3676d92b30
+ duobinary_target now supports memoryless decoding (FFE targets DB response without MLSE) + PAMmapper.quantize now supports custom constellations for quantization + Added a new folder 'Documentations' for pdfs, slides, etc. + Added new FSO evaluation scripts in projects/FSO transmission/Evaluation Scripts + Added ffe_db (rudimentary module, not important anymore)
113 lines
3.5 KiB
Matlab
113 lines
3.5 KiB
Matlab
classdef MaxVar_Timing_Recovery < handle
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properties(Access=public)
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mode
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sps
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fsym
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fadc
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num_tau
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comp_signal
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comp_mode
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end
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methods(Access=public)
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function obj = MaxVar_Timing_Recovery(options)
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arguments(Input)
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options.mode = 0;
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options.sps = 2;
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options.fsym = 32e9;
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options.fadc = 80e9;
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options.num_tau = 64;
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options.comp_signal = 0;
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options.comp_mode = 0;
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end
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fn = fieldnames(options);
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for n = 1:numel(fn)
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obj.(fn{n}) = options.(fn{n});
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end
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%obj-Initialization here%
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end
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function [data_out] = process(obj, data_in)
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data_out = data_in;
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x = data_in.signal;
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if obj.comp_mode
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our_signal = data_in;
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their_signal = obj.comp_signal;
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end
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if obj.mode == 0
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vars = zeros(obj.sps,1);
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for phi = 1:obj.sps
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% Test variance for different start samples
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y_phi = x(phi:obj.sps:end);
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vars(phi) = var(y_phi);
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% Comparison to reference signal
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if obj.comp_mode
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our_signal.signal = x(phi:obj.sps:end);
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our_signal.fs = 6e9;
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our_signal.normalize("mode","rms").plot("displayname",['Our signal, ' num2str(vars(phi))],'fignum',1231+phi);
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their_signal.normalize("mode","rms").plot("displayname",'Their signal','fignum',1231+phi);
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end
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end
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% Choose signal configuration with the maximum variance
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[~,phi_opt] = max(vars);
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y = x(phi_opt:obj.sps:end).';
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elseif obj.mode == 1
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% Create a grid with different (sub)sample starting points
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T = 1/obj.fsym;
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t = (0:length(x)-1)/obj.fadc;
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tauGrid = linspace(0, T, obj.num_tau);
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% Interpolate the signal starting from every defined point
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% and calculate the MMSE/variance
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vars = zeros(size(tauGrid));
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for i = 1:numel(tauGrid)
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tau = tauGrid(i);
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% tk = linspace(tau, t(end), length(x)/obj.sps);
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tk = tau : T : t(end)+tau;
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xk = interp1(t, x, tk, 'linear', 'extrap');
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if obj.comp_mode % MMSE
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e = xk.' - obj.comp_signal.signal;
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vars(i) = mean(abs(e).^2);
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else % Variance
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vars(i) = var(xk, 1);
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end
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end
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if obj.comp_mode % MMSE
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[~,idx] = min(vars);
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else % Variance
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[~,idx] = max(vars);
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end
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tau_opt = tauGrid(idx);
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% Choosing the signal at optimum
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% tk = linspace(tau_opt, t(end), length(x)/obj.sps);
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tk = tau_opt : T : t(end)+tau_opt;
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y = interp1(t, x, tk, 'linear', 'extrap');
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end
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% if length(y) ~= length(x)/obj.sps
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% y = [y 0];
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% end
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data_out.signal = y.';
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data_out.fs = obj.fsym;
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end
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end
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end
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