Merge branch 'main' of cau-git:nt/mitarbeiter/silas/imdd_simulation

Functions/Theory/Dispersion/power_fading_gif.m

@@ -0,0 +1,68 @@
+%% ============================================================
+%  IM/DD Power Fading Evolution GIF (1 km -> 20 km)
+%  Uses the provided GifWriter (serial mode)
+% ============================================================
+
+clear; close all; clc;
+
+%% Fiber and system parameters
+lambda0 = 1310e-9;       % zero-dispersion wavelength [m]
+lambda  = 1275e-9;       % operating wavelength [m]
+S0      = 0.09;          % dispersion slope [ps/(nm^2·km)]
+c       = physconst('lightspeed');
+
+%% Derived quantities (length-independent)
+D_lambda = (S0/4) * (lambda*1e9 - (lambda0*1e9)^4/(lambda*1e9)^3); % ps/(nm·km)
+D_si     = D_lambda * 1e-6;                                        % s/m^2
+b2       = -D_si * lambda^2 / (2*pi*c);                            % s^2/m
+
+%% Frequency grid
+f_max = 150e9;
+f = linspace(0, f_max, 4000);  % [Hz]
+
+%% Figure setup (keep it stable for nicer GIFs)
+fig = figure('Color','w');
+ax = axes(fig); %#ok<LAXES>
+hold(ax,'on'); grid(ax,'on'); box(ax,'on');
+xlabel(ax,'Frequency [GHz]');
+ylabel(ax,'Magnitude [dB]');
+ylim(ax,[-30 0]);
+xlim(ax,[0 f_max/1e9]);
+
+%% GIF writer (serial mode; simplest)
+g = GifWriter('Name','power_fading_evolution', 'DelayTime',0.12, 'Parallel',false);
+
+%% Loop: 1 km to 20 km
+L = [1:20,19:-1:1];
+for L_km = L
+    L_meter = L_km * 1e3; % [m]
+
+    % IM/DD transfer function (power fading)
+    phi = 2*pi^2 * b2 * f.^2 * L_meter;
+    H   = abs(cos(phi));
+    HdB = 10*log10(max(H, 1e-12)); % avoid -Inf for deep notches
+
+    % Clear and redraw (stable axes)
+    cla(ax);
+
+    plot(ax, f/1e9, HdB, 'LineWidth', 1.8, 'Color','black');
+
+    % Analytic first-null frequency marker
+    f_null = sqrt(c*(0.5)/(abs(D_si)*lambda^2*L_meter));
+    xline(ax, f_null/1e9, 'r--', 'LineWidth', 1.2, ...
+        'Label', sprintf('f_{null}=%.1f GHz', f_null/1e9), ...
+        'LabelOrientation','horizontal', ...
+        'LabelVerticalAlignment','bottom');
+
+    title(ax, sprintf('Power Fading for: %.0f km @ 1275 nm', L_km));
+
+    drawnow;
+
+    % Add frame to GIF
+    g.addFrame(fig);
+end
+
+%% Done
+g.compile(fig.Number);
+
+disp(fullfile(g.OutputDir, sprintf('%s_fig_%d.gif', g.Name, fig.Number)));

Functions/Theory/Dispersion/power_fading_gif_lambda_variation.m

@@ -0,0 +1,69 @@
+%% ============================================================
+%  IM/DD Power Fading Evolution vs Wavelength (L = 10 km)
+% ============================================================
+
+clear; close all; clc;
+
+%% Fixed fiber parameters
+lambda0 = 1310e-9;       % zero-dispersion wavelength [m]
+S0      = 0.09;          % dispersion slope [ps/(nm^2·km)]
+L       = 10e3;          % fiber length FIXED [m]
+c       = physconst('lightspeed');
+
+%% Frequency grid
+f_max = 150e9;
+f = linspace(0, f_max, 4000);  % [Hz]
+
+%% Figure setup (stable axes for clean GIF)
+fig = figure('Color','w');
+ax = axes(fig);
+hold(ax,'on'); grid(ax,'on'); box(ax,'on');
+xlabel(ax,'Frequency [GHz]');
+ylabel(ax,'Magnitude [dB]');
+ylim(ax,[-30 0]);
+xlim(ax,[0 f_max/1e9]);
+
+%% GIF writer
+g = GifWriter('Name','power_fading_vs_wavelength', ...
+              'DelayTime',0.12, ...
+              'Parallel',false);
+
+%% Wavelength sweep (around ZDW)
+lambda_vec = linspace(1260e-9, 1360e-9, 25);  % 1260–1360 nm
+
+for k = 1:length(lambda_vec)
+
+    lambda = lambda_vec(k);
+
+    %% Dispersion for current wavelength
+    D_lambda = (S0/4) * (lambda*1e9 - (lambda0*1e9)^4/(lambda*1e9)^3); % ps/(nm·km)
+    D_si     = D_lambda * 1e-6;                                       % s/m^2
+    b2       = -D_si * lambda^2 / (2*pi*c);                           % s^2/m
+
+    %% Power fading transfer function
+    phi = 2*pi^2 * b2 * f.^2 * L;
+    H   = abs(cos(phi));
+    HdB = 10*log10(max(H, 1e-12));
+
+    cla(ax)
+    plot(ax, f/1e9, HdB, 'LineWidth',1.8,'Color','black');
+
+    %% First-null frequency
+    if abs(D_si) > 0
+        f_null = sqrt(c*(0.5)/(abs(D_si)*lambda^2*L));
+        xline(ax, f_null/1e9, 'r--', 'LineWidth',1.2, ...
+            'Label', sprintf('f_{null}=%.1f GHz', f_null/1e9), ...
+            'LabelOrientation','horizontal', ...
+            'LabelVerticalAlignment','bottom');
+    end
+
+    title(ax, sprintf('Power Fading for: 10 km @ %.0f nm', lambda*1e9));
+
+    drawnow;
+    g.addFrame(fig);
+end
+
+%% Compile GIF
+g.compile(fig.Number);
+
+disp(fullfile(g.OutputDir, sprintf('%s_fig_%d.gif', g.Name, fig.Number)));

