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Many changes in DBHandler

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new general processing structure
just before splitting off the projects folder from this repo
This commit is contained in:
Silas Oettinghaus
2025-05-13 10:24:09 +02:00
parent 727c3d9364
commit 9ce23c4a10
38 changed files with 2440 additions and 1103 deletions
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177
Functions/EQ_structures/duobinary_signaling.m
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@@ -1,96 +1,129 @@
function [eq_package] = duobinary_signaling(eq_, mlse_,M ,rx_signal, tx_symbols, tx_bits,options)
%Duobinary Signaling
function [db_results] = duobinary_signaling(eq_, mlse_, M, rx_signal, tx_symbols, tx_bits, options)
% DUOBINARY_SIGNALING Processes signals through duobinary signaling
%
% Inputs:
% eq_ - Equalizer object
% mlse_ - MLSE object
% M - Modulation order
% rx_signal - Received signal
% tx_symbols - Transmitted symbols
% tx_bits - Transmitted bits
% options - Optional parameters
%
% Outputs:
% db_results - Results from duobinary signaling processing
arguments
eq_
mlse_
M
rx_signal
tx_symbols
tx_bits
options.postFFE = [];
eq_
mlse_
M
rx_signal
tx_symbols
tx_bits
options.precode_mode db_mode
options.showAnalysis = 0
options.eth_style_symbol_mapping = 0
options.postFFE = []
options.database = []
end
%% Process signals through equalizer
[eq_signal, eq_noise] = eq_.process(rx_signal, tx_symbols);
[eq_signal, eq_noise] = eq_.process(rx_signal,tx_symbols);
% Apply post-FFE if provided
if ~isempty(options.postFFE)
[eq_signal,eq_noise] = options.postFFE.process(eq_signal,tx_symbols);
[eq_signal, eq_noise] = options.postFFE.process(eq_signal, tx_symbols);
end
% Process through MLSE
eq_signal = mlse_.process(eq_signal);
% Apply duobinary encoding and decoding
eq_signal = Duobinary().encode(eq_signal);
eq_signal = Duobinary().decode(eq_signal);
% M = numel(unique(eq_signal.signal));
rx_bits = PAMmapper(M,0).demap(eq_signal);
% Demap symbols to bits
rx_bits = PAMmapper(M, 0, "eth_style", options.eth_style_symbol_mapping).demap(eq_signal);
[bits_db,errors_db,ber_db,~] = calc_ber(rx_bits.signal,tx_bits.signal,"skip_front",100,"skip_end",150,"returnErrorLocation",1);
%% Calculate BER and metrics
[bits_db, errors_db, ber_db, error_pos] = calc_ber(rx_bits.signal, tx_bits.signal, "skip_front", 100, "skip_end", 150, "returnErrorLocation", 1);
eq_package.ber = ber_db;
resultsDBsignaling = struct( ...
'result_id', NaN, ... %
'run_id', NaN, ... % Beispielhafte Run-ID
'eqParam_id', NaN, ... % Beispielhafter Fremdschlüssel zur EqualizerParameters-Tabelle
'date_of_processing', datetime('now'), ... % Aktuelles Datum und Uhrzeit
'numBits', bits_db, ... % Beispiel: 1.000.000 Bits
'numBitErr', errors_db, ... % Beispiel: 120 Bitfehler
'BER_precoded', [], ... % BER = 120 / 1.000.000
'numBitErr_precoded', [], ... % Beispiel: 120 Bitfehler
'BER', ber_db, ... % BER = 120 / 1.000.000
'SNR', [], ... % Beispielhafte SNR
'SNR_level', jsonencode([]), ... % SNR-Level als JSON-codiertes Array
'GMI', [], ... % Beispielhafter GMI-Wert
'AIR', [], ... % Beispielhafter AIR-Wert
'EVM', [], ... % Beispielhafte EVM
'EVM_level', jsonencode([]), ... % EVM-Level als JSON-codiertes Array
'Alpha', [] ... % Beispielhafter Alpha-Wert
);
% Calculate performance metrics
[snr, snr_lvl] = calc_snr(tx_symbols.signal, eq_noise.signal);
[gmi] = calc_air(eq_signal, tx_symbols, "skip_front", 10000, "skip_end", 10000);
air = tx_symbols.fs .* floor(log2(double(M))*10)/10 .* gmi ./ log2(double(M));
[evm_total, evm_lvl] = calc_evm(eq_signal, tx_symbols);
[std_total, std_lvl] = calc_std(eq_signal, tx_symbols);
[std_rxraw_total, std_rxraw_lvl] = calc_std(rx_signal.resample("fs_out", tx_symbols.fs), tx_symbols);
%% Prepare output structure
% Determine postFFE order
if ~isempty(options.postFFE)
npostFFE = options.postFFE.order;
else
npostFFE = 0;
end
equalizerConfigDBsignaling = struct( ...
