=white, column sep=2pt}' ...
});
% matlab2tikz(char(filename), ...
% 'width','\fwidth', ...
% 'height','\fheight', ...
% 'showInfo',false, ...
% 'extraAxisOptions',{ ...
% 'legend style={font=\footnotesize}', ...
% 'xlabel style={font=\color{white!15!black},font=\small},',...
% 'ylabel style={font=\color{white!15!black},font=\small},',...
% 'legend columns=1', ...
% 'every axis/.append style={font=\scriptsize}',...
% 'legend columns=1',...
% 'legend style={at={(0.02,0.98)},font=\footnotesize,draw=black!60,rounded corners=2pt,inner sep=1pt,fill=white,column sep=6pt,anchor= north west}',...
% 'legend style={at={(0.02,0.98)},draw=white!0!white,font=\scriptsize,inner sep=0.1pt,fill=white,column sep=1pt,anchor= north west}',...
% 'every axis/.append style={font=\scriptsize}',...
% 'scaled ticks=false', ...
% 'tick label style={/pgf/number format/fixed, /pgf/number format/1000 sep={}}' ...
% });
end

11
Tests/02_optical/DP_Fiber_test.m Normal file
View File

@@ -0,0 +1,11 @@
classdef DP_Fiber_test < matlab.unittest.TestCase
% Test class for DP_Fiber
methods (Test)
function testExample(testCase)
% Example test case for DP_Fiber
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/02_optical/Optical_Demultiplex_test.m Normal file
View File

@@ -0,0 +1,11 @@
classdef Optical_Demultiplex_test < matlab.unittest.TestCase
% Test class for Optical_Demultiplex
methods (Test)
function testExample(testCase)
% Example test case for Optical_Demultiplex
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/02_optical/Optical_Multiplex_test.m Normal file
View File

@@ -0,0 +1,11 @@
classdef Optical_Multiplex_test < matlab.unittest.TestCase
% Test class for Optical_Multiplex
methods (Test)
function testExample(testCase)
% Example test case for Optical_Multiplex
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/02_optical/Polarization_Controller_test.m Normal file
View File

@@ -0,0 +1,11 @@
classdef Polarization_Controller_test < matlab.unittest.TestCase
% Test class for Polarization_Controller
methods (Test)
function testExample(testCase)
% Example test case for Polarization_Controller
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/02_optical/dp_fiber_lib/CNLSE_plain_test.m Normal file
View File

@@ -0,0 +1,11 @@
classdef CNLSE_plain_test < matlab.unittest.TestCase
% Test class for CNLSE_plain
methods (Test)
function testExample(testCase)
% Example test case for CNLSE_plain
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/02_optical/dp_fiber_lib/CNLSE_test.m Normal file
View File

@@ -0,0 +1,11 @@
classdef CNLSE_test < matlab.unittest.TestCase
% Test class for CNLSE
methods (Test)
function testExample(testCase)
% Example test case for CNLSE
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/02_optical/dp_fiber_lib/canUseGPU_test.m Normal file
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@@ -0,0 +1,11 @@
classdef canUseGPU_test < matlab.unittest.TestCase
% Test class for canUseGPU
methods (Test)
function testExample(testCase)
% Example test case for canUseGPU
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/02_optical/dp_fiber_lib/getNLstepsize_original_test.m Normal file
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@@ -0,0 +1,11 @@
classdef getNLstepsize_original_test < matlab.unittest.TestCase
% Test class for getNLstepsize_original
methods (Test)
function testExample(testCase)
% Example test case for getNLstepsize_original
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/02_optical/dp_fiber_lib/getNLstepsize_test.m Normal file
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@@ -0,0 +1,11 @@
classdef getNLstepsize_test < matlab.unittest.TestCase
% Test class for getNLstepsize
methods (Test)
function testExample(testCase)
% Example test case for getNLstepsize
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/02_optical/dp_fiber_lib/lin_step_original_test.m Normal file
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@@ -0,0 +1,11 @@
classdef lin_step_original_test < matlab.unittest.TestCase
% Test class for lin_step_original
methods (Test)
function testExample(testCase)
% Example test case for lin_step_original
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/02_optical/dp_fiber_lib/lin_step_test.m Normal file
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@@ -0,0 +1,11 @@
classdef lin_step_test < matlab.unittest.TestCase
% Test class for lin_step
methods (Test)
function testExample(testCase)
% Example test case for lin_step
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/02_optical/dp_fiber_lib/nl_step_original_test.m Normal file
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@@ -0,0 +1,11 @@
classdef nl_step_original_test < matlab.unittest.TestCase
% Test class for nl_step_original
methods (Test)
function testExample(testCase)
% Example test case for nl_step_original
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/02_optical/dp_fiber_lib/nl_step_test.m Normal file
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@@ -0,0 +1,11 @@
classdef nl_step_test < matlab.unittest.TestCase
% Test class for nl_step
methods (Test)
function testExample(testCase)
% Example test case for nl_step
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/02_optical/dp_fiber_lib/split_step_loop_test.m Normal file
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@@ -0,0 +1,11 @@
classdef split_step_loop_test < matlab.unittest.TestCase
% Test class for split_step_loop
methods (Test)
function testExample(testCase)
% Example test case for split_step_loop
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/04_DSP/CIC_filter_test.m Normal file
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@@ -0,0 +1,11 @@
classdef CIC_filter_test < matlab.unittest.TestCase
% Test class for CIC_filter
methods (Test)
function testExample(testCase)
% Example test case for CIC_filter
% Add your test code here
testCase.verifyTrue(true);
end
end
end

