005e821131
- 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
78 lines
2.3 KiB
Matlab
78 lines
2.3 KiB
Matlab
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
|
|
|