Merge remote-tracking branch 'origin/swarm_optimisation'

main.py

@@ -5,6 +5,7 @@ from sklearn.metrics import accuracy_score
 from models import basic
 from models.basic import AWGNChannel, BPSKDemod, BPSKMod, BypassChannel, AlphabetMod, AlphabetDemod
 import misc
+import math
 from models.autoencoder import Autoencoder, view_encoder
 
 
@@ -45,6 +46,16 @@ def get_AWGN_ber(mod, demod, samples=1000, start=-8, stop=5, steps=30):
     return ber_x, ber_y
 
 
+def get_SNR(mod, demod, samples=1000, start=-8, stop=5, steps=30):
+    ber_x, ber_y = get_AWGN_ber(mod, demod, samples, start, stop, steps)
+    x_mod = mod.forward(misc.generate_random_bit_array(samples*mod.N))
+    sig_amp = x_mod[:, 0]
+    sig_power = [A ** 2 for A in sig_amp]
+    av_sig_pow = np.mean(sig_power)
+    av_sig_pow = math.log(av_sig_pow, 10)
+    SNR = (ber_x * -1) + av_sig_pow
+    return SNR, ber_y
+
 if __name__ == '__main__':
     # show_constellation(BPSKMod(10e6), AWGNChannel(-1), BPSKDemod(10e6, 10e3))
 
@@ -111,18 +122,18 @@ if __name__ == '__main__':
     #          label='AE 4bit -8dB')
 
     for scheme in ['64qam', '32qam', '16qam', 'qpsk', '8psk']:
-        plt.plot(*get_AWGN_ber(
+        plt.plot(*get_SNR(
             AlphabetMod(scheme, 10e6),
             AlphabetDemod(scheme, 10e6),
-            samples=20e3,
+            samples=100e3,
             steps=40,
             start=-15
         ), '-', label=scheme.upper())
 
     plt.yscale('log')
-    plt.gca().invert_xaxis()
+    # plt.gca().invert_xaxis()
     plt.grid()
-    plt.xlabel('Noise dB')
+    plt.xlabel('SNR dB')
     plt.ylabel('BER')
     plt.legend()
     plt.show()