2
Tests/04_DSP/Coding/Duobinary_test.m
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@@ -12,7 +12,7 @@ classdef Duobinary_test < matlab.unittest.TestCase
properties (MethodSetupParameter)
% Define method-level parameters for PRBS and bit pattern
% i.e.: useprbs = {0,1};
useprbs = struct('true', 1); % variations: {0, 1}
useprbs = struct('true', 0); % variations: {0, 1}
M = struct('M2', 2, 'M4', 4, 'M6', 6, 'M8', 8); % variations: {2, 4, 6, 8}
O = struct('O10', 10,'O11', 11,'O12', 12,'O13', 13, 'O15', 15, 'O17', 17, 'O18', 18, 'O19', 19); % variations: {10, 15, 17}
end

11
Tests/04_DSP/Equalizer/FFE_DCremoval_adaptive_mu_test.m Normal file
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@@ -0,0 +1,11 @@
classdef FFE_DCremoval_adaptive_mu_test < matlab.unittest.TestCase
% Test class for FFE_DCremoval_adaptive_mu
methods (Test)
function testExample(testCase)
% Example test case for FFE_DCremoval_adaptive_mu
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/04_DSP/Equalizer/FFE_DCremoval_level_test.m Normal file
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@@ -0,0 +1,11 @@
classdef FFE_DCremoval_level_test < matlab.unittest.TestCase
% Test class for FFE_DCremoval_level
methods (Test)
function testExample(testCase)
% Example test case for FFE_DCremoval_level
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/04_DSP/Equalizer/FFE_Kalman_Feedback_test.m Normal file
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@@ -0,0 +1,11 @@
classdef FFE_Kalman_Feedback_test < matlab.unittest.TestCase
% Test class for FFE_Kalman_Feedback
methods (Test)
function testExample(testCase)
% Example test case for FFE_Kalman_Feedback
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/04_DSP/Equalizer/FFE_Kalman_test.m Normal file
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@@ -0,0 +1,11 @@
classdef FFE_Kalman_test < matlab.unittest.TestCase
% Test class for FFE_Kalman
methods (Test)
function testExample(testCase)
% Example test case for FFE_Kalman
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/04_DSP/Equalizer/FFE_MLSE_test.m Normal file
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@@ -0,0 +1,11 @@
classdef FFE_MLSE_test < matlab.unittest.TestCase
% Test class for FFE_MLSE
methods (Test)
function testExample(testCase)
% Example test case for FFE_MLSE
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/04_DSP/Equalizer/ML_MLSE_GPU_test.m Normal file
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@@ -0,0 +1,11 @@
classdef ML_MLSE_GPU_test < matlab.unittest.TestCase
% Test class for ML_MLSE_GPU
methods (Test)
function testExample(testCase)
% Example test case for ML_MLSE_GPU
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/04_DSP/Equalizer/ML_MLSE_test.m Normal file
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@@ -0,0 +1,11 @@
classdef ML_MLSE_test < matlab.unittest.TestCase
% Test class for ML_MLSE
methods (Test)
function testExample(testCase)
% Example test case for ML_MLSE
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/04_DSP/Equalizer/Postfilter_test.m Normal file
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@@ -0,0 +1,11 @@
classdef Postfilter_test < matlab.unittest.TestCase
% Test class for Postfilter
methods (Test)
function testExample(testCase)
% Example test case for Postfilter
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/04_DSP/Sequence Detection/MLSE_new_test.m Normal file
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@@ -0,0 +1,11 @@
classdef MLSE_new_test < matlab.unittest.TestCase
% Test class for MLSE_new
methods (Test)
function testExample(testCase)
% Example test case for MLSE_new
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/04_DSP/Sequence Detection/MLSE_viterbi_test.m Normal file
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@@ -0,0 +1,11 @@
classdef MLSE_viterbi_test < matlab.unittest.TestCase
% Test class for MLSE_viterbi
methods (Test)
function testExample(testCase)
% Example test case for MLSE_viterbi
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/04_DSP/Timing Recovery/Godard_Timing_Recovery_test.m Normal file
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@@ -0,0 +1,11 @@
classdef Godard_Timing_Recovery_test < matlab.unittest.TestCase
% Test class for Godard_Timing_Recovery
methods (Test)
function testExample(testCase)
% Example test case for Godard_Timing_Recovery
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/04_DSP/Timing Recovery/MaxVar_Timing_Recovery_test.m Normal file
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@@ -0,0 +1,11 @@
classdef MaxVar_Timing_Recovery_test < matlab.unittest.TestCase
% Test class for MaxVar_Timing_Recovery
methods (Test)
function testExample(testCase)
% Example test case for MaxVar_Timing_Recovery
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/04_DSP/Timing Recovery/Time_Shifter_test.m Normal file
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@@ -0,0 +1,11 @@
classdef Time_Shifter_test < matlab.unittest.TestCase
% Test class for Time_Shifter
methods (Test)
function testExample(testCase)
% Example test case for Time_Shifter
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/04_DSP/Timing Recovery/Timing_Recovery_GPT_test.m Normal file
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@@ -0,0 +1,11 @@
classdef Timing_Recovery_GPT_test < matlab.unittest.TestCase
% Test class for Timing_Recovery_GPT
methods (Test)
function testExample(testCase)
