clean chaine telecom

telecom/hands_on_measurements/gr-fsk/apps/eval_limesdr.grc

@@ -72,7 +72,7 @@ blocks:
   id: variable
   parameters:
     comment: ''
-    value: '0.1'
+    value: '0.001'
   states:
     bus_sink: false
     bus_source: false

telecom/hands_on_measurements/gr-fsk/python/demodulation.py

@@ -20,8 +20,9 @@
 
 import numpy as np
 from gnuradio import gr
+from .utils import timeit
 
-
+@timeit
 def demodulate(y, B, R, Fdev):
         """
         Non-coherent demodulator.

telecom/hands_on_measurements/gr-fsk/python/synchronization.py

@@ -22,8 +22,9 @@ import numpy as np
 from scipy import signal as signal
 import matplotlib.pyplot as plt
 from gnuradio import gr
+from .utils import timeit
 
-
+@timeit
 def cfo_estimation(y, B, R, Fdev):
         """
         Estimates CFO using Moose algorithm, on first samples of preamble.
@@ -64,28 +65,26 @@ def old_sto_estimation(y, B, R, Fdev):
 
     return np.mod(save_i + 1, R)
 
-
+@timeit
 def sto_estimation(y,B,R,Fdev):
     """
     New method
     """
 
     phase_function = np.unwrap(np.angle(y))
-    filtered_phase = np.convolve(phase_function,signal.triang(3),mode="same" )
+    #filtered_phase = np.convolve(phase_function,signal.triang(3),mode="same" )
 
-    phase_derivative_1_filtered = filtered_phase[2:] - filtered_phase[:-2] #calcul de la dérivée première au 1er ordre (méthode centrée -> pas valide pour premier et dernier sample)
+    phase_derivative_1_filtered = phase_function[2:] - phase_function[:-2] #calcul de la dérivée première au 1er ordre (méthode centrée -> pas valide pour premier et dernier sample)
 
-    sum_der_saved = np.inf
-    save_i = 0
+  
+    sum_array = np.zeros(R)
     for i in range(0, R):
         sum_der = np.sum(np.abs(phase_derivative_1_filtered[i::R]))  # Sum every R samples. We take abs b.c. maxima don't exactly have nul derivative 
-        if sum_der < sum_der_saved:
-            sum_der_saved = sum_der
-            save_i = i
-    
-    new_estimate = np.mod(save_i + 1 , R)
+        sum_array[i] = sum_der
+    new_estimate = np.mod(np.argmin(sum_array) + 1 , R)
 
-    old_estimate = old_sto_estimation(y,B,R,Fdev) #to disable to gain in performance. Used only to compare to the old sto comuptation
+    
+    #old_estimate = old_sto_estimation(y,B,R,Fdev) #to disable to gain in performance. Used only to compare to the old sto comuptation
 
     #plt.subplot(2,1,1)
     #plt.stem(phase_function)
@@ -96,7 +95,7 @@ def sto_estimation(y,B,R,Fdev):
     #plt.stem(filtered_phase[1:]) 
     #plt.title(f"filtered phase new estimate = {new_estimate}, old estimate = {old_estimate}")
     #plt.show()
-    print(f"new sto estimate = {new_estimate}, old sto estimate = {old_estimate}")
+    #print(f"new sto estimate = {new_estimate}") #, old sto estimate = {old_estimate}
     return new_estimate