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This commit is contained in:
Silas Oettinghaus
2025-02-17 21:31:12 +01:00
parent becaf3f6c9
commit d099efea03
21 changed files with 1502 additions and 272 deletions
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177
Classes/04_DSP/Sequence Detection/MLSE_viterbi.m Normal file
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classdef MLSE_viterbi < handle
%MLSE calculates the most probable sequence for an input signal with given/ known channel impulse response of any length
properties(Access=public)
M %PAM-M
DIR
trellis_states
duobinary_output
end
methods (Access=public)
function obj = MLSE_viterbi(options)
%NAME Construct an instance of this class
% Detailed explanation goes here
arguments
options.M double = 4;
options.DIR double = [1];
options.trellis_states double = [-3 -1 1 3];
options.duobinary_output logical = false;
end
%
fn = fieldnames(options);
for n = 1:numel(fn)
try
obj.(fn{n}) = options.(fn{n});
end
end
% do more stuff
end
function signalclass = process(obj,signalclass)
data_in = signalclass.signal;
data_out = obj.process_(data_in);
signalclass.signal = data_out;
end
function data_out = process_(obj,data_in)
% remove unnecessary zeros at start of impulse response to keep
% number of trellis states minimal
DIR_nonzero = find(obj.DIR ~= 0);
if DIR_nonzero(1) > 1
obj.DIR(1:DIR_nonzero(1)-1) = [];
end
if isscalar(obj.DIR)
obj.DIR = [0 obj.DIR];
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%% WORKING
% impulse respnse i.e. [0.5, 1.0000]
obj.DIR = flip(obj.DIR);
% RMS normalization of input data
data_in = data_in ./ rms(data_in);
% seems to be the only way to use combvec for a flexible amount
% of vectors. 'combs' contains all trellis states
pre_comb_mat = repmat(obj.trellis_states,length(obj.DIR)-1,1);
pre_comb_cell = mat2cell(pre_comb_mat,ones(1,size(pre_comb_mat,1)),size(pre_comb_mat,2));
combs = fliplr(combvec(pre_comb_cell{:}).');
% Save first and last symbol of each state
first_sym = combs(:,1);
last_sym = combs(:,end);
states = sum(combs,2);
% Calculate all possible input symbols for the desired impulse
% response. Row number is the index of the previous state,
% column number is the index of the next state
% noise free received == branch metrics
noise_free_received = zeros(length(states),length(states));
count_row = 1;
count_col = 1;
for l1 = 1:length(states)
for l2 = 1:length(states)
if sum(combs(l2,2:end) == combs(l1,1:end-1)) == size(combs,2)-1
noise_free_received(count_row,count_col) = sum(combs(l2,:).*obj.DIR(end:-1:2)) + last_sym(l1)*obj.DIR(1);
else
noise_free_received(count_row,count_col) = inf;
end
count_row = count_row + 1;
end
count_col = count_col + 1;
count_row = 1;
end
% match amplitude levels of input signal to those of the calculated ideal symbols
% i.e. match the rms values of data_in to noise_free_received
if isreal(data_in)
if obj.M == round(obj.M)
data_in = data_in * rms(noise_free_received(noise_free_received ~= inf),'all','omitnan');
end
end
% OLD
% initilaize the output vector
data_out = NaN(size(data_in));
sum_path_metrics = zeros(length(states),length(states));
% first trellis path
% euclidian distance as path metric
path_metrics = (abs(repmat(data_in(1),size(noise_free_received)) - noise_free_received)).^2;
sum_path_metrics = sum_path_metrics + path_metrics; % calculation of all possible sum path metrics
[sum_path_metrics_res(:,1),path_idx(:,1)] = min(sum_path_metrics,[],2); % find the best path to each state, store sum path metric and predecessor for each state
% remaining trellis paths
for n = 2:length(data_in)
sum_path_metrics = repmat(sum_path_metrics_res(:,n-1).',length(states),1);
% path_metrics = (repmat(data_in(n),size(noise_free_received)) - noise_free_received).^2;%.*prob_mat;