% Example test case for Timing_Recovery_GPT
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/04_DSP/Timing Recovery/Timing_Recovery_Move_It_test.m Normal file
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@@ -0,0 +1,11 @@
classdef Timing_Recovery_Move_It_test < matlab.unittest.TestCase
% Test class for Timing_Recovery_Move_It
methods (Test)
function testExample(testCase)
% Example test case for Timing_Recovery_Move_It
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/04_DSP/Timing Recovery/Timing_Recovery_test.m Normal file
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@@ -0,0 +1,11 @@
classdef Timing_Recovery_test < matlab.unittest.TestCase
% Test class for Timing_Recovery
methods (Test)
function testExample(testCase)
% Example test case for Timing_Recovery
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/04_DSP/Timing_Recovery_test.m Normal file
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@@ -0,0 +1,11 @@
classdef Timing_Recovery_test < matlab.unittest.TestCase
% Test class for Timing_Recovery
methods (Test)
function testExample(testCase)
% Example test case for Timing_Recovery
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/04_DSP/TransmissionPerformance_test.m Normal file
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@@ -0,0 +1,11 @@
classdef TransmissionPerformance_test < matlab.unittest.TestCase
% Test class for TransmissionPerformance
methods (Test)
function testExample(testCase)
% Example test case for TransmissionPerformance
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/05_Lab/Awg2Scope_test.m Normal file
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@@ -0,0 +1,11 @@
classdef Awg2Scope_test < matlab.unittest.TestCase
% Test class for Awg2Scope
methods (Test)
function testExample(testCase)
% Example test case for Awg2Scope
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/05_Lab/AwgKeysight_test.m Normal file
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@@ -0,0 +1,11 @@
classdef AwgKeysight_test < matlab.unittest.TestCase
% Test class for AwgKeysight
methods (Test)
function testExample(testCase)
% Example test case for AwgKeysight
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/05_Lab/Exfo_laser_test.m Normal file
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@@ -0,0 +1,11 @@
classdef Exfo_laser_test < matlab.unittest.TestCase
% Test class for Exfo_laser
methods (Test)
function testExample(testCase)
% Example test case for Exfo_laser
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/05_Lab/LabDeviceTemplate_test.m Normal file
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@@ -0,0 +1,11 @@
classdef LabDeviceTemplate_test < matlab.unittest.TestCase
% Test class for LabDeviceTemplate
methods (Test)
function testExample(testCase)
% Example test case for LabDeviceTemplate
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/05_Lab/OSA_Advantest_test.m Normal file
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@@ -0,0 +1,11 @@
classdef OSA_Advantest_test < matlab.unittest.TestCase
% Test class for OSA_Advantest
methods (Test)
function testExample(testCase)
% Example test case for OSA_Advantest
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/05_Lab/OptAtten_test.m Normal file
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@@ -0,0 +1,11 @@
classdef OptAtten_test < matlab.unittest.TestCase
% Test class for OptAtten
methods (Test)
function testExample(testCase)
% Example test case for OptAtten
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/05_Lab/OptLaserN7714A_test.m Normal file
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@@ -0,0 +1,11 @@
classdef OptLaserN7714A_test < matlab.unittest.TestCase
% Test class for OptLaserN7714A
methods (Test)
function testExample(testCase)
% Example test case for OptLaserN7714A
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/05_Lab/OptPowerMeter8153A_test.m Normal file
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@@ -0,0 +1,11 @@
classdef OptPowerMeter8153A_test < matlab.unittest.TestCase
% Test class for OptPowerMeter8153A
methods (Test)
function testExample(testCase)
% Example test case for OptPowerMeter8153A
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/05_Lab/ScopeKeysight_test.m Normal file
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@@ -0,0 +1,11 @@
classdef ScopeKeysight_test < matlab.unittest.TestCase
% Test class for ScopeKeysight
methods (Test)
function testExample(testCase)
% Example test case for ScopeKeysight
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/05_Lab/Thor_PDFA_test.m Normal file
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@@ -0,0 +1,11 @@
classdef Thor_PDFA_test < matlab.unittest.TestCase
% Test class for Thor_PDFA
methods (Test)
function testExample(testCase)
% Example test case for Thor_PDFA
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/DataBaseHandler/DBHandler_test.m Normal file
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@@ -0,0 +1,11 @@
classdef DBHandler_test < matlab.unittest.TestCase
% Test class for DBHandler
methods (Test)
function testExample(testCase)
% Example test case for DBHandler
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/DataBaseHandler/Equalizerstruct_test.m Normal file
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@@ -0,0 +1,11 @@
classdef Equalizerstruct_test < matlab.unittest.TestCase
% Test class for Equalizerstruct
methods (Test)
function testExample(testCase)
% Example test case for Equalizerstruct
