start with 400G analysis work
This commit is contained in:
Silas Oettinghaus
@@ -30,6 +30,9 @@ classdef VNLE < handle
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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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mu_optimization_len
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plot_mu_optimization = 0
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mu_optimization_fignum = 3020;
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x_norm
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ce
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@@ -55,6 +58,9 @@ classdef VNLE < handle
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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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options.mu_optimization_len = 2^15;
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options.plot_mu_optimization = 0;
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options.mu_optimization_fignum = 3020;
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end
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@@ -110,7 +116,6 @@ classdef VNLE < handle
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lbdesc = [num2str(obj.order),' tap FFE'];
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X = X.logbookentry(lbdesc); % append to logbook
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N = X;
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N = X - D;
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@@ -138,13 +143,18 @@ classdef VNLE < handle
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ones(1,obj.ce(3))*mu(3) ]);
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end
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x = x(:);
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d = d(:);
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x = [zeros(floor(obj.order(1)/2),1); x; zeros(obj.order(1),1)];
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n_symbols = floor(N / obj.sps);
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y = zeros(n_symbols,1);
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d_hat = zeros(n_symbols,1);
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if showviz
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f = figure(111);
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figure(111);
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subplot(2,2,1:2);
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hold on
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a = scatter(1:numel(x),x,1,'.');
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scatter(1:numel(x),x,1,'.');
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a2 = scatter(1,1,1,'.');
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a3 = scatter(1,1,2,'.');
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a4 = xline(1);
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@@ -215,40 +225,50 @@ classdef VNLE < handle
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mu_range = [1e-5, 1e-2];
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mu_dc_range = [1e-5, 1e-1];
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vars = [optimizableVariable("mu_tr",mu_range,"Transform","log"), ...
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optimizableVariable("mu_dd",mu_range,"Transform","log")];
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[x_opt,d_opt,N_opt] = obj.optimizationSignals(x,d);
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vars = obj.muOptimizableVariables("mu_tr",mu_range);
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vars = [vars, obj.muOptimizableVariables("mu_dd",mu_range)];
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optimize_mu_dc = obj.mu_dc ~= 0;
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if optimize_mu_dc
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vars = [vars, optimizableVariable("mu_dc",mu_dc_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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fprintf("VNLE mu opt uses %d samples / %d symbols\n",N_opt,numel(d_opt));
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obj.mu_optimization = bayesopt(@(p)obj.muObjective(p,x_opt,d_opt),vars, ...
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"MaxObjectiveEvaluations",20, ...
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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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obj.mu_dd = obj.mu_optimization.XAtMinObjective.mu_dd;
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obj.mu_tr = obj.muVectorFromParams(obj.mu_optimization.XAtMinObjective,"mu_tr");
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obj.mu_dd = obj.muVectorFromParams(obj.mu_optimization.XAtMinObjective,"mu_dd");
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if optimize_mu_dc
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obj.mu_dc = obj.mu_optimization.XAtMinObjective.mu_dc;
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end
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objective_db = 10*log10(obj.mu_optimization.MinObjective);
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if optimize_mu_dc
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fprintf("\nVNLE mu opt done: mu_tr=%9.3e, mu_dd=%9.3e, mu_dc=%9.3e, MSE=%9.3e, MSE_dB=%7.2f dB\n", ...
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obj.mu_tr,obj.mu_dd,obj.mu_dc,obj.mu_optimization.MinObjective,objective_db);
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fprintf("\nVNLE mu opt done: mu_tr=[%s], mu_dd=[%s], mu_dc=%9.3e, BER=%9.3e\n", ...
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obj.formatMuVector(obj.mu_tr),obj.formatMuVector(obj.mu_dd), ...
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obj.mu_dc,obj.mu_optimization.MinObjective);
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else
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fprintf("\nVNLE mu opt done: mu_tr=%9.3e, mu_dd=%9.3e, MSE=%9.3e, MSE_dB=%7.2f dB\n", ...
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obj.mu_tr,obj.mu_dd,obj.mu_optimization.MinObjective,objective_db);
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fprintf("\nVNLE mu opt done: mu_tr=[%s], mu_dd=[%s], BER=%9.3e\n", ...
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obj.formatMuVector(obj.mu_tr),obj.formatMuVector(obj.mu_dd), ...
