FFE now has a automatic optimization of the mu-parameters -> enables the fast comparison of LMS, NLMS and RLS and also good for very long filters (1000+ taps) which might be unstable else.
Minor changes here and there
This commit is contained in:
Silas Oettinghaus
@@ -15,7 +15,7 @@ classdef FFE < handle
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e
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e
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e_tr
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e_tr
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error
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error
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debug_struct
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len_tr
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len_tr
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mu_tr
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mu_tr
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@@ -31,6 +31,13 @@ classdef FFE < handle
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constellation % symbol constellation
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constellation % symbol constellation
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decide %wether to return the (hard) decisions or the result after FFE (soft)
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decide %wether to return the (hard) decisions or the result after FFE (soft)
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save_debug = 0;
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debug_struct
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optmize_mus = 0;
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mu_optimization
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mu_optimization_iter = 0;
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end
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end
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methods
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methods
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@@ -51,6 +58,9 @@ classdef FFE < handle
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options.decide = false;
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options.decide = false;
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options.save_debug = 0;
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options.optmize_mus = 0;
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end
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end
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fn = fieldnames(options);
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fn = fieldnames(options);
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@@ -75,6 +85,12 @@ classdef FFE < handle
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delta = 0.05;
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delta = 0.05;
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obj.P = (1/delta) * eye(obj.order);
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obj.P = (1/delta) * eye(obj.order);
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if obj.optmize_mus
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obj.optimizeMus(X.signal,D.signal);
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obj.e = zeros(obj.order,1);
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obj.P = (1/delta) * eye(obj.order);
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end
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% Training Mode
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% Training Mode
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training = 1;
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training = 1;
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showviz = 0;
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showviz = 0;
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@@ -131,7 +147,6 @@ classdef FFE < handle
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mask = ones(obj.order,1);
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mask = ones(obj.order,1);
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maincursor_pos=ceil(length(obj.e)/2);
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maincursor_pos=ceil(length(obj.e)/2);
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always_ideal_decision = 0;
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always_ideal_decision = 0;
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save_debug = 0;
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grad =0;
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grad =0;
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weight = 0;
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weight = 0;
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update = 0;
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update = 0;
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@@ -205,21 +220,88 @@ classdef FFE < handle
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end
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end
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if save_debug
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if obj.save_debug
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obj.debug_struct.error(epoch,symbol) = err(symbol) * err(symbol)'; % Instantaneous square error
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obj.debug_struct.error(epoch,symbol) = err(symbol) * err(symbol)'; % Instantaneous square error
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if training
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if training
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obj.debug_struct.error_tr(epoch,symbol) = err(symbol) * err(symbol)'; % Instantaneous square error
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obj.debug_struct.error_tr(epoch,symbol) = err(symbol) * err(symbol)'; % Instantaneous square error
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obj.debug_struct.update_tr(epoch,symbol) = update.'*update ./ rms(obj.e);
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obj.debug_struct.update_tr(epoch,symbol) = update.'*update ./ rms(obj.e);
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else
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% if symbol == length(d)
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% M = numel(unique(d));
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% bits_ref = PAMmapper(M,0).demap(d);
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% bits_eq = PAMmapper(M,0).demap(d_hat);
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% [~,~,obj.debug_struct.ber(epoch)] = calc_ber(bits_eq,bits_ref);
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% end
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end
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end
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obj.debug_struct.main_cursor(epoch,symbol) = abs(obj.e(maincursor_pos));
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obj.debug_struct.mu_nlms(epoch,symbol) = weight;
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% obj.debug_struct.main_cursor(epoch,symbol) = abs(obj.e(maincursor_pos));
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obj.debug_struct.update_gradient(epoch,symbol) = grad.'*grad;
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% obj.debug_struct.mu_nlms(epoch,symbol) = weight;
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obj.debug_struct.update(epoch,symbol) = update.'*update ./ rms(obj.e);
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% obj.debug_struct.update_gradient(epoch,symbol) = grad.'*grad;
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% obj.debug_struct.update(epoch,symbol) = update.'*update ./ rms(obj.e);
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end
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end
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end
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end
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end
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end
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end
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end
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function optimizeMus(obj,x,d)
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switch obj.adaption_technique
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case adaption_method.lms
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mu_range = [1e-5, 1e-2];
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case adaption_method.nlms
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mu_range = [1e-3, 5e-1];
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case adaption_method.rls
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mu_range = [0.98, 0.99999];
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end
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mu_tr_var = optimizableVariable("mu_tr",mu_range,"Transform","log");
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vars = mu_tr_var;
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if obj.dd_mode
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vars = [vars, optimizableVariable("mu_dd",mu_range,"Transform","log")];
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end
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obj.mu_optimization_iter = 0;
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obj.mu_optimization = bayesopt(@(p)obj.muObjective(p,x,d),vars, ...
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"MaxObjectiveEvaluations",10, ...
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"AcquisitionFunctionName","expected-improvement-plus", ...
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"IsObjectiveDeterministic",false, ...
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"Verbose",0, ...
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"PlotFcn",[]);
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obj.mu_tr = obj.mu_optimization.XAtMinObjective.mu_tr;
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if obj.dd_mode
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obj.mu_dd = obj.mu_optimization.XAtMinObjective.mu_dd;
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end
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fprintf("\nFFE mu opt done: mu_tr=%9.3e, mu_dd=%9.3e, metric=%9.3e\n", ...
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obj.mu_tr,obj.mu_dd,obj.mu_optimization.MinObjective);
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end
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function objective = muObjective(obj,params,x,d)
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old_debug = obj.save_debug;
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obj.save_debug = 1;
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obj.e = zeros(obj.order,1);
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obj.P = (1/0.05) * eye(obj.order);
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obj.debug_struct = struct();
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obj.equalize(x,d,params.mu_tr,obj.epochs_tr,obj.len_tr,1,0);
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if obj.dd_mode
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obj.equalize(x,d,params.mu_dd,obj.epochs_dd,numel(x),0,0);
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objective = mean(obj.debug_struct.error(end,:),"omitnan");
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else
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objective = mean(obj.debug_struct.error_tr(end,:),"omitnan");
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end
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if ~isfinite(objective)
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objective = inf;
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end
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obj.mu_optimization_iter = obj.mu_optimization_iter + 1;
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if obj.dd_mode
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fprintf("\rFFE mu opt %02d: mu_tr=%9.3e, mu_dd=%9.3e, metric=%9.3e", ...
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obj.mu_optimization_iter,params.mu_tr,params.mu_dd,objective);
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else
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fprintf("\rFFE mu opt %02d: mu_tr=%9.3e, metric=%9.3e", ...
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obj.mu_optimization_iter,params.mu_tr,objective);
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end
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obj.save_debug = old_debug;
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end
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end
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end
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end
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end
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@@ -54,7 +54,7 @@ classdef DBHandler < handle
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options.password, ... % Password (or getSecret)
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options.password, ... % Password (or getSecret)
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"Vendor", "MySQL", ...