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/DataBaseHandler/Metricstruct_test.m Normal file
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@@ -0,0 +1,11 @@
classdef Metricstruct_test < matlab.unittest.TestCase
% Test class for Metricstruct
methods (Test)
function testExample(testCase)
% Example test case for Metricstruct
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/DataBaseHandler/QueryFilter_test.m Normal file
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@@ -0,0 +1,11 @@
classdef QueryFilter_test < matlab.unittest.TestCase
% Test class for QueryFilter
methods (Test)
function testExample(testCase)
% Example test case for QueryFilter
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/DataBaseHandler/SqlFilter_test.m Normal file
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@@ -0,0 +1,11 @@
classdef SqlFilter_test < matlab.unittest.TestCase
% Test class for SqlFilter
methods (Test)
function testExample(testCase)
% Example test case for SqlFilter
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/DataBaseHandler/SqlOperator_test.m Normal file
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@@ -0,0 +1,11 @@
classdef SqlOperator_test < matlab.unittest.TestCase
% Test class for SqlOperator
methods (Test)
function testExample(testCase)
% Example test case for SqlOperator
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/DataBaseHandler/related functions/cleanUpTable_test.m Normal file
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@@ -0,0 +1,11 @@
classdef cleanUpTable_test < matlab.unittest.TestCase
% Test class for cleanUpTable
methods (Test)
function testExample(testCase)
% Example test case for cleanUpTable
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/DataBaseHandler/related functions/groupIt_test.m Normal file
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@@ -0,0 +1,11 @@
classdef groupIt_test < matlab.unittest.TestCase
% Test class for groupIt
methods (Test)
function testExample(testCase)
% Example test case for groupIt
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/DataBaseHandler/related functions/removeGroupOutliers_test.m Normal file
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@@ -0,0 +1,11 @@
classdef removeGroupOutliers_test < matlab.unittest.TestCase
% Test class for removeGroupOutliers
methods (Test)
function testExample(testCase)
% Example test case for removeGroupOutliers
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/GifWriter_test.m Normal file
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@@ -0,0 +1,11 @@
classdef GifWriter_test < matlab.unittest.TestCase
% Test class for GifWriter
methods (Test)
function testExample(testCase)
% Example test case for GifWriter
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/Moveit_wrapper_test.m Normal file
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@@ -0,0 +1,11 @@
classdef Moveit_wrapper_test < matlab.unittest.TestCase
% Test class for Moveit_wrapper
methods (Test)
function testExample(testCase)
% Example test case for Moveit_wrapper
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/Warehouse_class/classes/DataStorage_test.m Normal file
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@@ -0,0 +1,11 @@
classdef DataStorage_test < matlab.unittest.TestCase
% Test class for DataStorage
methods (Test)
function testExample(testCase)
% Example test case for DataStorage
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/Warehouse_class/classes/Parameter_test.m Normal file
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@@ -0,0 +1,11 @@
classdef Parameter_test < matlab.unittest.TestCase
% Test class for Parameter
methods (Test)
function testExample(testCase)
% Example test case for Parameter
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/Warehouse_class/classes/StorageParameter_test.m Normal file
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@@ -0,0 +1,11 @@
classdef StorageParameter_test < matlab.unittest.TestCase
% Test class for StorageParameter
methods (Test)
function testExample(testCase)
% Example test case for StorageParameter
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/Warehouse_class/classes/minimal_warehouse_example_test.m Normal file
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@@ -0,0 +1,11 @@
classdef minimal_warehouse_example_test < matlab.unittest.TestCase
% Test class for minimal_warehouse_example
methods (Test)
function testExample(testCase)
% Example test case for minimal_warehouse_example
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/Warehouse_class/functions/interpCurve_test.m Normal file
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@@ -0,0 +1,11 @@
classdef interpCurve_test < matlab.unittest.TestCase
% Test class for interpCurve
methods (Test)
function testExample(testCase)
% Example test case for interpCurve
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/Warehouse_class/functions/phase_predist_plots/build_wh_test.m Normal file
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@@ -0,0 +1,11 @@
classdef build_wh_test < matlab.unittest.TestCase
% Test class for build_wh
methods (Test)
function testExample(testCase)
% Example test case for build_wh
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/Warehouse_class/functions/phase_predist_plots/fwm_plots/CompleteRoutine_DifferentChannels_Fig3_test.m Normal file
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@@ -0,0 +1,11 @@
