Updated Plotting Graphs

models/modulation_schemes.py

@@ -92,7 +92,7 @@ class OpticalChannel(layers.Layer):
                  dispersion_factor,
                  fiber_length,
                  lpf_cutoff=32e9,
-                 rx_stddev=0.01,
+                 rx_stddev=0.03,
                  q_stddev=0.01):
         """
         A channel model that simulates chromatic dispersion, non-linear photodiode detection, finite bandwidth of
@@ -298,13 +298,13 @@ class ModulationModel(tf.keras.Model):
         pass
 
     def plot(self, inputs, signal):
-        t = np.arange(self.messages_per_block * self.samples_per_symbol*3)
+        t = np.arange(self.messages_per_block * self.samples_per_symbol*1)
         frequency = SAMPLING_FREQUENCY*1e-9
         t = np.divide(t, frequency)
         plt.figure()
-        plt.plot(t, signal.flatten()[:self.messages_per_block * self.samples_per_symbol*3], label='Received Signal')
+        plt.plot(t, signal.flatten()[:self.messages_per_block * self.samples_per_symbol*1], label='Received Signal')
         inputs = np.square(inputs)
-        plt.plot(t, inputs.flatten()[:self.messages_per_block * self.samples_per_symbol*3], label='Transmitted Signal')
+        plt.plot(t, inputs.flatten()[:self.messages_per_block * self.samples_per_symbol*1], label='Transmitted Signal')
         plt.xlabel('Time (ns)')
         plt.ylabel('Power')
         plt.title('Effect of Chromatic Dispersion and Noise on Generated Signal')
@@ -314,7 +314,7 @@ class ModulationModel(tf.keras.Model):
     def demodulate(self, validation, outputs):
         symbol_rate = SAMPLING_FREQUENCY/SAMPLES_PER_SYMBOL
 
-        b, a = bessel(5, 10*(symbol_rate)/(SAMPLING_FREQUENCY/2), btype='low', analog=False)
+        b, a = bessel(5, 8*(symbol_rate)/(SAMPLING_FREQUENCY/2), btype='low', analog=False)
         outputsfilt = filtfilt(b, a, outputs)
         demodulate = np.sqrt(outputsfilt)
         #modulation_scheme.plot(inputs, demodulate)
@@ -350,14 +350,17 @@ class ModulationModel(tf.keras.Model):
 def plot_output_graphs():
     inputs, validation = modulation_scheme.generate_random_inputs(num_of_blocks=size)
     outputs = optical_channel(inputs).numpy()
-    b, a = bessel(5, 10 * (SAMPLING_FREQUENCY/SAMPLES_PER_SYMBOL) / (SAMPLING_FREQUENCY / 2), btype='low', analog=False)
+    b, a = bessel(3, 8 * (SAMPLING_FREQUENCY/SAMPLES_PER_SYMBOL) / (SAMPLING_FREQUENCY / 2), btype='low', analog=False)
     outputsfilt = filtfilt(b, a, outputs)
-    modulation_scheme.plot(inputs, outputs)
-    modulation_scheme.plot(inputs, outputsfilt)
+    input = np.sqrt(inputs)
+    noisy = np.sqrt(outputs)
+    demodulate = np.sqrt(outputsfilt)
+    modulation_scheme.plot(input, noisy)
+    modulation_scheme.plot(input, demodulate)
 
     #ber.append(modulation_scheme.demodulate(validation, outputs))
 def plot_fibre_length_vs_ber():
-    lengths = np.linspace(5, 50, 200)
+    lengths = np.linspace(5, 70, 1000)
     ber =[]
     for length in lengths:
         optical_channel = OpticalChannel(fs=SAMPLING_FREQUENCY,
@@ -387,10 +390,10 @@ def plot_fibre_length_vs_ber():
 if __name__ == '__main__':
     SAMPLING_FREQUENCY = 336e9
     CARDINALITY = 4
-    SAMPLES_PER_SYMBOL = 64
+    SAMPLES_PER_SYMBOL = 128
     MESSAGES_PER_BLOCK = 9
     DISPERSION_FACTOR = -21.7 * 1e-24
-    FIBER_LENGTH = 30
+    FIBER_LENGTH = 50
 
     optical_channel = OpticalChannel(fs=SAMPLING_FREQUENCY,
                                      num_of_samples=MESSAGES_PER_BLOCK * SAMPLES_PER_SYMBOL,
@@ -406,6 +409,7 @@ if __name__ == '__main__':
     #inputs, validation = modulation_scheme.generate_random_inputs(num_of_blocks=size)
     #outputs = optical_channel(inputs).numpy()
     #decisions = modulation_scheme.demodulate(validation, outputs)
+    #plot_output_graphs()
     plot_fibre_length_vs_ber()
     #modulation_scheme.plot(inputs, outputs, size)
     print("done")