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"Vendor", "MySQL", ...
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"Server", options.server, ...
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"Server", options.server, ...
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"PortNumber", 3306, ...
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"PortNumber", options.port, ...
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"JDBCDriverLocation", "C:\Users\Silas\Documents\mysql-connector-j-9.3.0\mysql-connector-j-9.3.0.jar");
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"JDBCDriverLocation", "C:\Users\Silas\Documents\mysql-connector-j-9.3.0\mysql-connector-j-9.3.0.jar");
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end
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end
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@@ -7,6 +7,10 @@ arguments
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options.parameters = struct();
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options.parameters = struct();
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options.database_type
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options.database_type
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options.dataBase
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options.dataBase
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options.server = "134.245.243.254";
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options.port = 3306;
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options.user = "silas";
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options.password = "silas";
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options.load_file_path = struct();
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options.load_file_path = struct();
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options.storage_path
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options.storage_path
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options.mode
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options.mode
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@@ -18,7 +22,8 @@ try
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if options.mode == "load_run_id" || options.append_to_db
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if options.mode == "load_run_id" || options.append_to_db
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database = DBHandler("dataBase", [options.dataBase], "type", options.database_type, ...
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database = DBHandler("dataBase", [options.dataBase], "type", options.database_type, ...
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'user', "silas", 'password', "silas", 'server', "134.245.243.254");
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"user", options.user, "password", options.password, ...
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"server", options.server, "port", options.port);
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end
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end
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if options.mode == "load_run_id"
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if options.mode == "load_run_id"
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@@ -68,7 +73,6 @@ function output = initializeOutput()
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end
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end
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function dspInput = loadDspInputFromFiles(run_id, options)
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function dspInput = loadDspInputFromFiles(run_id, options)
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%#ok<INUSD>
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Tx_bits = load(options.load_file_path.tx_bits_path);
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Tx_bits = load(options.load_file_path.tx_bits_path);
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Symbols = load(options.load_file_path.tx_symbols_path);
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Symbols = load(options.load_file_path.tx_symbols_path);
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Scpe_sig_raw = load(options.load_file_path.rx_raw_path);
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Scpe_sig_raw = load(options.load_file_path.rx_raw_path);
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@@ -82,6 +82,9 @@ try
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eq_.b2 = [];
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eq_.b2 = [];
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eq_.b3 = [];
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eq_.b3 = [];
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end
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end
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try
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eq_.mu_optimization = [];
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end
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ffe_results.config = Equalizerstruct();
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ffe_results.config = Equalizerstruct();
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ffe_results.config.eq = jsonencode(eq_);
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ffe_results.config.eq = jsonencode(eq_);
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@@ -274,32 +274,32 @@ showEQNoisePSD(eq_noise, "fignum", 338, "displayname", 'Residual Noise after VNL
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for t = 1:4
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for t = 1:4
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pf_.ncoeff = t;
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pf_.ncoeff = t;
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[~,~] = pf_.process(eq_signal_sd, eq_noise);
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[~,~] = pf_.process(eq_signal_sd, eq_noise);
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showEQNoisePSD(eq_noise, "fignum", 3388, "displayname", 'Residual Noise after VNLE', 'postfilter_taps', pf_.coefficients);
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showEQNoisePSD(eq_noise, "fignum", 339, "displayname", 'Residual Noise after VNLE', 'postfilter_taps', pf_.coefficients);
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end
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end
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tx_symbols.spectrum("displayname",'Equalized Signal','fignum',1234,'normalizeTo0dB',1);
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tx_symbols.spectrum("displayname",'Equalized Signal','fignum',340,'normalizeTo0dB',1);
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if ~isempty(postFFE)
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if ~isempty(postFFE)
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showEQcoefficients('n1', postFFE.e, "displayname", 'Coefficients', 'fignum', 338);
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showEQcoefficients('n1', postFFE.e, "displayname", 'Coefficients', 'fignum', 341);
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end
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end
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% showEQcoefficients('n1', eq_.e1,'n2', eq_.e2,'n3', eq_.e3, "displayname", 'Coefficients', 'fignum', 339);
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% showEQcoefficients('n1', eq_.e1,'n2', eq_.e2,'n3', eq_.e3, "displayname", 'Coefficients', 'fignum', 339);
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showEQfilter(eq_.e, eq_signal_sd.fs.*2);
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showEQfilter(eq_.e, eq_signal_sd.fs.*2);
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figure(340); clf;
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figure(340); clf;
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eq_signal_sd.eye(eq_signal_sd.fs, M, "fignum", 340);
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eq_signal_sd.eye(eq_signal_sd.fs, M, "fignum", 342);
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figure(341); clf;
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figure(341); clf;
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showLevelHistogram(eq_signal_sd, tx_symbols, "fignum", 341);
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showLevelHistogram(eq_signal_sd, tx_symbols, "fignum", 343);
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warning off
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warning off
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showLevelScatter(eq_signal_sd, tx_symbols, "fignum", 400);
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showLevelScatter(eq_signal_sd, tx_symbols, "fignum", 344);
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% showLevelScatter(rx_signal.resample("fs_out", tx_symbols.fs), tx_symbols, "fignum", 401);
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% showLevelScatter(rx_signal.resample("fs_out", tx_symbols.fs), tx_symbols, "fignum", 401);
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drawnow;
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drawnow;
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warning on
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warning on
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whitened_noise.spectrum("displayname",'after postfilter','fignum',342);
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whitened_noise.spectrum("displayname",'after postfilter','fignum',345);
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eq_noise.spectrum("displayname",'before postfilter','fignum',342);
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eq_noise.spectrum("displayname",'before postfilter','fignum',345);
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end
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end
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@@ -36,17 +36,10 @@ for r = 1:nRunIds
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for k = 1:nJobsPerRunId
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for k = 1:nJobsPerRunId
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jobCounter = jobCounter + 1;
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jobCounter = jobCounter + 1;
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optionalVars = buildOptionalVars(k, submit_options.wh);
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optionalVars = buildOptionalVars(k, submit_options.wh);
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jobOptions = dsp_options;
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jobOptions.parameters = optionalVars;
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jobs(jobCounter).args = { ...
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jobs(jobCounter).args = [{run_ids(r)}, structToNameValue(jobOptions)];
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run_ids(r), ...
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"database_type", dsp_options.database_type, ...
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"dataBase", dsp_options.dataBase, ...
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"append_to_db", dsp_options.append_to_db, ...
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"load_file_path", dsp_options.load_file_path, ...
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"max_occurences", dsp_options.max_occurences, ...
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"storage_path", dsp_options.storage_path, ...
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"mode", dsp_options.mode, ...