classdef CompleteRoutine_DifferentChannels_Fig3_test < matlab.unittest.TestCase
% Test class for CompleteRoutine_DifferentChannels_Fig3
methods (Test)
function testExample(testCase)
% Example test case for CompleteRoutine_DifferentChannels_Fig3
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/Warehouse_class/functions/phase_predist_plots/fwm_plots/CompleteRoutine_test.m Normal file
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@@ -0,0 +1,11 @@
classdef CompleteRoutine_test < matlab.unittest.TestCase
% Test class for CompleteRoutine
methods (Test)
function testExample(testCase)
% Example test case for CompleteRoutine
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/Warehouse_class/functions/phase_predist_plots/fwm_plots/automate_JLT_plots_test.m Normal file
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@@ -0,0 +1,11 @@
classdef automate_JLT_plots_test < matlab.unittest.TestCase
% Test class for automate_JLT_plots
methods (Test)
function testExample(testCase)
% Example test case for automate_JLT_plots
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/Warehouse_class/functions/phase_predist_plots/fwm_plots/automate_PTL_plot_new_test.m Normal file
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@@ -0,0 +1,11 @@
classdef automate_PTL_plot_new_test < matlab.unittest.TestCase
% Test class for automate_PTL_plot_new
methods (Test)
function testExample(testCase)
% Example test case for automate_PTL_plot_new
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/Warehouse_class/functions/phase_predist_plots/fwm_plots/dispersion_only_test.m Normal file
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@@ -0,0 +1,11 @@
classdef dispersion_only_test < matlab.unittest.TestCase
% Test class for dispersion_only
methods (Test)
function testExample(testCase)
% Example test case for dispersion_only
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/Warehouse_class/functions/phase_predist_plots/fwm_plots/dispersion_validation_miniskript_test.m Normal file
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@@ -0,0 +1,11 @@
classdef dispersion_validation_miniskript_test < matlab.unittest.TestCase
% Test class for dispersion_validation_miniskript
methods (Test)
function testExample(testCase)
% Example test case for dispersion_validation_miniskript
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/Warehouse_class/functions/phase_predist_plots/fwm_plots/generatePlots_test.m Normal file
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@@ -0,0 +1,11 @@
classdef generatePlots_test < matlab.unittest.TestCase
% Test class for generatePlots
methods (Test)
function testExample(testCase)
% Example test case for generatePlots
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/Warehouse_class/functions/phase_predist_plots/fwm_plots/plot3dCurve_test.m Normal file
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@@ -0,0 +1,11 @@
classdef plot3dCurve_test < matlab.unittest.TestCase
% Test class for plot3dCurve
methods (Test)
function testExample(testCase)
% Example test case for plot3dCurve
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/Warehouse_class/functions/phase_predist_plots/fwm_plots/plotBerVsZDW_test.m Normal file
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@@ -0,0 +1,11 @@
classdef plotBerVsZDW_test < matlab.unittest.TestCase
% Test class for plotBerVsZDW
methods (Test)
function testExample(testCase)
% Example test case for plotBerVsZDW
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/Warehouse_class/functions/phase_predist_plots/fwm_plots/plotBerVsZdwFailureRate_test.m Normal file
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@@ -0,0 +1,11 @@
classdef plotBerVsZdwFailureRate_test < matlab.unittest.TestCase
% Test class for plotBerVsZdwFailureRate
methods (Test)
function testExample(testCase)
% Example test case for plotBerVsZdwFailureRate
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/Warehouse_class/functions/phase_predist_plots/fwm_plots/plotChannelSpacingAna_test.m Normal file
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@@ -0,0 +1,11 @@
classdef plotChannelSpacingAna_test < matlab.unittest.TestCase
% Test class for plotChannelSpacingAna
methods (Test)
function testExample(testCase)
% Example test case for plotChannelSpacingAna
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/Warehouse_class/functions/phase_predist_plots/fwm_plots/plotCurve_test.m Normal file
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@@ -0,0 +1,11 @@
classdef plotCurve_test < matlab.unittest.TestCase
% Test class for plotCurve
methods (Test)
function testExample(testCase)
% Example test case for plotCurve
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/Warehouse_class/functions/phase_predist_plots/fwm_plots/plotHistogram_test.m Normal file
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@@ -0,0 +1,11 @@
classdef plotHistogram_test < matlab.unittest.TestCase
% Test class for plotHistogram
methods (Test)
function testExample(testCase)
% Example test case for plotHistogram
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/Warehouse_class/functions/phase_predist_plots/fwm_plots/plotViolin_test.m Normal file
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@@ -0,0 +1,11 @@
classdef plotViolin_test < matlab.unittest.TestCase
% Test class for plotViolin
methods (Test)
function testExample(testCase)
% Example test case for plotViolin
% Add your test code here
testCase.verifyTrue(true);
end
end
end