path_metrics = (abs(repmat(data_in(n),size(noise_free_received)) - noise_free_received)).^2;
sum_path_metrics = sum_path_metrics + path_metrics;
[sum_path_metrics_res(:,n),path_idx(:,n)] = min(sum_path_metrics,[],2);
end
%% trace back
ideal_path = NaN(1,length(data_in)+1);
% find ideal trellis path by going through the trellis
% backwards
[~,ideal_path(length(data_in)+1)] = min(sum_path_metrics_res(:,length(data_in)));
% starting with the state that has the lowest sum path
% metric, follow the stored information about the
% predecessor
for h = length(data_in):-1:1
ideal_path(h) = path_idx(ideal_path(h+1),h);
end
idx_out = ideal_path(2:length(data_in)+1);
%%%%%%%%%%%%%%%%%%%%%%%%%%%% WORKING
if obj.duobinary_output
%use duobinary encoder, output is already scaled inside this
%one
data_out = Duobinary().encode(first_sym(idx_out));
else
%
data_out(1:length(data_in)) = first_sym(idx_out);
% scale to rms = 1 using the standard sqrt() expressions
if obj.M == 4
data_out = data_out./sqrt(5);
elseif obj.M == 6
data_out = data_out./sqrt(10);
elseif obj.M == 8
data_out = data_out./sqrt(21);
end
end
end
end
methods (Access=private)
% Cant be seen from outside! So put all your functions here that can/
% shall not be called from outside
end
end

214
Classes/04_DSP/TransmissionPerformance.m Normal file
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classdef TransmissionPerformance
% TransmissionPerformance
%
% This class calculates the best possible net data rate (in bits/s)
% from a given gross rate (bits/s) and a measured channel quality
% parameter (either bit error ratio (BER) or NGMI). The class uses
% lookup tables for three different FEC modes:
%
% 1. SD+HD concatenated (uses overall code rate and an NGMI threshold)
% 2. SD+HD concatenated with punctured LDPC (uses overall code rate and an NGMI threshold)
% 3. HD-only (uses a code rate and a BER threshold)
%
% The basic algorithm is as follows:
% 1. Given a measured quality (NGMI or BER) for each measurement, find in
% the table the first (i.e. “best”) entry that is satisfied by the
% measured value. For NGMI the condition is measured NGMI >= threshold,
% while for BER the condition is measured BER <= threshold.
% 2. Use the corresponding overall code rate to compute:
%
% net rate = gross rate * code rate.
%
% USAGE EXAMPLE (array inputs):
%
% tp = TransmissionPerformance;
% % Suppose we have 3 measurements:
% % Gross rates: 10, 15, and 20 Gbps
% % Measured NGMI: 0.92, 0.88, and 0.85
% % Measured BER: 1e-4, 7e-3, and 2e-2
% netrates = tp.calculatenetrate([10e9 15e9 20e9], ...
% 'NGMI', [0.92 0.88 0.85], ...
% 'BER', [1e-4 7e-3 2e-2]);
%
% The returned structure netrates will contain the net rates (and the
% used code rates and thresholds) for each mode in vector form.
properties (Constant)
%% Lookup Table for SD+HD concatenated (using NGMI)
% Overall code rate and NGMI threshold for each row.
OVERALLCODERATES_SDHD = [0.7519, 0.7602, 0.7684, 0.7766, 0.7850, ...
0.7932, 0.8014, 0.8098, 0.8180, 0.8262, ...
0.8345, 0.8428, 0.8510, 0.8593, 0.8676, ...
0.8733, 0.8790, 0.8848, 0.8905, 0.8962, ...
0.9019, 0.9077, 0.9134, 0.9191, 0.9248, ...
0.9306, 0.9363, 0.9420, 0.9477, 0.9535];
NGMITHRESHOLDS_SDHD = [0.8116, 0.8167, 0.8241, 0.8317, 0.8401, ...
0.8459, 0.8512, 0.8574, 0.8685, 0.8746, ...
0.8829, 0.8892, 0.8958, 0.9022, 0.9090,...
0.9150, 0.9210, 0.9270, 0.9330, 0.9390, ...
0.9450, 0.9510, 0.9570, 0.9630, 0.9690, ...
0.9750, 0.9810, 0.9870, 0.9930, 0.9990];
ISPUNCT_LDPI = [zeros(1,15),ones(1,15)];
%% Lookup Table for HD-only mode (using BER)
% For HD-only, the table gives the code rate and the maximum acceptable BER.
CODE_RATES_HD = [0.7500, 0.8000, 0.8123, 0.8333, 0.8571, ...
0.8750, 0.8889, 0.9000, 0.9091, 0.9167, 0.9412];
BERTHRESHOLDS_HD = [2.12e-2, 1.76e-2, 1.62e-2, 1.44e-2, 1.25e-2, ...