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"parameters", optionalVars};
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jobs(jobCounter).label = sprintf('RunID %d, Job %d', run_ids(r), k);
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jobs(jobCounter).label = sprintf('RunID %d, Job %d', run_ids(r), k);
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jobs(jobCounter).meta.runIndex = r;
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jobs(jobCounter).meta.runIndex = r;
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jobs(jobCounter).meta.jobIndex = k;
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jobs(jobCounter).meta.jobIndex = k;
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@@ -91,6 +84,16 @@ end
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end
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end
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end
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end
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function nameValue = structToNameValue(options)
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names = fieldnames(options);
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nameValue = cell(1, 2*numel(names));
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for i = 1:numel(names)
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nameValue{2*i - 1} = string(names{i});
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nameValue{2*i} = options.(names{i});
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end
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end
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function storeResult(val, job, ~)
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function storeResult(val, job, ~)
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if ~isempty(wh)
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if ~isempty(wh)
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jobIndex = job.meta.jobIndex;
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jobIndex = job.meta.jobIndex;
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342
Theory/Signal_Processing/EQ_adaption.m
Normal file
342
Theory/Signal_Processing/EQ_adaption.m
Normal file
@@ -0,0 +1,342 @@
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% minimal example IM/DD
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M = 4;
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fsym = 180e9;
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apply_pulsef = 1;
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fdac = 256e9;
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fadc = 256e9;
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random_key = 1;
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rcalpha = 0.05;
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kover = 16;
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duob_mode = db_mode.no_db;
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vbias_rel = 0.5;
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u_pi = 3;
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vbias = -vbias_rel*u_pi;
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laser_wavelength = 1293;
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laser_linewidth = 0;
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tx_bw_nyquist = 0.8;
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% Channel
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link_length = 1;
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% RX
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rop = -7;
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rx_bw_nyquist = 0.8;
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vnle_order1 = 50;
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vnle_order2 = 7;
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vnle_order3 = 7;
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vnle_order=[vnle_order1,vnle_order2,vnle_order3];
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dfe_order = [0 0 0];
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pf_ncoeffs = 1;
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alpha = 0;
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|
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len_tr = 4096*2;
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|
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mu_ffe1 = 0.0001;
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mu_ffe2 = 0.0008;
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mu_ffe3 = 0.001;
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||||||
|
mu_dc = 0.005;
|
||||||
|
% mu_dc = 0;
|
||||||
|
|
||||||
|
mu_ffe = [mu_ffe1 mu_ffe3 mu_ffe3];
|
||||||
|
mu_dfe = 0.0004;
|
||||||
|
|
||||||
|
n_stat_runs = 1;
|
||||||
|
base_random_key = random_key;
|
||||||
|
output.eq_stats = struct();
|
||||||
|
|
||||||
|
for stat_run = 1:n_stat_runs
|
||||||
|
random_key = base_random_key + stat_run - 1;
|
||||||
|
Pform = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rrc","pulselength",16,"alpha",rcalpha);
|
||||||
|
|
||||||
|
[Digi_sig,Symbols,Tx_bits] = PAMsource(...
|
||||||
|
"fsym",fsym,"M",M,"order",16,"useprbs",0,...
|
||||||
|
"fs_out",fdac,...
|
||||||
|
"applyclipping",0,"clipfactor",1.5,...
|
||||||
|
"applypulseform",apply_pulsef,"pulseformer",Pform,...
|
||||||
|
"randkey",random_key,...
|
||||||
|
'duobinary_mode',duob_mode,...
|
||||||
|
"mrds_code",0,"mrds_blocklength",512).process();
|
||||||
|
|
||||||
|
%%%%% AWG
|
||||||
|
El_sig = M8199A("kover",kover).process(Digi_sig);
|
||||||
|
% El_sig = AWG("fdac",fdac,"f_cutoff",fsym,"lpf_active",0,"kover",kover,"bit_resolution",12,"upsampling_method","samplehold","precomp_sinc_rolloff",1).process(Digi_sig);
|
||||||
|
El_sig.spectrum("displayname",'Digi Spectrum','fignum',1,'normalizeTo0dB',1);
|
||||||
|
xlim([0,130]);
|
||||||
|
ylim([-30,5]);
|
||||||
|
% El_sig = El_sig.setPower(0,"dBm");
|
||||||
|
|
||||||
|
%%%%% Electrical Driver Amplifier %%%%%%
|
||||||
|
% El_sig = Amplifier("amp_mode","ideal_no_noise","gain_mode","gain","amplification_db",3).process(El_sig);
|
||||||
|
El_sig = El_sig.normalize("mode","oneone");
|
||||||
|
scaling = 0.6*(u_pi/2-abs(vbias-u_pi/2));
|
||||||
|
El_sig = El_sig .* scaling;
|
||||||
|
|
||||||
|
%%%%% MODULATE E/O CONVERSION %%%%%%
|
||||||
|
[Opt_sig] = EML("mode",eml_mode.im_cosinus,"power",3,"fsimu",El_sig.fs,"lambda",laser_wavelength,"bias",vbias,"u_pi",u_pi,"linewidth",laser_linewidth,"randomkey",random_key+1,"alpha",alpha).process(El_sig);
|
||||||
|
|
||||||
|
Opt_sig.spectrum("displayname",'Opt Spectrum','fignum',10,'normalizeTo0dB',1);
|
||||||
|
|
||||||
|
% Opt_sig.eye(fsym,M,"displayname",'eye adter modulator','fignum',2026);
|
||||||
|
|
||||||
|
Opt_sig = Fiber("fsimu",Opt_sig.fs,"fiber_length",link_length,"alpha",0.3,"D",0,"lambda0",1310,"gamma",0,"Dslope",0.07).process(Opt_sig);
|
||||||
|
|
||||||
|
%%%%%% ROP %%%%%%
|
||||||
|
Rx_sig = Amplifier("amp_mode","ideal_no_noise","gain_mode","output_power","amplification_db",rop).process(Opt_sig);
|
||||||
|
|
||||||
|
%%%%%% PD Square Law %%%%%%
|
||||||
|
Rx_sig = Photodiode("fsimu",fdac*kover,"dark_current",2e-08,"responsivity",1,"temperature",20,"nep",1.8e-11).process(Rx_sig);
|
||||||
|
|
||||||
|
%%%%%% Low-pass RX (PD, El. Connectors and Scope %%%%%%
|
||||||
|
rx_bwl = 80e9;
|
||||||
|
Rx_sig = Filter('filtdegree',4,"f_cutoff",rx_bwl,"fs",fdac*kover,"filterType",filtertypes.butterworth,"active",true).process(Rx_sig);
|
||||||
|
|
||||||
|
% %%%%%% Low-pass Scope %%%%%%
|
||||||
|
Lp_scpe = Filter('filtdegree',4,"f_cutoff",110e9,"fs",fadc,"filterType",filtertypes.butterworth,"active",true);
|
||||||
|
|
||||||
|
%%%%%% Scope %%%%%%
|
||||||
|
Scpe_sig = Scope("fsimu",fdac*kover,"fadc",fadc,...
|
||||||
|
"delay",0,"fixed_delay",0,"filtertype",filtertypes.butterworth,...