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obj.mu_optimization.MinObjective);
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end
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if obj.plot_mu_optimization
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obj.plotMuOptimization();
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end
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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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old_mu_dc = obj.mu_dc;
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obj.save_debug = 1;
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old_state = obj.captureObjectiveState();
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cleanup = onCleanup(@()obj.restoreObjectiveState(old_state));
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obj.save_debug = 0;
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optimize_mu_dc = ismember("mu_dc",string(params.Properties.VariableNames));
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if optimize_mu_dc
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obj.mu_dc = params.mu_dc;
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@@ -257,34 +277,217 @@ classdef VNLE < handle
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obj.e = zeros(sum(obj.ce),1);
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obj.e_dc = 0;
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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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obj.equalize(x,d,params.mu_dd,obj.epochs_dd,numel(x),0,0);
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muTrCandidate = obj.muVectorFromParams(params,"mu_tr");
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muDdCandidate = obj.muVectorFromParams(params,"mu_dd");
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N_tr = min(obj.len_tr,numel(x));
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obj.equalize(x,d,muTrCandidate,obj.epochs_tr,N_tr,1,0);
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[signal,~] = obj.equalize(x,d,muDdCandidate,obj.epochs_dd,numel(x),0,0);
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objective = mean(obj.debug_struct.error(end,:),"omitnan");
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[ber,errors] = obj.berObjective(signal,d);
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objective = ber;
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if ~isfinite(objective)
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objective = inf;
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end
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objective_db = 10*log10(objective);
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obj.mu_optimization_iter = obj.mu_optimization_iter + 1;
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if optimize_mu_dc
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fprintf("\rVNLE mu opt %02d: mu_tr=%9.3e, mu_dd=%9.3e, mu_dc=%9.3e, MSE=%9.3e, MSE_dB=%7.2f dB", ...
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obj.mu_optimization_iter,params.mu_tr,params.mu_dd,params.mu_dc,objective,objective_db);
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fprintf("\rVNLE mu opt %02d: mu_tr=[%s], mu_dd=[%s], mu_dc=%9.3e, BER=%9.3e, errors=%d", ...
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obj.mu_optimization_iter,obj.formatMuVector(muTrCandidate), ...
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obj.formatMuVector(muDdCandidate),params.mu_dc,ber,errors);
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else
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fprintf("\rVNLE mu opt %02d: mu_tr=%9.3e, mu_dd=%9.3e, MSE=%9.3e, MSE_dB=%7.2f dB", ...
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obj.mu_optimization_iter,params.mu_tr,params.mu_dd,objective,objective_db);
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fprintf("\rVNLE mu opt %02d: mu_tr=[%s], mu_dd=[%s], BER=%9.3e, errors=%d", ...
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obj.mu_optimization_iter,obj.formatMuVector(muTrCandidate), ...
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obj.formatMuVector(muDdCandidate),ber,errors);
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end
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obj.save_debug = old_debug;
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obj.mu_dc = old_mu_dc;
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clear cleanup
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end
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function [x_opt,d_opt,N_opt] = optimizationSignals(obj,x,d,opt_len)
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if nargin < 4
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opt_len = obj.mu_optimization_len;
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end
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N_available = min(numel(x),numel(d) * obj.sps);
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if isempty(opt_len) || opt_len <= 0 || isinf(opt_len)
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N_opt = N_available;
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else
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N_opt = min(N_available,max(obj.len_tr,opt_len));
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end
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N_opt = obj.sps * floor(N_opt / obj.sps);
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N_opt = max(obj.sps,N_opt);
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n_symbols = N_opt / obj.sps;
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x_opt = x(1:N_opt);
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d_opt = d(1:n_symbols);
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end
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function [ber,errors] = berObjective(~,signal,d)
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M = numel(unique(d));
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mapper = PAMmapper(M,0);
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eq_signal_sd = Signal(signal);
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eq_signal_hd = mapper.quantize(eq_signal_sd);
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tx_symbols = Signal(d);
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rx_bits = mapper.demap(eq_signal_hd);
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tx_bits = mapper.demap(tx_symbols);
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skip_front = min(1000,max(0,floor(numel(rx_bits.signal) / 4)));
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[~,errors,ber,~] = calc_ber(rx_bits.signal,tx_bits.signal, ...
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"skip_front",skip_front, ...
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"skip_end",0, ...
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"returnErrorLocation",1);
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end
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function vars = muOptimizableVariables(obj,prefix,mu_range)
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vars = optimizableVariable.empty;
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activeOrders = find(obj.order > 0);
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for idx = 1:numel(activeOrders)
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orderIdx = activeOrders(idx);
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varName = sprintf("%s_%d",prefix,orderIdx);
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vars = [vars, optimizableVariable(varName,mu_range,"Transform","log")]; %#ok<AGROW>
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end
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end
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function mu = muVectorFromParams(obj,params,prefix)
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mu = zeros(1,3);
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for orderIdx = 1:numel(mu)
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varName = sprintf("%s_%d",prefix,orderIdx);
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if ismember(varName,string(params.Properties.VariableNames))
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mu(orderIdx) = params.(varName);
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elseif numel(obj.(char(prefix))) >= orderIdx
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mu(orderIdx) = obj.(char(prefix))(orderIdx);
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else
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mu(orderIdx) = obj.(char(prefix))(1);
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end
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end
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end
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function plotMuOptimization(obj)
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if isempty(obj.mu_optimization)
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return
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end
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X = obj.mu_optimization.XTrace;
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objective = obj.mu_optimization.ObjectiveTrace;
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objective = objective(:);
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valid = isfinite(objective);