1.03e-2, 9.29e-3, 8.33e-3, 7.54e-3, 7.04e-3, 4.70e-3];
%% LUT for KP4-FEC and Inner Code https://grouper.ieee.org/groups/802/3/dj/public/23_03/patra_3dj_01b_2303.pdf
CODE_RATE_KP4_AND_INNER = [0.885799];
BERTHRESHOLDS_KP4_AND_INNER = 4.85e-3;
end
methods
function netrates = calculateNetRate(obj, grossRate, varargin)
% calculatenetrate Calculate the net data rate(s) from measured data.
%
% netrates = tp.calculatenetrate(grossRate, 'BER', berValue, 'NGMI', ngmiValue)
%
% INPUTS:
% grossRate - (scalar or vector) gross data rate(s) [bits/s]
%
% Optional name/value pairs:
% 'BER' - (scalar or vector) measured bit error ratio(s)
% 'NGMI' - (scalar or vector) measured NGMI value(s)
%
% At least one of 'BER' or 'NGMI' must be provided.
%
% OUTPUT:
% netrates - a structure with the following fields (if available):
% .SDHD - for SD+HD concatenated (using NGMI)
% .SDHD_LDPC - for SD+HD with punctured LDPC (using NGMI)
% .HD - for HD-only (using BER)
%
% Each sub-structure contains fields:
% .CodeRate - the chosen overall code rate from the table (vector)
% .Threshold - the threshold value used from the table (vector)
% .<BER/NGMI> - net rate computed as grossRate * CodeRate (vector)
% Parse inputs.
p = inputParser;
addRequired(p, 'grossRate', @(x) isnumeric(x));
addParameter(p, 'BER', [], @(x) isnumeric(x));
addParameter(p, 'NGMI', [], @(x) isnumeric(x));
parse(p, grossRate, varargin{:});
ber = p.Results.BER;
ngmi = p.Results.NGMI;
if isempty(ber) && isempty(ngmi)
error('At least one of ''BER'' or ''NGMI'' must be provided.');
end
% Determine the number of measurements.
numMeasurements = max([numel(grossRate), numel(ngmi), numel(ber)]);
% Broadcast scalars if needed.
if isscalar(grossRate) && numMeasurements > 1
grossRate = repmat(grossRate, 1, numMeasurements);
elseif numel(grossRate) ~= numMeasurements
error('grossRate must be scalar or have %d elements.', numMeasurements);
end
if ~isempty(ngmi)
if isscalar(ngmi) && numMeasurements > 1
ngmi = repmat(ngmi, 1, numMeasurements);
elseif numel(ngmi) ~= numMeasurements
error('NGMI must be scalar or have %d elements.', numMeasurements);
end
end
if ~isempty(ber)
if isscalar(ber) && numMeasurements > 1
ber = repmat(ber, 1, numMeasurements);
elseif numel(ber) ~= numMeasurements
error('BER must be scalar or have %d elements.', numMeasurements);
end
end
% Initialize the output structure.
netrates = struct;
if ~isempty(ngmi)
netrates.SDHD.GrossRate = NaN(1, numMeasurements);
netrates.SDHD.NetRate = NaN(1, numMeasurements);
netrates.SDHD.punctLDPI = NaN(1, numMeasurements);
netrates.SDHD.CodeRate = NaN(1, numMeasurements);
netrates.SDHD.Threshold = NaN(1, numMeasurements);
end
if ~isempty(ber)
netrates.HD.GrossRate = NaN(1, numMeasurements);
netrates.HD.NetRate = NaN(1, numMeasurements);
netrates.HD.CodeRate = NaN(1, numMeasurements);
netrates.HD.Threshold = NaN(1, numMeasurements);
netrates.KP4_hamming.GrossRate = NaN(1, numMeasurements);
netrates.KP4_hamming.NetRate = NaN(1, numMeasurements);
netrates.KP4_hamming.CodeRate = NaN(1, numMeasurements);
netrates.KP4_hamming.Threshold = NaN(1, numMeasurements);
end
% Process each measurement individually.
for i = 1:numMeasurements
% --- NGMI-based modes ---
if ~isempty(ngmi)
% SD+HD concatenated mode.
idx = find(obj.NGMITHRESHOLDS_SDHD <= ngmi(i), 1, 'last');
if ~isempty(idx)
codeRate = obj.OVERALLCODERATES_SDHD(idx);
netrates.SDHD.NetRate(i) = grossRate(i) * codeRate;
netrates.SDHD.GrossRate(i) = grossRate(i) ;
netrates.SDHD.CodeRate(i) = codeRate;
netrates.SDHD.Threshold(i) = obj.NGMITHRESHOLDS_SDHD(idx);
netrates.SDHD.punctLDPI(i) = obj.ISPUNCT_LDPI(idx);
end
end
% --- BER-based mode (HD-only) ---
if ~isempty(ber)
% Loop through the BER thresholds from the best (highest code rate)
% to the worst until the measured BER is acceptable.