|
||||||
|
"samplingdelay",0,"rand_samplingdelay",0,"freq_offset",0,"samp_jitter",0,...
|
||||||
|
"adcresolution",8,"quantbuffer",0.1,'block_dc',1,'lpf_active',1,'H_lpf',Lp_scpe).process(Rx_sig);
|
||||||
|
%%
|
||||||
|
|
||||||
|
% 1) matched filter
|
||||||
|
% pulse is symmetric, hence we can use pulsef firectly as matched filter.
|
||||||
|
% It feels off (bit I think correct) that the fsym is now the output freq.!!
|
||||||
|
% -> output 2 sps to omit timing recovery!?
|
||||||
|
Pform = Pulseformer("fsym",fsym,"fdac",2*fsym,"pulse","rrc","pulselength",16,"alpha",rcalpha,"matched",1);
|
||||||
|
Scpe_sig = Pform.process(Scpe_sig);
|
||||||
|
Scpe_sig.spectrum("displayname",'Signal after matched filter','fignum',1,'normalizeTo0dB',1);
|
||||||
|
%
|
||||||
|
|
||||||
|
% %%
|
||||||
|
% %%%%%% Sample to 2x fsym %%%%%%
|
||||||
|
% Scpe_sig = Scpe_sig.resample("fs_out",2*fsym);
|
||||||
|
% Scpe_sig.signal = Scpe_sig.signal(1:2*length(Symbols));
|
||||||
|
|
||||||
|
%%
|
||||||
|
%%%%%% Sync Rx signal with reference %%%%%%
|
||||||
|
[Scpe_sig,~] = Scpe_sig.tsynch("reference",Symbols,"fs_ref",fsym,"debug_plots",0);
|
||||||
|
Scpe_sig.spectrum("displayname",'Opt Spectrum','fignum',11,'normalizeTo0dB',1);
|
||||||
|
|
||||||
|
% Scpe_sig = Filter('filtdegree',4,"f_cutoff",Symbols.fs.*0.5,"fs",Scpe_sig.fs,"filterType",filtertypes.gaussian,"active",true).process(Scpe_sig);
|
||||||
|
|
||||||
|
Scpe_sig = Scpe_sig - mean(Scpe_sig.signal);
|
||||||
|
Scpe_sig.signal = Scpe_sig.signal(1:2*length(Symbols));
|
||||||
|
|
||||||
|
%% -------------------- FFE --------------------
|
||||||
|
ffe_options = struct( ...
|
||||||
|
"epochs_tr", 3, ...
|
||||||
|
"epochs_dd", 3, ...
|
||||||
|
"len_tr", 4096, ...
|
||||||
|
"order", 40, ...
|
||||||
|
"sps", 2, ...
|
||||||
|
"decide", 0, ...
|
||||||
|
"adaption", adaption_method.rls, ...
|
||||||
|
"dd_mode", 1);
|
||||||
|
|
||||||
|
eq_nlms = FFE("epochs_tr",ffe_options.epochs_tr,"epochs_dd",ffe_options.epochs_dd, ...
|
||||||
|
"len_tr",ffe_options.len_tr,"mu_dd",0.001,"mu_tr",0.001, ...
|
||||||
|
"order",ffe_options.order,"sps",ffe_options.sps,"decide",ffe_options.decide, ...
|
||||||
|
"adaption",ffe_options.adaption,"dd_mode",ffe_options.dd_mode,"save_debug",1,"optmize_mus",1);
|
||||||
|
|
||||||
|
output.ffe_results = ffe(eq_nlms,M,Scpe_sig,Symbols,Tx_bits, ...
|
||||||
|
"precode_mode",duob_mode,'showAnalysis',0,"postFFE",[], ...
|
||||||
|
"eth_style_symbol_mapping",0);
|
||||||
|
|
||||||
|
output.ffe_results.metrics.print("description","FFE");
|
||||||
|
|
||||||
|
%%
|
||||||
|
|
||||||
|
% -------------------- FFE --------------------
|
||||||
|
%%
|
||||||
|
|
||||||
|
for i = 3:-1:1
|
||||||
|
|
||||||
|
mu_adaptation = adaption_method(i);
|
||||||
|
|
||||||
|
switch mu_adaptation
|
||||||
|
case adaption_method.lms
|
||||||
|
mu_search_range = [1e-5, 1e-2];
|
||||||
|
|
||||||
|
case adaption_method.nlms
|
||||||
|
mu_search_range = [1e-3, 5e-1];
|
||||||
|
|
||||||
|
case adaption_method.rls
|
||||||
|
mu_search_range = [0.98, 0.99999]; % here mu is lambda
|
||||||
|
end
|
||||||
|
mu_max_evaluations = 10;
|
||||||
|
|
||||||
|
ffe_options = struct( ...
|
||||||
|
"epochs_tr", 3, ...
|
||||||
|
"epochs_dd", 2, ...
|
||||||
|
"len_tr", 4096*2, ...
|
||||||
|
"order", 20, ...
|
||||||
|
"sps", 2, ...
|
||||||
|
"decide", 0, ...
|
||||||
|
"adaption", mu_adaptation, ...
|
||||||
|
"dd_mode", 1);
|
||||||
|
|
||||||
|
method_name = char(mu_adaptation);
|
||||||
|
rng(random_key, "twister");
|
||||||
|
mu_dd_var = optimizableVariable("mu_dd", mu_search_range, "Transform", "log");
|
||||||
|
mu_tr_var = optimizableVariable("mu_tr", mu_search_range, "Transform", "log");
|
||||||
|
global ffe_mu_trace
|
||||||
|
ffe_mu_trace = table('Size',[0 5], ...
|
||||||
|
'VariableTypes',{'double','double','double','double','double'}, ...
|
||||||
|
'VariableNames',{'mu_dd','mu_tr','BER','SNR','mse_last_epoch'});
|
||||||
|
objective_fn = @(params) ffe_mu_objective(params, ffe_options, M, Scpe_sig, Symbols, Tx_bits, duob_mode);
|
||||||
|
|
||||||
|
output.eq_stats.(method_name).mu_optimization{stat_run} = bayesopt(objective_fn, [mu_dd_var, mu_tr_var], ...
|
||||||
|
"MaxObjectiveEvaluations", mu_max_evaluations, ...
|
||||||
|
"AcquisitionFunctionName", "expected-improvement-plus", ...
|
||||||
|
"IsObjectiveDeterministic", false, ...
|
||||||
|
"Verbose", 1, ...
|
||||||
|
"PlotFcn", []);
|
||||||
|
|
||||||
|
output.eq_stats.(method_name).mu_observed(stat_run,:) = output.eq_stats.(method_name).mu_optimization{stat_run}.XAtMinObjective;
|
||||||
|
output.eq_stats.(method_name).mu_estimated(stat_run,:) = output.eq_stats.(method_name).mu_optimization{stat_run}.XAtMinEstimatedObjective;
|
||||||
|
output.eq_stats.(method_name).optimization_trace{stat_run} = ffe_mu_trace;
|
||||||
|
|
||||||
|
best_mu_dd = output.eq_stats.(method_name).mu_observed.mu_dd(stat_run);
|
||||||
|
best_mu_tr = output.eq_stats.(method_name).mu_observed.mu_tr(stat_run);
|
||||||
|
output.eq_stats.(method_name).mu_used(stat_run,:) = table(best_mu_dd,best_mu_tr, ...
