Added:

- Class folder 'Timing Recovery' with different timing recoveries
- Minimal example for the timing recovery on the FSO data
- New evaluation scripts in the FSO project folder

Classes/04_DSP/Timing Recovery/Timing_Recovery_GPT.m

@@ -0,0 +1,77 @@
+classdef Timing_Recovery_GPT < handle
+
+    properties(Access=public)
+        sps
+        muGrid
+    end
+
+    methods(Access=public)
+        function obj = Timing_Recovery_GPT(options)
+            arguments(Input)
+
+                options.sps = 2;
+                options.muGrid = 0;
+
+            end
+
+            fn = fieldnames(options);
+            for n = 1:numel(fn)
+                obj.(fn{n}) = options.(fn{n});
+            end
+
+            %obj-Initialization here%
+
+        end
+
+        function [data_out, mu_best, score] = process(obj, data_in)
+            %MAXVARTIMINGSYNC Choose sampling phase mu that maximizes variance of downsampled symbols.
+            %
+            % data_in      : matched-filtered samples (complex or real), length N
+            % sps    : samples per symbol (here typically 2)
+            % muGrid : candidate fractional offsets in [0,1)
+            %
+            % data_out  : symbol-rate samples (length floor(N/sps))
+            % mu_best: chosen fractional offset
+            % score  : variance score for each mu in muGrid
+
+            data_out = data_in;
+            
+            x = data_in.signal(:);
+            N = length(x);
+            Ns = floor(N/obj.sps);
+        
+            if nargin < 3 || isempty(obj.muGrid)
+                obj.muGrid = linspace(0, 0.99, 101); % 0..0.99 in ~0.01 steps
+            end
+        
+            % Symbol indices (1-based sample positions)
+            n0 = 1; % start sample index
+            k  = (0:Ns-1).';
+            tBase = n0 + k*obj.sps; % integer times (1, 1+sps, ...)
+        
+            score = zeros(numel(obj.muGrid),1);
+        
+            for m = 1:numel(obj.muGrid)
+                mu = obj.muGrid(m);
+                t  = tBase + mu;
+        
+                % Linear fractional sampling
+                y  = interp1(1:N, x, t, 'linear', 'extrap');
+        
+                % For PAM, maximize variance of real part (or abs if you prefer)
+                yr = real(y);
+                score(m) = var(yr, 1);  % use population variance (normalization doesn't matter for argmax)
+            end
+        
+            % Pick best mu
+            [~, idx] = max(score);
+            mu_best = obj.muGrid(idx);
+        
+            % Resample with best mu
+            t = tBase + mu_best;
+            data_out.signal = interp1(1:N, x, t, 'linear', 'extrap');
+
+        end
+    end
+end
+