diff --git a/projects/FSO_transmission/first_analysis.m b/projects/FSO_transmission/first_analysis.m
index d8361bc..9a327ab 100644
--- a/projects/FSO_transmission/first_analysis.m
+++ b/projects/FSO_transmission/first_analysis.m
@@ -83,8 +83,8 @@ Scope_sig = Electricalsignal(traceData.YData,"fs",fs);
 % pulse is symmetric, hence we can use pulsef directly 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!?
-apply_matched_filter = 0;
-k = 4;
+apply_matched_filter = 1;
+k = 1;
 if apply_matched_filter
     
     Pform = Pulseformer("fsym",fsym,"fdac",k*fsym,"pulse","rrc","pulselength",16,"alpha",rolloff,"matched",1);
@@ -96,6 +96,18 @@ else
 end
 Rx_matched.spectrum();
 
+
+%%
+
+coefficients = arburg(Rx_matched.signal,25);
+
+figure()
+[h,w] = freqz(1,coefficients,Rx_matched.length,"whole",Rx_matched.fs);
+h = h/max(abs(h));
+hold on
+w_ = (w - Rx_matched.fs/2);
+plot(w_.*1e-9,20*log10(fftshift(abs(h))),'DisplayName',['Burg Coeffs: ', num2str(round(coefficients,2)), ' '],'LineWidth',2);
+
 %% Timing Rec
 apply_timing_rec = 1;
 if apply_timing_rec
diff --git a/projects/ML_based_MLSE/analyze_filter_length.m b/projects/ML_based_MLSE/analyze_filter_length.m
index 616b715..bcf2a91 100644
--- a/projects/ML_based_MLSE/analyze_filter_length.m
+++ b/projects/ML_based_MLSE/analyze_filter_length.m
@@ -5,9 +5,9 @@ M = 4;
 randkey = 1;
 
 % --- Parameter sweep
-order_range = 2:3:11;        % FFE order
-delta_range = 0:2:4;        % delta
-SNR_dB = 20;
+order_range = 5:5:50;        % FFE order
+delta_range = 0:5:20;        % delta
+SNR_dB = 30;
 
 % --- Prepare bit sequence
 order_bits = 19;
@@ -21,10 +21,14 @@ Symbols = PAMmapper(M,0).map(Bits);
 Symbols.fs = 200e9;
 
 % --- Channel (minimal ISI + AWGN)
-h = [0.3 0.9 0.3]; h = h/norm(h);
-symbols_filt = Symbols.filter(h,1);
+h = abs([0.3 0.9 0.3]); h = h/norm(h);
+
+% h = [1	-1.67085330039878	1.17918163282514	-0.805210559745616	0.571564213123367	-0.296337147529674	0.00649773445209780	0.0854177610195952	-0.0576009020965258	0.0520994427061551	-0.0624586034913656	0.0553280962699552	-0.00705582559925755	-0.0336399056707792	0.0706903719452810	-0.0334124287931977	0.0131699455037966	0.0587431373842994	-0.0515902976066452	0.00647904355473619	0.0137506750904990	-0.0547974515885928	0.00994735499340592	-0.0135513582534086	-0.00463322575007739	0.0277311946101940];
+% h = h/norm(h);
+symbols_filt = Symbols.filter(1,h);
 symbols_noi = symbols_filt;
 symbols_noi.signal = awgn(symbols_filt.signal,SNR_dB,'measured');
+symbols_noi.spectrum;
 
 % --- Generate all parameter pairs
 [O,D] = ndgrid(order_range, delta_range);
@@ -51,7 +55,7 @@ parfor k = 1:size(pairs,1)
     try
         ml = ML_MLSE("epochs_tr",training_len,"epochs_dd",1,"len_tr",2^15, ...
             "mu_dd",0.1,"mu_tr",0.1,"order",order_k,"sps",1, ...
-            "traceback_depth",128,"L",3,"delta",delta_k,"adaptive_mu",0);
+            "traceback_depth",128,"L",2,"delta",delta_k,"adaptive_mu",0);
 
         [y_ml,y_ref] = ml.process(symbols_noi,Symbols);
         ref_bits = PAMmapper(M,0).demap(y_ref);
@@ -89,7 +93,7 @@ end
 beautifyBERplot
 ylabel('BER'); xlabel('Filter Order [N]');
 title('BER vs. Filter order');
-ylim([1e-4, 0.1]);
+% ylim([1e-4, 0.1]);
 yline(3.8e-3,'HandleVisibility','off');
 yline(2.2e-4,'HandleVisibility','off');