|
||||||
|
'VariableNames',{'mu_dd','mu_tr'});
|
||||||
|
|
||||||
|
eq_nlms = FFE("epochs_tr",ffe_options.epochs_tr,"epochs_dd",ffe_options.epochs_dd, ...
|
||||||
|
"len_tr",ffe_options.len_tr,"mu_dd",best_mu_dd,"mu_tr",best_mu_tr, ...
|
||||||
|
"order",ffe_options.order,"sps",ffe_options.sps,"decide",ffe_options.decide, ...
|
||||||
|
"adaption",ffe_options.adaption,"dd_mode",ffe_options.dd_mode,"save_debug",1);
|
||||||
|
|
||||||
|
output.ffe_results = ffe(eq_nlms,M,Scpe_sig,Symbols,Tx_bits, ...
|
||||||
|
"precode_mode",duob_mode,'showAnalysis',0,"postFFE",[], ...
|
||||||
|
"eth_style_symbol_mapping",0);
|
||||||
|
|
||||||
|
output.ffe_results.metrics.print("description",sprintf('%s',mu_adaptation));
|
||||||
|
|
||||||
|
output.eq_stats.(method_name).error_tr_first_epoch(stat_run,:) = eq_nlms.debug_struct.error_tr(1,:);
|
||||||
|
|
||||||
|
% figure(6)
|
||||||
|
% hold on
|
||||||
|
% for epoch = 1:ffe_options.epochs_tr
|
||||||
|
% plot(1:size(eq_nlms.debug_struct.error_tr,2),movmean(eq_nlms.debug_struct.error_tr(epoch,:),10));
|
||||||
|
% end
|
||||||
|
|
||||||
|
end
|
||||||
|
|
||||||
|
end
|
||||||
|
%%
|
||||||
|
|
||||||
|
figure(5);
|
||||||
|
clf;
|
||||||
|
hold on;
|
||||||
|
method_names = fieldnames(output.eq_stats);
|
||||||
|
cols = cbrewer2('Paired',6);
|
||||||
|
cols = cols(2:2:end,:);
|
||||||
|
for i = 1:numel(method_names)
|
||||||
|
method_name = method_names{i};
|
||||||
|
first_epoch = output.eq_stats.(method_name).error_tr_first_epoch;
|
||||||
|
|
||||||
|
|
||||||
|
for j = 1:size(first_epoch,1)
|
||||||
|
plot(movmean(output.eq_stats.(method_name).error_tr_first_epoch(j,:),10), ...
|
||||||
|
"DisplayName",method_name,"Color",[0.7,0.7,0.7],"LineWidth",0.1,'HandleVisibility','off');
|
||||||
|
end
|
||||||
|
|
||||||
|
end
|
||||||
|
|
||||||
|
for i = 1:numel(method_names)
|
||||||
|
method_name = method_names{i};
|
||||||
|
first_epoch = output.eq_stats.(method_name).error_tr_first_epoch;
|
||||||
|
|
||||||
|
output.eq_stats.(method_name).error_tr_first_epoch_mean = mean(first_epoch,1,"omitnan");
|
||||||
|
output.eq_stats.(method_name).error_tr_first_epoch_std = std(first_epoch,0,1,"omitnan");
|
||||||
|
plot(movmean(output.eq_stats.(method_name).error_tr_first_epoch_mean,50), ...
|
||||||
|
"DisplayName",method_name,"Color",cols(i,:));
|
||||||
|
end
|
||||||
|
|
||||||
|
|
||||||
|
grid on;
|
||||||
|
xlabel("Training symbol index","Interpreter","tex");
|
||||||
|
ylabel("Mean squared error, first epoch","Interpreter","tex");
|
||||||
|
legend("Location","best","Interpreter","tex");
|
||||||
|
|
||||||
|
figure(6);
|
||||||
|
clf;
|
||||||
|
tiledlayout(numel(method_names),3,"TileSpacing","compact","Padding","compact");
|
||||||
|
metric_names = {'BER','SNR','mse_last_epoch'};
|
||||||
|
metric_labels = {'BER','SNR','mean abs(error)^2, last epoch'};
|
||||||
|
for i = 1:numel(method_names)
|
||||||
|
method_name = method_names{i};
|
||||||
|
trace = vertcat(output.eq_stats.(method_name).optimization_trace{:});
|
||||||
|
observed_mu = output.eq_stats.(method_name).mu_observed;
|
||||||
|
estimated_mu = output.eq_stats.(method_name).mu_estimated;
|
||||||
|
for metric_idx = 1:numel(metric_names)
|
||||||
|
nexttile;
|
||||||
|
metric = trace.(metric_names{metric_idx});
|
||||||
|
if metric_idx ~= 2
|
||||||
|
metric = log10(max(metric,realmin));
|
||||||
|
end
|
||||||
|
scatter(trace.mu_tr,trace.mu_dd,25,metric,"filled");
|
||||||
|
hold on;
|
||||||
|
plot(observed_mu.mu_tr,observed_mu.mu_dd,"kp","MarkerSize",11, ...
|
||||||
|
"MarkerFaceColor","y","LineWidth",1.2,"DisplayName","Observed best");
|
||||||
|
plot(estimated_mu.mu_tr,estimated_mu.mu_dd,"kx","MarkerSize",10, ...
|
||||||
|
"LineWidth",1.6,"DisplayName","Estimated best");
|
||||||
|
set(gca,"XScale","log","YScale","log");
|
||||||
|
grid on;
|
||||||
|
xlabel('\mu_{tr}',"Interpreter","tex");
|
||||||
|
ylabel('\mu_{dd}',"Interpreter","tex");
|
||||||
|
title(sprintf("%s: %s",method_name,metric_labels{metric_idx}),"Interpreter","tex");
|
||||||
|
cb = colorbar;
|
||||||
|
if metric_idx ~= 2
|
||||||
|
cb.Label.String = sprintf("log10(%s)",metric_labels{metric_idx});
|
||||||
|
else
|
||||||
|
cb.Label.String = metric_labels{metric_idx};
|
||||||
|
end
|
||||||
|
cb.Label.Interpreter = "tex";
|
||||||
|
legend("Location","best","Interpreter","tex");
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function objective = ffe_mu_objective(params, ffe_options, M, rx_signal, tx_symbols, tx_bits, duob_mode)
|
||||||
|
global ffe_mu_trace
|
||||||
|
try
|
||||||
|
eq_candidate = FFE("epochs_tr",ffe_options.epochs_tr,"epochs_dd",ffe_options.epochs_dd, ...