'eq_id', NaN, ... % Auto-Inkrement, wird in der DB gesetzt
'equalizer_structure', int32(equalizer_structure.db_encoded), ... % Beispiel: 1 (z.B. für vnle)
'M', M, ... % Ordnung der PAM-Konstellation
'target_constellation', jsonencode(round(unique(tx_symbols.signal),5)), ... % Beispielhafter Target-String
'db_target', 1, ... % 0 oder 1
'diff_precode', 1, ... % 0 oder 1
'postFFE', ~isempty(options.postFFE), ... % Beispielwert
'NpostFFE', npostFFE, ... % Beispielwert
'Ne1', eq_.Ne(1), ... % Feedforward Koeffizienten 1. Ordnung
'Ne2', eq_.Ne(2), ... % Feedforward Koeffizienten 2. Ordnung
'Ne3', eq_.Ne(3), ... % Feedforward Koeffizienten 3. Ordnung
'Nb1', eq_.Nb(1), ... % Decision Feedback Koeffizienten 1. Ordnung
'Nb2', eq_.Nb(2), ... % Decision Feedback Koeffizienten 2. Ordnung
'Nb3', eq_.Nb(3), ... % Decision Feedback Koeffizienten 3. Ordnung
'K', eq_.K, ... % Samples pro Symbol
'DCmu', eq_.DCmu, ... % Anpassungsrate für DC-Tap
'ideal_dfe', eq_.ideal_dfe, ... % Flag für ideal DFE (0 oder 1)
'training_length', eq_.training_length, ... % Anzahl Trainingssymbole
'training_loops', eq_.training_loops, ... % Anzahl Trainingsdurchläufe
'TRmu1', eq_.FFEmu, ... % mu für DD-Modus (1. Ordnung)
'TRmu2', eq_.FFEmu, ... % mu für DD-Modus (2. Ordnung)
'TRmu3', eq_.FFEmu, ... % mu für DD-Modus (3. Ordnung)
'TRmuDFE', eq_.DFEmu, ... % mu für DFE-Modus im DD
'dd_loops', 5, ... % Anzahl Durchläufe im DD-Modus
'DDmu1', eq_.DDmu(1), ... % mu für DD-Modus (1. Ordnung)
'DDmu2', eq_.DDmu(2), ... % mu für DD-Modus (2. Ordnung)
'DDmu3', eq_.DDmu(3), ... % mu für DD-Modus (3. Ordnung)
'DDmuDFE', eq_.DDmu(4), ... % mu für DFE-Modus im DD
'MLSE_mode', 'viterbi', ... % Beispiel: MLSE-Modus als String
'MLSE_trellis_states', jsonencode(mlse_.trellis_states), ... % Trellis-States, z.B. als JSON-String oder kommasepariert
'comment', 'function: duobinary_target.m', ... % Zusätzliche Kommentare
'config_hash', NaN ...
);
% Create results structure
db_results = struct();
db_results.metrics = Metricstruct;
db_results.metrics.result_id = NaN;
db_results.metrics.run_id = NaN;
db_results.metrics.eqParam_id = NaN;
db_results.metrics.date_of_processing = datetime('now');
db_results.metrics.BER = ber_db;
db_results.metrics.numBits = bits_db;
db_results.metrics.numBitErr = errors_db;
db_results.metrics.SNR = snr;
db_results.metrics.SNR_level = snr_lvl;
db_results.metrics.STD = std_total;
db_results.metrics.STD_level = std_lvl;
db_results.metrics.STDrx = std_rxraw_total;
db_results.metrics.STDrx_level = std_rxraw_lvl;
db_results.metrics.GMI = gmi;
db_results.metrics.AIR = air;
db_results.metrics.EVM = evm_total;
db_results.metrics.EVM_level = evm_lvl;
db_results.metrics.MLSE_dir = mlse_.DIR;
eq_package.resultsDBsignaling = resultsDBsignaling;
eq_package.equalizerConfigDBsignaling = equalizerConfigDBsignaling;
% Create configuration structure
eq_.e = [];
eq_.e2 = [];
eq_.e3 = [];
db_results.config = Equalizerstruct();
db_results.config.eq = jsonencode(eq_);
mlse_.DIR = [];
db_results.config.mlse = jsonencode(mlse_);
db_results.config.equalizer_structure = int32(equalizer_structure.db_encoded);
db_results.config.comment = 'function: duobinary_signaling';
%% Display analysis if requested
if options.showAnalysis
displayAnalysis(eq_noise, eq_signal, rx_signal, eq_, tx_symbols, M, options.postFFE);
end
end
%% Helper Function
function displayAnalysis(eq_noise, eq_signal, rx_signal, eq_, tx_symbols, M, postFFE)
% Display analysis plots and metrics
figure(336);
showEQNoisePSD(eq_noise, "fignum", 336, "displayname", 'Residual Noise after Duobinary');
if ~isempty(postFFE)
showEQcoefficients('n1', postFFE.e, "displayname", 'Coefficients', 'fignum', 338);
end
showEQfilter(eq_.e, eq_signal.fs.*2);
figure(341); clf;
showLevelHistogram(eq_signal, tx_symbols, "fignum", 341);
warning off
figure(400); clf;
showLevelScatter(eq_signal, tx_symbols, "fignum", 400);
figure(401); clf;
showLevelScatter(rx_signal.resample("fs_out", tx_symbols.fs), tx_symbols, "fignum", 401);
warning on
end