Files
imdd_silas/projects/HighSpeedExperiment_2024/Auswertung_JLT/final/FIGURE_ROP.m
2025-12-15 15:41:02 +01:00

154 lines
4.0 KiB
Matlab

%% ============================================================
% LOAD DATA (PAM-4, sweep over ROP)
% ============================================================
database_type = 'mysql';
db = DBHandler("dataBase", "labor_highspeed", "type", database_type);
pam_level = 4;
fiberL = 1; % 1 km
wlen = 1310;
baudrate = 360e9;
fp = QueryFilter();
fp.where('Runs','pam_level','EQUALS', pam_level);
fp.where('Runs','fiber_length','EQUALS', fiberL);
fp.where('Runs','wavelength','EQUALS', wlen);
fp.where('Runs','bitrate','EQUALS', baudrate);
fp.where('Runs','power_pd_in','LESS_THAN', 7);
fields = [
db.getTableFieldNames('power_state_info');
db.getTableFieldNames('dashboard_ungrouped_alltime')
];
[dataTable,~] = db.queryDB(fp, fields);
%% ============================================================
% DSP SCHEMES (Best combinations only)
% ============================================================
curves = struct;
curves(1).name = 'VNLE';
curves(1).eq = equalizer_structure.vnle;
curves(1).color = clr.Paired.red;
curves(2).name = 'PF + MLSE';
curves(2).eq = equalizer_structure.vnle_pf_mlse;
curves(2).color = clr.Paired.green;
curves(3).name = 'DB-target + MLSE';
curves(3).eq = equalizer_structure.vnle_db_mlse;
curves(3).color = clr.Paired.blue;
curves(4).name = 'ML-based MLSE';
curves(4).eq = equalizer_structure.ml_mlse;
curves(4).color = clr.Paired.purple;
%% ============================================================
% ANALYSIS ENGINE (No plotting)
% ============================================================
results = struct;
for k = 1:numel(curves)
pre_emph = decide_preemph(pam_level,curves(k).eq);
precoded = decide_precoded(pam_level,curves(k).eq);
cfg = struct;
cfg.x_axis = 'power_mzm'; % ROP axis
cfg.y_axis = 'BER';
cfg.agg = 'min';
cfg.outlier = 'none';
cfg.show_raw = false;
cfg.filters = struct( ...
'pam_level', pam_level, ...
'fiber_length', fiberL, ...
'wavelength', wlen, ...
'bitrate', baudrate, ...
'is_mpi', 0, ...
'equalizer_structure', curves(k).eq, ...
'pre_emph', pre_emph);
A = analyze_measurements_gpt(dataTable, cfg);
results(k).x = A.group{1}.x;
if precoded
results(k).ber = A.group{1}.y_precoded;
else
results(k).ber = A.group{1}.y;
end
end
%% ============================================================
% PLOT — BER vs ROP (Single Axis)
% ============================================================
fig = figure(); clf; hold on;
lw = 2.0;
ms = 7;
for k = 1:numel(curves)
plot(results(k).x, results(k).ber, ...
'-o', ...
'Color', curves(k).color, ...
'MarkerFaceColor', curves(k).color, ...
'MarkerSize', ms, ...
'LineWidth', lw, ...
'DisplayName', curves(k).name);
end
set(gca,'YScale','log');
grid on;
xlabel('ROP / Power (MZM) [dBm]');
ylabel('BER');
ylim([1e-4 2e-1]);
title(sprintf('BER vs ROP — PAM-%d, %.0f km, %.0f GBd, %.0f nm', ...
pam_level, fiberL, baudrate*1e-9, wlen));
legend('Location','best');
beautifyBERplot();
pos = 1e3.*[0.2 0.6 1.3 0.4];
set(fig, 'Position', pos);
%% ============================================================
% DECISION LOGIC (INLINE FUNCTIONS)
% ============================================================
function pe = decide_preemph(M, eq)
% PRE-EMPH RULES:
switch M
case 4
if eq == equalizer_structure.vnle
pe = 1; % PAM4: VNLE → pre-emph on
else
pe = 0; % PAM4: all others → off
end
case {6,8}
pe = 1; % PAM6/8: all → pre-emph on
otherwise
pe = 0;
end
end
function flag = decide_precoded(M, eq)
% PRE-CODE RULES:
if eq == equalizer_structure.vnle_db_mlse
flag = 1; % Always for DB-target
elseif eq == equalizer_structure.ml_mlse && M == 4
flag = 1; % PAM4: ML-based → precoded
else
flag = 0;
end
end