|
||||||
|
"len_tr",ffe_options.len_tr,"mu_dd",params.mu_dd,"mu_tr",params.mu_tr, ...
|
||||||
|
"order",ffe_options.order,"sps",ffe_options.sps,"decide",ffe_options.decide, ...
|
||||||
|
"adaption",ffe_options.adaption,"dd_mode",ffe_options.dd_mode,"save_debug",1);
|
||||||
|
|
||||||
|
candidate_results = ffe(eq_candidate, M, rx_signal, tx_symbols, tx_bits, ...
|
||||||
|
"precode_mode",duob_mode,'showAnalysis',0,"postFFE",[], ...
|
||||||
|
"eth_style_symbol_mapping",0);
|
||||||
|
|
||||||
|
objective = candidate_results.metrics.BER;
|
||||||
|
if ~isfinite(objective)
|
||||||
|
objective = 0.5;
|
||||||
|
end
|
||||||
|
mse_last_epoch = mean(eq_candidate.debug_struct.error_tr(end,:),"omitnan");
|
||||||
|
ffe_mu_trace = [ffe_mu_trace; table(params.mu_dd,params.mu_tr,objective, ...
|
||||||
|
candidate_results.metrics.SNR,mse_last_epoch, ...
|
||||||
|
'VariableNames',{'mu_dd','mu_tr','BER','SNR','mse_last_epoch'})];
|
||||||
|
|
||||||
|
fprintf("mu_dd=%9.3e, mu_tr=%9.3e -> BER=%9.3e, SNR=%6.2f dB, MSE=%9.3e\n", ...
|
||||||
|
params.mu_dd, params.mu_tr, objective, candidate_results.metrics.SNR, mse_last_epoch);
|
||||||
|
catch ME
|
||||||
|
objective = 0.5;
|
||||||
|
ffe_mu_trace = [ffe_mu_trace; table(params.mu_dd,params.mu_tr,objective,NaN,NaN, ...
|
||||||
|
'VariableNames',{'mu_dd','mu_tr','BER','SNR','mse_last_epoch'})];
|
||||||
|
fprintf("mu_dd=%9.3e, mu_tr=%9.3e -> failed: %s\n", ...
|
||||||
|
params.mu_dd, params.mu_tr, ME.message);
|
||||||
|
end
|
||||||
|
end
|
||||||
@@ -1,6 +1,17 @@
|
|||||||
dsp_options.storage_path = 'Z:\2024\sioe_labor\';
|
dsp_options.storage_path = 'Z:\2024\sioe_labor\';
|
||||||
dsp_options.max_occurences = 1;
|
dsp_options.max_occurences = 1;
|
||||||
database = DBHandler("dataBase", 'labor_highspeed', "type", 'mysql' );
|
% database = DBHandler("dataBase", 'labor_highspeed', "type", 'mysql' );
|
||||||
|
dsp_options.database_type = "mysql";
|
||||||
|
dsp_options.dataBase = 'labor_highspeed';%'C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\silas_labor_newdsp_newstructure.db';
|
||||||
|
dsp_options.server = "192.168.178.192";% "134.245.243.254";
|
||||||
|
dsp_options.user = "silas";
|
||||||
|
dsp_options.password = "silas";
|
||||||
|
dsp_options.storage_path = 'W:\labdata\sioe_labor\';
|
||||||
|
database = DBHandler("dataBase", [dsp_options.dataBase],...
|
||||||
|
"type", dsp_options.database_type,...
|
||||||
|
"server", dsp_options.server,...
|
||||||
|
"user", dsp_options.user, "password", dsp_options.password);
|
||||||
|
|
||||||
|
|
||||||
rates = [420e9];
|
rates = [420e9];
|
||||||
cols = cbrewer2('BuPu',25);
|
cols = cbrewer2('BuPu',25);
|
||||||
@@ -10,10 +21,10 @@ cols = cbrewer2('Set1',6);
|
|||||||
fignum = 200;
|
fignum = 200;
|
||||||
fig=figure(fignum);clf;
|
fig=figure(fignum);clf;
|
||||||
|
|
||||||
for dbmode = 1%length(rates)
|
for dbmode = 0%length(rates)
|
||||||
|
|
||||||
|
|
||||||
if 0
|
if 1
|
||||||
rcalpha = 0.05;
|
rcalpha = 0.05;
|
||||||
fsym = rates/2;
|
fsym = rates/2;
|
||||||
pulsef = 1;
|
pulsef = 1;
|
||||||
@@ -41,7 +52,7 @@ for dbmode = 1%length(rates)
|
|||||||
maxamp = -37;
|
maxamp = -37;
|
||||||
precomp_est = ChannelFreqResp("Nacq",2048,"Navg",100,"Ncp",63,'f_ref',Digi_sig.fs);
|
precomp_est = ChannelFreqResp("Nacq",2048,"Navg",100,"Ncp",63,'f_ref',Digi_sig.fs);
|
||||||
|
|
||||||
precomp_path = "C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\precomp";
|
precomp_path = "W:\labdata\sioe_labor\precomp";
|
||||||
precomp_fn = "lab_high_speed";
|
precomp_fn = "lab_high_speed";
|
||||||
Digi_sig_pre = precomp_est.precomp(Digi_sig,'maxampdb',maxamp,'loadPath',precomp_path,'fileName',precomp_fn);
|
Digi_sig_pre = precomp_est.precomp(Digi_sig,'maxampdb',maxamp,'loadPath',precomp_path,'fileName',precomp_fn);
|
||||||
|
|
||||||
|
|||||||
@@ -1,7 +1,16 @@
|
|||||||
dsp_options.storage_path = 'Z:\2024\sioe_labor\';
|
dsp_options.storage_path = 'Z:\2024\sioe_labor\';
|
||||||
dsp_options.max_occurences = 1;
|
dsp_options.max_occurences = 1;
|
||||||
database = DBHandler("dataBase", 'labor_highspeed', "type", 'mysql' );
|
% database = DBHandler("dataBase", 'labor_highspeed', "type", 'mysql' );
|
||||||
|
dsp_options.database_type = "mysql";
|
||||||
|
dsp_options.dataBase = 'labor_highspeed';%'C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\silas_labor_newdsp_newstructure.db';
|
||||||
|
dsp_options.server = "192.168.178.192";% "134.245.243.254";
|
||||||
|
dsp_options.user = "silas";
|
||||||
|
dsp_options.password = "silas";
|
||||||
|
dsp_options.storage_path = 'W:\labdata\sioe_labor\';
|
||||||
|
database = DBHandler("dataBase", [dsp_options.dataBase],...
|
||||||
|
"type", dsp_options.database_type,...
|
||||||
|
"server", dsp_options.server,...
