Auswertung Experiment

Database tüddelei
DBHanlder läuft gut für DIESE Datanbank struktur...
App begonnen aber weit entfernt von gutem Stand

Classes/04_DSP/Equalizer/EQ.m

@@ -1,4 +1,4 @@
-classdef EQ %< handle
+classdef EQ < handle
     %EQ Summary of this class goes here
     %   Detailed explanation goes here
 
@@ -289,9 +289,9 @@ classdef EQ %< handle
 
                         e_dc = e_dc - obj.DCmu*error;
 
-                        obj.error_log.e_ffe(cnt,trainloops) = e_ffe;
-                        obj.error_log.e_dfe(cnt,trainloops) = e_dfe;
-                        obj.error_log.e_(cnt,trainloops) = error;
+                        % obj.error_log.e_ffe(cnt,trainloops) = e_ffe;
+                        % obj.error_log.e_dfe(cnt,trainloops) = e_dfe;
+                        % obj.error_log.e_(cnt,trainloops) = error;
 
                         cnt = cnt+1;
                         if obj.Nb(1) > 0
@@ -460,7 +460,7 @@ classdef EQ %< handle
 
                         if 1%mu_mat ~= 0
                              e_dc = e_dc - obj.DCmu*error;
-                             error_log(cnt,dd_loop) = e_dc;
+                             % error_log(cnt,dd_loop) = e_dc;
                              cnt = cnt+1;
                         end
 

Classes/04_DSP/Equalizer/Postfilter.m

@@ -0,0 +1,60 @@
+classdef Postfilter < handle
+    %NAME Summary of this class goes here
+    %   Detailed explanation goes here
+
+    properties(Access=public)
+        ncoeff = 2;
+        burg_coeff = [];
+
+    end
+
+    methods (Access=public)
+        function obj = Postfilter(options)
+            %NAME Construct an instance of this class
+            %   Detailed explanation goes here
+
+            arguments
+                options.ncoeff double = 2
+            end
+
+            % 
+            fn = fieldnames(options);
+            for n = 1:numel(fn)
+                try
+                    obj.(fn{n}) = options.(fn{n});
+                end
+            end
+
+            % do more stuff 
+
+        end
+
+        function signalclass_out = process(obj,signalclass_in,noiseclass_in)
+
+            obj.burg_coeff = arburg(noiseclass_in.signal,obj.ncoeff);
+            signalclass_in = signalclass_in.filter(obj.burg_coeff,1);
+
+            % append to logbook
+            lbdesc = ['Postfilter'];
+            signalclass_in  = signalclass_in.logbookentry(lbdesc);
+
+            % write to output
+            signalclass_out = signalclass_in;
+
+        end
+
+        function showFilter(obj,noiseclass_in)
+
+            noiseclass_in.spectrum('displayname','Noise PSD shifted to 0dBm','fignum',123,'normalizeTo0dB',1);
+
+            [h,w] = freqz(1,obj.burg_coeff,length(noiseclass_in),"whole",noiseclass_in.fs);
+            h = h/max(abs(h));
+            hold on
+            w_ = (w - noiseclass_in.fs/2);
+            plot(w_.*1e-9,20*log10(fftshift(h)),'DisplayName',['Burg Coeffs: ', num2str(obj.burg_coeff), ' ']);
+
+        end
+
+    end
+
+end

Classes/04_DSP/Sequence Detection/MLSE.m

@@ -1,4 +1,4 @@
-classdef MLSE
+classdef MLSE < handle
     %MLSE calculates the most probable sequence for an input signal with given/ known channel impulse response of any length
 
     properties(Access=public)