Added length vs ber graph generation

models/modulation_schemes.py

@@ -297,14 +297,14 @@ class ModulationModel(tf.keras.Model):
         plt.show()
         pass
 
-    def plot(self, inputs, signal, size):
-        t = np.arange(self.messages_per_block * self.samples_per_symbol)
+    def plot(self, inputs, signal):
+        t = np.arange(self.messages_per_block * self.samples_per_symbol*3)
         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], label='Received Signal')
+        plt.plot(t, signal.flatten()[:self.messages_per_block * self.samples_per_symbol*3], label='Received Signal')
         inputs = np.square(inputs)
-        plt.plot(t, inputs.flatten()[:self.messages_per_block * self.samples_per_symbol], label='Transmitted Signal')
+        plt.plot(t, inputs.flatten()[:self.messages_per_block * self.samples_per_symbol*3], label='Transmitted Signal')
         plt.xlabel('Time (ns)')
         plt.ylabel('Power')
         plt.title('Effect of Chromatic Dispersion and Noise on Generated Signal')
@@ -312,11 +312,13 @@ class ModulationModel(tf.keras.Model):
         plt.show()
 
     def demodulate(self, validation, outputs):
-        b, a = bessel(5, 2 / 45, btype='low', analog=False)
+        symbol_rate = SAMPLING_FREQUENCY/SAMPLES_PER_SYMBOL
+
+        b, a = bessel(5, 10*(symbol_rate)/(SAMPLING_FREQUENCY/2), btype='low', analog=False)
         outputsfilt = filtfilt(b, a, outputs)
         demodulate = np.sqrt(outputsfilt)
-        modulation_scheme.plot(inputs, demodulate, size)
-        average = np.mean(outputs.reshape(1000, -1, SAMPLES_PER_SYMBOL), axis=2).flatten()
+        #modulation_scheme.plot(inputs, demodulate)
+        average = np.mean(demodulate.reshape(1000, -1, SAMPLES_PER_SYMBOL), axis=2).flatten()
         validation = validation.flatten()
         decisions = []
         for symbol in average:
@@ -329,7 +331,15 @@ class ModulationModel(tf.keras.Model):
             else:
                 decisions.append(3)
         decisions = np.array(decisions)
-        return decisions
+        error = 0
+        index = 0
+        while index < len(validation):
+            if validation[index] != decisions[index]:
+                error += 1
+            index += 1
+
+        #print("ber = " + str(error/len(validation)))
+        return error/len(validation)
 
 
     def call(self, inputs, training=None, mask=None):
@@ -337,15 +347,50 @@ class ModulationModel(tf.keras.Model):
         rx = self.channel(tx)
         outputs = self.decoder(rx)
         return outputs
+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)
+    outputsfilt = filtfilt(b, a, outputs)
+    modulation_scheme.plot(inputs, outputs)
+    modulation_scheme.plot(inputs, outputsfilt)
+
+    #ber.append(modulation_scheme.demodulate(validation, outputs))
+def plot_fibre_length_vs_ber():
+    lengths = np.linspace(5, 50, 200)
+    ber =[]
+    for length in lengths:
+        optical_channel = OpticalChannel(fs=SAMPLING_FREQUENCY,
+                                         num_of_samples=MESSAGES_PER_BLOCK * SAMPLES_PER_SYMBOL,
+                                         dispersion_factor=DISPERSION_FACTOR,
+                                         fiber_length=length)
+
+        inputs, validation = modulation_scheme.generate_random_inputs(num_of_blocks=size)
+        outputs = optical_channel(inputs).numpy()
+        ber.append(modulation_scheme.demodulate(validation, outputs))
+
+    plt.figure()
+    plt.semilogy(lengths, ber, label='ber')
+    plt.xlabel('Fibre Length (km)')
+    plt.ylabel('BER')
+    plt.title('Effect of Fibre Length on BER of 4PAM System')
+    # Show the major grid lines with dark grey lines
+    plt.grid(b=True, which='major', color='#666666', linestyle='-')
+
+    # Show the minor grid lines with very faint and almost transparent grey lines
+    plt.minorticks_on()
+    plt.grid(b=True, which='minor', color='#999999', linestyle='-', alpha=0.2)
+    plt.legend()
+    plt.show()
 
 
 if __name__ == '__main__':
     SAMPLING_FREQUENCY = 336e9
     CARDINALITY = 4
-    SAMPLES_PER_SYMBOL = 128
+    SAMPLES_PER_SYMBOL = 64
     MESSAGES_PER_BLOCK = 9
     DISPERSION_FACTOR = -21.7 * 1e-24
-    FIBER_LENGTH = 50
+    FIBER_LENGTH = 30
 
     optical_channel = OpticalChannel(fs=SAMPLING_FREQUENCY,
                                      num_of_samples=MESSAGES_PER_BLOCK * SAMPLES_PER_SYMBOL,
@@ -358,9 +403,10 @@ if __name__ == '__main__':
                                         channel=optical_channel)
 
     size = 1000
-    inputs, validation = modulation_scheme.generate_random_inputs(num_of_blocks=size)
-    outputs = optical_channel(inputs).numpy()
-    decisions = modulation_scheme.demodulate(validation, outputs)
+    #inputs, validation = modulation_scheme.generate_random_inputs(num_of_blocks=size)
+    #outputs = optical_channel(inputs).numpy()
+    #decisions = modulation_scheme.demodulate(validation, outputs)
+    plot_fibre_length_vs_ber()
     #modulation_scheme.plot(inputs, outputs, size)
     print("done")