|
||||||
|
"user", dsp_options.user, "password", dsp_options.password);
|
||||||
rate = 390e9;
|
rate = 390e9;
|
||||||
|
|
||||||
%% 1 - PAM 4 with preemphasis
|
%% 1 - PAM 4 with preemphasis
|
||||||
@@ -14,7 +23,7 @@ fp.where('Runs', 'wavelength','EQUALS', 1310);
|
|||||||
fp.where('Runs', 'db_mode','EQUALS', 0);
|
fp.where('Runs', 'db_mode','EQUALS', 0);
|
||||||
fp.where('Runs', 'rop_attenuation','EQUAL', 0);
|
fp.where('Runs', 'rop_attenuation','EQUAL', 0);
|
||||||
|
|
||||||
[dataTable,~] = db.queryDB(fp, database.getTableFieldNames('Runs'));
|
[dataTable,~] = database.queryDB(fp, database.getTableFieldNames('Runs'));
|
||||||
|
|
||||||
dataTable = queryRunid(dataTable.run_id, database);
|
dataTable = queryRunid(dataTable.run_id, database);
|
||||||
fsym = dataTable.symbolrate;
|
fsym = dataTable.symbolrate;
|
||||||
@@ -46,7 +55,7 @@ fp.where('Runs', 'wavelength','EQUALS', 1310);
|
|||||||
fp.where('Runs', 'db_mode','EQUALS', 1);
|
fp.where('Runs', 'db_mode','EQUALS', 1);
|
||||||
fp.where('Runs', 'rop_attenuation','EQUAL', 0);
|
fp.where('Runs', 'rop_attenuation','EQUAL', 0);
|
||||||
|
|
||||||
[dataTable,~] = db.queryDB(fp, database.getTableFieldNames('Runs'));
|
[dataTable,~] = database.queryDB(fp, database.getTableFieldNames('Runs'));
|
||||||
|
|
||||||
dataTable = queryRunid(dataTable.run_id, database);
|
dataTable = queryRunid(dataTable.run_id, database);
|
||||||
fsym = dataTable.symbolrate;
|
fsym = dataTable.symbolrate;
|
||||||
|
|||||||
@@ -12,11 +12,14 @@ if dsp_options.mode == "load_run_id"
|
|||||||
|
|
||||||
dsp_options.database_type = "mysql";
|
dsp_options.database_type = "mysql";
|
||||||
dsp_options.dataBase = 'labor_highspeed';%'C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\silas_labor_newdsp_newstructure.db';
|
dsp_options.dataBase = 'labor_highspeed';%'C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\silas_labor_newdsp_newstructure.db';
|
||||||
|
dsp_options.server = "192.168.178.192";% "134.245.243.254";
|
||||||
|
dsp_options.user = "silas";
|
||||||
|
dsp_options.password = "silas";
|
||||||
dsp_options.storage_path = 'W:\labdata\sioe_labor\';
|
dsp_options.storage_path = 'W:\labdata\sioe_labor\';
|
||||||
db = DBHandler("dataBase", [dsp_options.dataBase],...
|
db = DBHandler("dataBase", [dsp_options.dataBase],...
|
||||||
"type", dsp_options.database_type,...
|
"type", dsp_options.database_type,...
|
||||||
"server","192.168.178.192",...% "server","134.245.243.254",...
|
"server", dsp_options.server,...
|
||||||
"user","silas","password","silas");
|
"user", dsp_options.user, "password", dsp_options.password);
|
||||||
|
|
||||||
elseif experiment == "mpi_ecoc_2025"
|
elseif experiment == "mpi_ecoc_2025"
|
||||||
|
|
||||||
|
|||||||
@@ -1,7 +1,7 @@
|
|||||||
% minimal example IM/DD
|
% minimal example IM/DD
|
||||||
|
|
||||||
M = 4;
|
M = 4;
|
||||||
fsym = 112e9;
|
fsym = 180e9;
|
||||||
|
|
||||||
apply_pulsef = 1;
|
apply_pulsef = 1;
|
||||||
fdac = 256e9;
|
fdac = 256e9;
|
||||||
@@ -63,8 +63,8 @@ Pform = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rrc","pulselength",16,"al
|
|||||||
"mrds_code",0,"mrds_blocklength",512).process();
|
"mrds_code",0,"mrds_blocklength",512).process();
|
||||||
|
|
||||||
%%%%% AWG
|
%%%%% AWG
|
||||||
% El_sig = M8199A("kover",kover).process(Digi_sig);
|
El_sig = M8199B("kover",kover).process(Digi_sig);
|
||||||
El_sig = AWG("fdac",fdac,"f_cutoff",fsym,"lpf_active",0,"kover",kover,"bit_resolution",12,"upsampling_method","samplehold","precomp_sinc_rolloff",1).process(Digi_sig);
|
% El_sig = AWG("fdac",fdac,"f_cutoff",fsym,"lpf_active",0,"kover",kover,"bit_resolution",12,"upsampling_method","samplehold","precomp_sinc_rolloff",1).process(Digi_sig);
|
||||||
El_sig.spectrum("displayname",'Digi Spectrum','fignum',1,'normalizeTo0dB',1);
|
El_sig.spectrum("displayname",'Digi Spectrum','fignum',1,'normalizeTo0dB',1);
|
||||||
xlim([0,130]);
|
xlim([0,130]);
|
||||||
ylim([-30,5]);
|
ylim([-30,5]);
|
||||||
@@ -130,24 +130,24 @@ Scpe_sig = Scpe_sig - mean(Scpe_sig.signal);
|
|||||||
Scpe_sig.signal = Scpe_sig.signal(1:2*length(Symbols));
|
Scpe_sig.signal = Scpe_sig.signal(1:2*length(Symbols));
|
||||||
|
|
||||||
%%
|
%%
|
||||||
|
if 0
|
||||||
|
% -------------------- FFE --------------------
|
||||||
|
ffe_order = [50, 0, 0];
|
||||||
|
eq_ = EQ("Ne",ffe_order,"Nb",[2,0,0], ...
|
||||||
|
"training_length",len_tr,"training_loops",5,"dd_loops",5, ...
|
||||||
|
"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005, ...
|
||||||
|
"FFEmu",0,"plotfinal",0,"ideal_dfe",0);
|
||||||
|
|
||||||
% -------------------- FFE --------------------
|
% eq_ = FFE("epochs_tr",4,"epochs_dd",5,"len_tr",4096,"mu_dd",0.01,"mu_tr",0.01,"order",50,"sps",2,"decide",0, "adaption",adaption_method.nlms,"dd_mode",1);
|
||||||
ffe_order = [50, 0, 0];
|
eq_ = FFE_DFE("epochs_tr",5,"epochs_dd",5,"len_tr",512,"ffe_mu_dd",1e-4,"dfe_mu_dd",5e-4,"ffe_mu_tr",0,"dfe_mu_tr",0,"ffe_order",99,"dfe_order",99,"sps",2,"decide",0);
|
||||||
eq_ = EQ("Ne",ffe_order,"Nb",[2,0,0], ...
|
|
||||||
"training_length",len_tr,"training_loops",5,"dd_loops",5, ...
|
|
||||||
"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005, ...