11
Tests/Warehouse_class/functions/phase_predist_plots/fwm_plots/plot_ber_distribution_test.m Normal file
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@@ -0,0 +1,11 @@
classdef plot_ber_distribution_test < matlab.unittest.TestCase
% Test class for plot_ber_distribution
methods (Test)
function testExample(testCase)
% Example test case for plot_ber_distribution
% Add your test code here
testCase.verifyTrue(true);
end
end
end

4
Tests/generateTests.m
View File

@@ -3,7 +3,7 @@ function generateTests()
if ismac
cd('/Users/silasoettinghaus/Documents/MATLAB/imdd_simulation');
else
cd('C:\Users\Silas\Documents\MATLAB\imdd_simulation');
end
% Define the root folders for classes and tests
@@ -16,7 +16,7 @@ function generateTests()
end
% Get all .m files in the Classes folder and its subfolders
classFiles = dir(fullfile(classesFolder, '**', '*.m'));
classFiles = dir(fullfile(pwd,classesFolder, '**', '*.m'));
% Iterate over all class files
for i = 1:length(classFiles)

0
Functions/Theory/Dissertation/power_fading_gif.m → power_fading_gif.m
View File

0
Functions/Theory/Dissertation/power_fading_gif_lambda_variation.m → power_fading_gif_lambda_variation.m
View File

3
projects/IMDD_base_system/imdd_it.m
View File

@@ -3,13 +3,14 @@
if 1
uloops = struct;
uloops.precomp = [1];
uloops.precomp = [0];
uloops.bitrate = [300].*1e9; %[300,330,360,390,420,450,480] [224,336,360,390,420,448] for MPI
% uloops.laser_wavelength = [1293,1297.5,1302,1306.5,1310,1313.4,1318,1322.7,1327.4];
uloops.laser_wavelength = [1293];
uloops.M = [4];
uloops.link_length = [0:2:10]; % 1,2,3,5,6,8,10
uloops.alpha = [0];
uloops.duob_mode = db_mode.no_db;
wh = DataStorage(uloops);
wh.addStorage("ber");