idxBER = [];
for j = length(obj.BERTHRESHOLDS_HD):-1:1
if ber(i) <= obj.BERTHRESHOLDS_HD(j)
idxBER = j;
break;
end
end
if ~isempty(idxBER)
codeRate = obj.CODE_RATES_HD(idxBER);
netrates.HD.NetRate(i) = grossRate(i) * codeRate;
netrates.HD.GrossRate(i) = grossRate(i) ;
netrates.HD.CodeRate(i) = codeRate;
netrates.HD.Threshold(i) = obj.BERTHRESHOLDS_HD(idxBER);
end
idxBER = [];
for j = length(obj.BERTHRESHOLDS_KP4_AND_INNER):-1:1
if ber(i) <= obj.BERTHRESHOLDS_KP4_AND_INNER(j)
idxBER = j;
break;
end
end
if ~isempty(idxBER)
codeRate = obj.CODE_RATE_KP4_AND_INNER(idxBER);
netrates.KP4_hamming.NetRate(i) = grossRate(i) * codeRate;
netrates.KP4_hamming.GrossRate(i) = grossRate(i) ;
netrates.KP4_hamming.CodeRate(i) = codeRate;
netrates.KP4_hamming.Threshold(i) = obj.BERTHRESHOLDS_KP4_AND_INNER(idxBER);
end
end
end
end
end
end

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Classes/DataBaseHandler/copyStylingFrom.m Normal file
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function copyStylingFrom(figNumSource, figNumTgt)
% Get handles to the source and target figures
sourceFig = figure(figNumSource);
targetFig = figure(figNumTgt);
% Get axes of source and target figures
sourceAxes = findall(sourceFig, 'type', 'axes');
targetAxes = findall(targetFig, 'type', 'axes');
% Ensure the number of axes match
if length(sourceAxes) ~= length(targetAxes)
error('Number of axes in source and target figures must be the same.');
end
% Loop through each pair of axes and copy styling properties
for i = 1:length(sourceAxes)
copyAxesProperties(sourceAxes(i), targetAxes(i));
end
% Apply general figure properties if desired
targetFig.Color = sourceFig.Color; % Background color
end
function copyAxesProperties(sourceAx, targetAx)
% List of properties to copy from source to target axes
propsToCopy = {'XColor', 'YColor', 'ZColor', 'FontSize', 'FontName', ...
'GridColor', 'GridLineStyle', 'MinorGridColor', 'Box', ...
'XGrid', 'YGrid', 'ZGrid', 'XMinorGrid', 'YMinorGrid', 'ZMinorGrid', ...
'LineWidth', 'TitleFontSizeMultiplier', 'LabelFontSizeMultiplier'};
% Copy properties from source to target
for i = 1:length(propsToCopy)
try
targetAx.(propsToCopy{i}) = sourceAx.(propsToCopy{i});
catch
% Skip property if it doesn't exist or can't be copied
end
end
% Copy axis labels and titles
targetAx.Title.String = sourceAx.Title.String;
targetAx.XLabel.String = sourceAx.XLabel.String;
targetAx.YLabel.String = sourceAx.YLabel.String;
targetAx.ZLabel.String = sourceAx.ZLabel.String;
% Copy children elements like lines, patches, etc.
sourceChildren = allchild(sourceAx);
targetChildren = allchild(targetAx);
% Ensure the number of children elements match
if length(sourceChildren) ~= length(targetChildren)
warning('Number of elements in source and target axes differ. Styling may not be applied completely.');
end
% Copy properties of children (like lines, patches, etc.), except colors and legends
for i = 1:min(length(sourceChildren), length(targetChildren))
copyObjectProperties(sourceChildren(i), targetChildren(i));
end
end
function copyObjectProperties(sourceObj, targetObj)
% List of common properties to copy for plot elements (lines, patches, etc.)
propsToCopy = {'LineStyle', 'LineWidth', 'Marker', 'MarkerSize', ...
'MarkerEdgeColor', 'MarkerFaceColor', 'DisplayName'};
% Copy properties from source to target, excluding colors
for i = 1:length(propsToCopy)
try
if ~contains(propsToCopy{i}, 'Color') % Skip color properties
targetObj.(propsToCopy{i}) = sourceObj.(propsToCopy{i});
end
catch
% Skip property if it doesn't exist or can't be copied
end
end
end