|
|
||||||
"FFEmu",0,"plotfinal",0,"ideal_dfe",0);
|
|
||||||
|
|
||||||
% eq_ = FFE("epochs_tr",4,"epochs_dd",5,"len_tr",4096,"mu_dd",0.01,"mu_tr",0.01,"order",50,"sps",2,"decide",0, "adaption",adaption_method.nlms,"dd_mode",1);
|
|
||||||
eq_ = FFE_DFE("epochs_tr",5,"epochs_dd",5,"len_tr",512,"ffe_mu_dd",1e-4,"dfe_mu_dd",5e-4,"ffe_mu_tr",0,"dfe_mu_tr",0,"ffe_order",99,"dfe_order",99,"sps",2,"decide",0);
|
|
||||||
|
|
||||||
|
|
||||||
output.ffe_results = ffe(eq_,M,Scpe_sig,Symbols,Tx_bits, ...
|
output.ffe_results = ffe(eq_,M,Scpe_sig,Symbols,Tx_bits, ...
|
||||||
"precode_mode",duob_mode,'showAnalysis',1,"postFFE",[], ...
|
"precode_mode",duob_mode,'showAnalysis',1,"postFFE",[], ...
|
||||||
"eth_style_symbol_mapping",0);
|
"eth_style_symbol_mapping",0);
|
||||||
|
|
||||||
output.ffe_results.metrics.print("description",'DFE');
|
|
||||||
|
|
||||||
|
output.ffe_results.metrics.print("description",'DFE');
|
||||||
|
end
|
||||||
%%
|
%%
|
||||||
|
|
||||||
% -------------------- VNLE + MLSE --------------------
|
% -------------------- VNLE + MLSE --------------------
|
||||||
|
|||||||
@@ -1,5 +1,5 @@
|
|||||||
|
|
||||||
M = 6;
|
M = 4;
|
||||||
if M == 2
|
if M == 2
|
||||||
file_codes = {'20250221T035221' '20250221T035354' '20250221T035824' '20250221T035931' '20250221T040035' '20250221T040132' '20250221T040226' '20250221T040523' '20250221T040646' '20250221T040723' '20250221T040843' '20250221T041011' '20250221T041101' '20250221T041244'};
|
file_codes = {'20250221T035221' '20250221T035354' '20250221T035824' '20250221T035931' '20250221T040035' '20250221T040132' '20250221T040226' '20250221T040523' '20250221T040646' '20250221T040723' '20250221T040843' '20250221T041011' '20250221T041101' '20250221T041244'};
|
||||||
elseif M == 4
|
elseif M == 4
|
||||||
@@ -10,7 +10,7 @@ elseif M ==8
|
|||||||
file_codes = {'20250221T004926' '20250221T023534' '20250221T024256' '20250221T024629' '20250221T024929' '20250221T025305' '20250221T025505' '20250221T025856' '20250221T030122' '20250221T030311' '20250221T030513'};
|
file_codes = {'20250221T004926' '20250221T023534' '20250221T024256' '20250221T024629' '20250221T024929' '20250221T025305' '20250221T025505' '20250221T025856' '20250221T030122' '20250221T030311' '20250221T030513'};
|
||||||
end
|
end
|
||||||
|
|
||||||
if 0
|
if 1
|
||||||
uloops = struct;
|
uloops = struct;
|
||||||
uloops.filecode = file_codes;
|
uloops.filecode = file_codes;
|
||||||
uloops.vnle_order1 = [50];
|
uloops.vnle_order1 = [50];
|
||||||
@@ -19,7 +19,7 @@ if 0
|
|||||||
wh = DataStorage(uloops);
|
wh = DataStorage(uloops);
|
||||||
wh.addStorage("ber");
|
wh.addStorage("ber");
|
||||||
|
|
||||||
wh = submit_handle(@dsp_ief_file,wh,"parallel",1);
|
wh = submit_handle(@dsp_ief_file,wh,"parallel",0);
|
||||||
end
|
end
|
||||||
|
|
||||||
if 0
|
if 0
|
||||||
|
|||||||
@@ -36,9 +36,11 @@ function [results] = dsp_ief_file(varargin)
|
|||||||
end
|
end
|
||||||
|
|
||||||
pathToMeasurement = "C:\Users\Silas\Documents\MATLAB\Datensätze\IEF_Polariton_2025\36_IMDD_Kiel\Data\20250221";
|
pathToMeasurement = "C:\Users\Silas\Documents\MATLAB\Datensätze\IEF_Polariton_2025\36_IMDD_Kiel\Data\20250221";
|
||||||
|
pathToMeasurement = "W:\labdata\Plasmonic O-band Modulator (ETH Zurich)\Data\20250221";
|
||||||
filename = findFileByCode(pathToMeasurement, filecode{1});
|
filename = findFileByCode(pathToMeasurement, filecode{1});
|
||||||
|
|
||||||
pathToTimingRecov = "C:\Users\Silas\Documents\MATLAB\Datensätze\IEF_Polariton_2025\36_IMDD_Kiel\Data\TR_ZIP";
|
pathToTimingRecov = "C:\Users\Silas\Documents\MATLAB\Datensätze\IEF_Polariton_2025\36_IMDD_Kiel\Data\TR_ZIP";
|
||||||
|
pathToTimingRecov = "W:\labdata\Plasmonic O-band Modulator (ETH Zurich)\Data\TR_ZIP";
|
||||||
filename_2 = findFileByCode(pathToTimingRecov, filecode{1});
|
filename_2 = findFileByCode(pathToTimingRecov, filecode{1});
|
||||||
|
|
||||||
% Extract parameters from the filename using an updated regex
|
% Extract parameters from the filename using an updated regex
|
||||||
@@ -166,7 +168,7 @@ function [results] = dsp_ief_file(varargin)
|
|||||||
%%%%% VNLE + PF + MLSE %%%%
|
%%%%% VNLE + PF + MLSE %%%%
|
||||||
if 1
|
if 1
|
||||||
|
|
||||||
eq_post = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",1e-4,"mu_tr",0,"order",1001,"sps",1,"decide",0);
|
eq_post = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",1e-4,"mu_tr",1.933e-04,"order",1001,"sps",1,"decide",0,"optmize_mus",1,"dd_mode",0);
|
||||||
pf_ = Postfilter("ncoeff",1,"useBurg",1);
|
pf_ = Postfilter("ncoeff",1,"useBurg",1);
|
||||||
mlse_ = MLSE("duobinary_output",0,'M',config.M,'trellis_states',PAMmapper(config.M,0).levels);
|
mlse_ = MLSE("duobinary_output",0,'M',config.M,'trellis_states',PAMmapper(config.M,0).levels);
|
||||||
mlse_ = MLSE_viterbi("duobinary_output",0,'M',config.M,'trellis_states',PAMmapper(config.M,0).levels);
|
mlse_ = MLSE_viterbi("duobinary_output",0,'M',config.M,'trellis_states',PAMmapper(config.M,0).levels);
|
||||||
|
|||||||
Reference in New Issue
Block a user