106
projects/IMDD_base_system/imdd_model.m
View File

@@ -173,60 +173,64 @@ Scpe_sig = Scpe_sig - mean(Scpe_sig.signal);
%%% EQUALIZING
if 0
% -------------------- FFE --------------------
ffe_order = [50, 0, 0];
eq_ffe = EQ("Ne",ffe_order,"Nb",[0,0,0], ...
"training_length",len_tr,"training_loops",5,"dd_loops",5, ...
"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005, ...
"FFEmu",0,"plotfinal",0,"ideal_dfe",0);
% -------------------- FFE --------------------
ffe_order = [50, 0, 0];
eq_ffe = EQ("Ne",ffe_order,"Nb",[0,0,0], ...
"training_length",len_tr,"training_loops",5,"dd_loops",5, ...
"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005, ...
"FFEmu",0,"plotfinal",0,"ideal_dfe",0);
output.ffe_results = ffe(eq_ffe,M,Scpe_sig,Symbols,Tx_bits, ...
"precode_mode",duob_mode,'showAnalysis',0,"postFFE",[], ...
"eth_style_symbol_mapping",0);
output.ffe_results = ffe(eq_ffe,M,Scpe_sig,Symbols,Tx_bits, ...
"precode_mode",duob_mode,'showAnalysis',0,"postFFE",[], ...
"eth_style_symbol_mapping",0);
output.ffe_results.metrics.print
end
if 0
% -------------------- DFE --------------------
eq_dfe = EQ("Ne",ffe_order,"Nb",[2,0,0], ...
"training_length",len_tr,"training_loops",5,"dd_loops",5, ...
"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005, ...
"FFEmu",0,"plotfinal",0,"ideal_dfe",0);
output.ffe_results.metrics.print
output.dfe_results = ffe(eq_dfe,M,Scpe_sig,Symbols,Tx_bits, ...
"precode_mode",duob_mode,'showAnalysis',0,"postFFE",[], ...
"eth_style_symbol_mapping",0);
% -------------------- DFE --------------------
eq_dfe = EQ("Ne",ffe_order,"Nb",[2,0,0], ...
"training_length",len_tr,"training_loops",5,"dd_loops",5, ...
"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005, ...
"FFEmu",0,"plotfinal",0,"ideal_dfe",0);
output.dfe_results = ffe(eq_dfe,M,Scpe_sig,Symbols,Tx_bits, ...
"precode_mode",duob_mode,'showAnalysis',0,"postFFE",[], ...
"eth_style_symbol_mapping",0);
output.dfe_results.metrics.print("description",'DFE');
% -------------------- VNLE + MLSE --------------------
pf_ncoeffs = 1;
ffe_order3 = [50, 5, 5];
eq_v = EQ("Ne",ffe_order3,"Nb",dfe_order, ...
"training_length",len_tr,"training_loops",5,"dd_loops",5, ...
"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005, ...
"FFEmu",0,"plotfinal",0,"ideal_dfe",1);
pf_ = Postfilter("ncoeff",pf_ncoeffs,"useBurg",1);
mlse_ = MLSE("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels);
[output.vnle_results, output.mlse_results] = vnle_postfilter_mlse(eq_v, pf_, mlse_, M, Scpe_sig, Symbols, Tx_bits, ...
"precode_mode", duob_mode, 'showAnalysis', 0, "postFFE", [], "eth_style_symbol_mapping", 0);
% -------------------- DB target --------------------
mlse_db_ = MLSE("DIR",[1,1],"duobinary_output",0,"M",M,'trellis_states',PAMmapper(M,0).levels);
ffe_order = [50, 5, 5];
eq_ = EQ("Ne",ffe_order,"Nb",dfe_order,"training_length",len_tr,"training_loops",5,"dd_loops",5, ...
"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",1);
output.dbt_results = duobinary_target(eq_,mlse_db_, M, Scpe_sig, Symbols, Tx_bits, ...
"precode_mode", duob_mode, 'showAnalysis', 0, "postFFE", []);
output.dbt_results.metrics.print("description",'Duobinary');
disp('- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ')
fprintf('\n')
output.dfe_results.metrics.print("description",'DFE');
end
if 0
% -------------------- VNLE + MLSE --------------------
pf_ncoeffs = 1;
ffe_order3 = [50, 5, 5];
eq_v = EQ("Ne",ffe_order3,"Nb",dfe_order, ...
"training_length",len_tr,"training_loops",5,"dd_loops",5, ...
"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005, ...
"FFEmu",0,"plotfinal",0,"ideal_dfe",1);
pf_ = Postfilter("ncoeff",pf_ncoeffs,"useBurg",1);
mlse_ = MLSE("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels);
[output.vnle_results, output.mlse_results] = vnle_postfilter_mlse(eq_v, pf_, mlse_, M, Scpe_sig, Symbols, Tx_bits, ...
"precode_mode", duob_mode, 'showAnalysis', 0, "postFFE", [], "eth_style_symbol_mapping", 0);
end
if 1
% -------------------- DB target --------------------
mlse_db_ = MLSE("DIR",[1,1],"duobinary_output",0,"M",M,'trellis_states',PAMmapper(M,0).levels);
ffe_order = [50, 5, 5];
eq_ = EQ("Ne",ffe_order,"Nb",dfe_order,"training_length",len_tr,"training_loops",5,"dd_loops",5, ...
"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",1);
output.dbt_results = duobinary_target(eq_,mlse_db_, M, Scpe_sig, Symbols, Tx_bits, ...
"precode_mode", duob_mode, 'showAnalysis', 0, "postFFE", []);
output.dbt_results.metrics.print("description",'Duobinary');
disp('- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ')
fprintf('\n')
end
end