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if isempty(X) || ~any(valid)
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return
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end
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var_names = X.Properties.VariableNames;
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n_vars = numel(var_names);
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eval_idx = (1:numel(objective)).';
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objective_plot = obj.positiveObjectiveForLogPlot(objective);
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best_plot = obj.positiveObjectiveForLogPlot(cummin(objective));
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figure(obj.mu_optimization_fignum);
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clf;
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t = tiledlayout(2,2,"TileSpacing","compact","Padding","compact");
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title(t,"VNLE Bayesian mu optimization");
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nexttile;
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h_candidate = semilogy(eval_idx,objective_plot,"o-","DisplayName","candidate");
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obj.addOptimizationDataTips(h_candidate,X,objective,objective_plot,eval_idx,var_names);
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hold on;
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h_best_trace = semilogy(eval_idx,best_plot,"k-","LineWidth",1.2,"DisplayName","best so far");
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h_best_trace.DataTipTemplate.DataTipRows(end+1) = dataTipTextRow("Evaluation",eval_idx);
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h_best_trace.DataTipTemplate.DataTipRows(end+1) = dataTipTextRow("Best BER",best_plot);
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grid on;
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xlabel("Evaluation");
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ylabel("BER");
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legend("Location","best");
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if n_vars < 2
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return
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end
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pairs = nchoosek(1:n_vars,2);
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n_pair_plots = min(size(pairs,1),3);
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[~,best_idx] = min(objective);
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for pair_idx = 1:n_pair_plots
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nexttile;
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x_name = var_names{pairs(pair_idx,1)};
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y_name = var_names{pairs(pair_idx,2)};
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x_data = X.(x_name);
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y_data = X.(y_name);
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c_data = log10(objective_plot);
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h_scatter = scatter(log10(x_data),log10(y_data),35,c_data,"filled");
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obj.addOptimizationDataTips(h_scatter,X,objective,objective_plot,eval_idx,var_names);
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hold on;
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h_best = plot(log10(x_data(best_idx)),log10(y_data(best_idx)),"kp", ...
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"MarkerSize",12, ...
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"MarkerFaceColor","y", ...
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"DisplayName","best");
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obj.addOptimizationDataTips(h_best,X(best_idx,:),objective(best_idx),objective_plot(best_idx),eval_idx(best_idx),var_names);
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grid on;
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xlabel("log10(" + string(x_name) + ")");
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ylabel("log10(" + string(y_name) + ")");
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cb = colorbar;
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cb.Label.String = "log10(BER)";
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title(string(x_name) + " vs " + string(y_name));
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end
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end
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function addOptimizationDataTips(~,plot_handle,X,objective,objective_plot,eval_idx,var_names)
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plot_handle.DataTipTemplate.DataTipRows(end+1) = dataTipTextRow("Evaluation",eval_idx);
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plot_handle.DataTipTemplate.DataTipRows(end+1) = dataTipTextRow("BER",objective);
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plot_handle.DataTipTemplate.DataTipRows(end+1) = dataTipTextRow("BER shown",objective_plot);
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for var_idx = 1:numel(var_names)
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var_name = var_names{var_idx};
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plot_handle.DataTipTemplate.DataTipRows(end+1) = dataTipTextRow(var_name,X.(var_name));
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end
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end
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function objective_plot = positiveObjectiveForLogPlot(~,objective)
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objective_plot = objective;
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positive_values = objective(isfinite(objective) & objective > 0);
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if isempty(positive_values)
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floor_value = 1e-12;
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else
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floor_value = min(positive_values) / 10;
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end
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objective_plot(~isfinite(objective_plot) | objective_plot <= 0) = floor_value;
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end
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function state = captureObjectiveState(obj)
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state.e = obj.e;
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state.e_dc = obj.e_dc;
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state.error = obj.error;
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state.mu_dc = obj.mu_dc;
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state.save_debug = obj.save_debug;
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state.debug_struct = obj.debug_struct;
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end
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function restoreObjectiveState(obj,state)
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obj.e = state.e;
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obj.e_dc = state.e_dc;
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obj.error = state.error;
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obj.mu_dc = state.mu_dc;
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obj.save_debug = state.save_debug;
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obj.debug_struct = state.debug_struct;
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end
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function s = formatMuVector(~,mu)
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s = strtrim(sprintf("%9.3e ",mu));
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end
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%% Functions needed During Adaption
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function x_in_vnle_format = calcVNLENonlinVecs(~,x_in_block,I_2,I_3,N_,norm_)
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% These are the second and third order input signal products of the VNLE EQ
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% ∑ h1 x_in(k-n1) + ∑∑ h2 x_in(k-n1)*x_in(k-n2) + ∑∑∑ h3 x_in(k-n1)*x_in(k-n2)*x_in(k-n3)
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x_in_block = x_in_block(:);
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l1=length(x_in_block);
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l2=length(I_2);
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l3=length(I_3);
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l2=size(I_2,1);
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l3=size(I_3,1);
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final_length = l1+l2+l3;
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x_in_vnle_format = zeros(final_length,1);
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@@ -382,6 +585,7 @@ classdef VNLE < handle
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end
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function powerNorm = calcPowerNormalization(~,v)
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v = v(:);
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powerNorm(1) = sqrt(mean(abs(v ).^2));
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powerNorm(2) = sqrt(mean(abs(v.^2).^2));
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