Functions/Theory/Dissertation/loss_curve_digitized.m

@@ -1,120 +0,0 @@
-function loss_curve_digitized(filename)
-% PLOT_WPD_DATASETS Reads and plots scattering data from a CSV file.
-%   filename: String containing the path to the CSV file (e.g., 'wpd_datasets.csv')
-%
-%   This function expects a CSV with the following structure:
-%   Row 1: Dataset Names (every 2nd column)
-%   Row 2: Variable Names (X, Y, X, Y...)
-%   Row 3+: Numeric Data (potentially with NaNs for unequal lengths)
-
-    % --- 1. Import Data ---
-    if nargin < 1
-        filename = 'wpd_datasets.csv'; % Default filename
-    end
-
-    % Read the numeric data, skipping the first 2 header lines
-    % 'TreatAsMissing' ensures empty cells become NaNs
-    raw_data = readmatrix(filename, 'NumHeaderLines', 2);
-
-    % Read the first line separately to parse Dataset Names
-    fid = fopen(filename, 'r');
-    if fid == -1
-        error('Could not open file: %s', filename);
-    end
-    header_line = fgetl(fid);
-    fclose(fid);
-    
-    % Split header by comma to get names
-    raw_names = split(header_line, ',');
-    
-    % Extract non-empty names (assuming names are in col 1, 3, 5...)
-    dataset_names = raw_names(~cellfun('isempty', raw_names));
-    
-    % --- 2. Setup Plot ---
-    figure('Color', 'w', 'Position', [100, 100, 800, 600]);
-    ax = gca;
-    hold(ax, 'on');
-   
-    
-    line_styles = {'-', '-', '-', '-'};
-    
-    % --- 3. Iterate and Plot Each Dataset ---
-    num_datasets = length(dataset_names);
-    
-    for i = 1:num_datasets
-        % Calculate column indices for X and Y
-        % Dataset 1: Cols 1,2 | Dataset 2: Cols 3,4 | etc.
-        col_x = (i-1)*2 + 1;
-        col_y = (i-1)*2 + 2;
-        
-        % Extract data
-        if col_y > size(raw_data, 2)
-            warning('Data columns missing for dataset %d', i);
-            break;
-        end
-        
-        X = raw_data(:, col_x);
-        Y = raw_data(:, col_y);
-
-        
-        
-        % Remove NaNs (missing data due to unequal lengths)
-        valid_mask = ~isnan(X) & ~isnan(Y);
-        X = X(valid_mask);
-        Y = Y(valid_mask);
-
-        [X, sortIdx] = sort(X);
-        Y = Y(sortIdx);
-        
-        % 3. Smooth the Data
-
-        if numel(X) > 20
-        if 1
-            Y = smoothdata(Y, 'sgolay', 15); 
-        else
-            Y = movmean(Y, 15); 
-        end
-        end
-
-        
-        % Plotting
-        % Using semilogy because scattering data often spans orders of magnitude
-        % (Adjust to 'plot' if linear scale is preferred)
-        p = plot(ax, X, Y, ...
-            'LineStyle', line_styles{mod(i-1, length(line_styles)) + 1}, ...
-            'Marker', 'none', ...
-            'Color', 'black', ...
-            'LineWidth', 1.5, ...
-            'MarkerSize', 6, ...
-            'DisplayName', dataset_names{i});
-            
-        % Optional: Fill marker faces for better visibility
-        % p.MarkerFaceColor = p.Color;
-        % p.MarkerFaceAlpha = 0.3; % Semi-transparent fill
-    end
-    
-    % --- 4. Styling and Formatting ---
-    
-    % Axis Labels (Inferred from typical scattering plots)
-    xlabel(ax, 'Wavelength (\mu m)', 'FontSize', 12, 'FontWeight', 'bold');
-    ylabel(ax, 'Intensity / Cross-Section (a.u.)', 'FontSize', 12, 'FontWeight', 'bold');
-    
-    % Title
-    title(ax, 'Dataset Comparison', 'FontSize', 14);
-    
-    % Legend
-    legend(ax, 'Location', 'best', 'Interpreter', 'none', 'Box', 'on');
-    
-    % Grid
-    grid(ax, 'on');
-    ax.GridAlpha = 0.3;
-    ax.MinorGridAlpha = 0.1;
-    
-    % Set Log Scale for Y (likely required for this data type)
-    set(ax, 'YScale', 'log'); 
-    
-    % Enhance axis appearance
-    set(ax, 'Box', 'on', 'LineWidth', 1.2, 'FontSize', 10);
-    
-    hold(ax, 'off');
-end

Functions/Theory/Dissertation/wpd_datasets.csv

@@ -1,189 +0,0 @@
-Rayleigh,,Experimental,,Infrared Absorption,
-X,Y,X,Y,X,Y
-0.7066005680911753,3.651009696525016,0.7072188355785799,4.848577786727532,1.4943918372804705,0.009324755400827079
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