30
test/duobinary_emulation_minimal_example.m
View File

@@ -1,6 +1,6 @@
M = 6;
M = 4;
apply_precode = 1;
bitpattern = [];
@@ -27,6 +27,10 @@ if apply_precode
else
symbols_tx = symbols;
end
disp(['Tx Sequenz: -- RMS:',sprintf('%.1f',rms(symbols_rx.signal)),' - - Levels -',num2str(numel(unique(symbols_rx.signal)))]);
unique(symbols_tx.signal)
disp('- - - - - - - - - -');
show2Dconstellation(symbols_tx,symbols_tx,"displayname",'VNLE Out','fignum',2241);
@@ -35,6 +39,10 @@ if apply_precode
% Entschiedene Symbole codieren: d_DB(n) = d(n) + d(n-1) (im Fall von PAM4 7 level [0 1 2 3 4 5 6])
symbols_db = Duobinary().encode(symbols_tx);
disp(['DB encoded -- RMS:',sprintf('%.1f',rms(symbols_db.signal)),' - - Levels -',num2str(numel(unique(symbols_db.signal)))]);
unique(symbols_db.signal)
disp('- - - - - - - - - -');
% Entschiedene codierte Symbole decodieren: d_dec(n) = d_DB(n) mod4
symbols_rx = Duobinary().decode(symbols_db);
else
@@ -43,18 +51,18 @@ end
% Vergleichen von b(n) und d_dec(n)
bits_rx = PAMmapper(M,0).demap(symbols_rx);
disp(['Wieder normal -- RMS:',sprintf('%.1f',rms(symbols_rx.signal)),' - - Levels -',num2str(numel(unique(symbols_rx.signal)))]);
unique(symbols_rx.signal)
disp('- - - - - - - - - -');
[~,~,ber,~] = calc_ber(bits.signal,bits_rx.signal,"skip_front",10,"skip_end",10,"returnErrorLocation",1);
disp(['BER: ',sprintf('%.1E',ber),' - - PAM-',num2str(M)]);
figure()
subplot(1,2,1)
histogram(symbols_tx.signal,100,'Normalization','count')
figure(200)
clf
hold on
idxs = start-10:start+burstwidth+10;
scatter(idxs,d.signal(idxs),'DisplayName',['Orig Signal'],'Marker','o');
scatter(idxs,d_burst.signal(idxs),'DisplayName',['Error Signal'],'Marker','x');
scatter(idxs,d_dec.signal(idxs),'DisplayName',['EQ Signal'],'Marker','x');
yline(PAMmapper(M,0).thresholds,'HandleVisibility','off');
legend
ylim([-2 2])
subplot(1,2,2)
histogram(symbols_db.signal,100,'Normalization','count')