def bit_error_rate_NN(N, k, C, Nb_sequences, e0, e1, channel='BSC'):
print(
'*******************NN-Decoder********************************************'
)
# model_decoder = keras.models.load_model("autoencoder/model_decoder_bsc_16_8_array.h5")
# model_decoder = keras.models.load_model("./model/model_decoder_16_4_std.h5")
print("Decoder Loaded from disk, ready to be used")
U_k = utils.symbols_generator(k) # all possible messages
ber = {}
bler = {}
count = 0
Nb_iter_max = 10
Nb_words = int(Nb_sequences / Nb_iter_max)
for ep0 in e0:
ber_row = []
bler_row = []
for ep1 in (ep1 for ep1 in e1 if ep1 + ep0 <= 1 and ep1 <= ep0):
if ep1 == ep0 or ep1 == e0[0]:
N_errors = 0
N_errors_bler = 0
N_iter = 0
while N_iter < Nb_iter_max: # and N_errors < N_errors_mini:
N_iter += 1
idx = np.random.randint(0,
len(U_k) - 1,
size=(1, Nb_words)).tolist()[0]
u = [U_k[a] for a in idx]
x = [C[a] for a in idx] # coded bits
y_bac = [utils.BAC_channel(xi, ep0, ep1)
for xi in x] # received symbols
yh = np.reshape(y_bac, [Nb_words, N]).astype(np.float64)
u_nn = [
U_k[idy] for idy in np.argmax(model_dec.predict(yh), 1)
] # NN Detector
for i in range(len(u)):
N_errors += np.sum(
np.abs(np.array(u[i]) - np.array(u_nn[i]))
) # bit error rate compute with NN
N_errors_bler += np.sum(1.0 * (u[i] != u_nn[i]))
ber_row.append(
N_errors /
(k * 1.0 * Nb_sequences)) # bit error rate compute with NN
bler_row.append(
N_errors_bler /
(1.0 * Nb_sequences)) # block error rate compute with NN
ber[ep0] = ber_row
bler[ep0] = bler_row
print("{:.2f}".format(ep0), '|', ["{:.4f}".format(a) for a in ber_row])
print("{:.2f}".format(ep0), '|',
["{:.4f}".format(a) for a in bler_row])
count += 1
print("{:.3f}".format(count / len(e0) * 100), '% completed ')
return ber, bler
def bit_error_rate_NN(N, k, C, N_iter_max, e0, e1, channel='BSC'):
print(
'******************* NN-Decoder ********************************************',
channel)
N_errors_mini = 100
U_k = utils.symbols_generator(k) # all possible messages
ber = {}
count = 0
for ep0 in e0:
ber_row = []
interval = np.zeros(4)
# interval[int(ep1*4)] = 1.0
interval[int(3 * np.log10(ep0) + 4) if ep0 >= 0.1 else 0] = 1.0
for ep1 in (ep1 for ep1 in e1 if ep1 + ep0 <= 1 and ep1 <= ep0):
if ep1 == ep0 or ep1 == e0[0]:
N_errors = 0
N_iter = 0
while N_iter < N_iter_max: # and N_errors < N_errors_mini:
N_iter += 1
idx = np.random.randint(0, len(U_k) - 1)
u = U_k[idx] # Bits to be sent
x = C[idx] # coded bits
y_bac = utils.BAC_channel(x, ep0, ep1) # received symbols
yh = np.reshape(
np.concatenate((y_bac, interval), axis=0), [1, N + 4]
) #if channel == 'BAC' else np.reshape(y_bac, [1, N]).astype(np.float64)
u_nn = U_k[np.argmax(model_decoder(yh))] # NN Detector
N_errors += utils.NbOfErrors(
u, u_nn) # bit error rate compute with NN
ber_tmp = N_errors / (k * 1.0 * N_iter
) # bit error rate compute with NN
ber_row.append(ber_tmp)
ber[ep0] = ber_row
print("{:.2f}".format(ep0), '|', ["{:.4f}".format(a) for a in ber_row])
count += 1
print("{:.3f}".format(count / len(e0) * 100), '% completed ')
return ber
def bit_error_rate_NN_decoder_irregular(N, k, C, nb_packets, e0, e1,
model_decoder, output):
""" computes the bit an block error rate using the NN-model decoder when external irregular interval
@param C: codebook
@param nb_packets: number of packets used in the computation
@param e0 and e1: linspaces containing all the values of epsilon_0 and epsilon_1 to be evaluated
@param model_decoder: the NN-model of the decoder, previously trained
@param output: type of output 'array' or 'one' (One-hot coding) it must agree with the type of the decoder model
@return: two dictionaries 'ber' and 'bler' containing metrics, as keys use the ep0
"""
print(
f'*******************NN-Decoder******************************************** {nb_packets} packets'
)
U_k = utils.symbols_generator(k) # all possible messages
ber = {}
bler = {}
count = 0
Nb_iter_max = 10
Nb_words = int(nb_packets / Nb_iter_max)
for ep0 in e0:
ber_row = []
bler_row = []
# interval = np.eye(4)[int(ep0*4/e_t-0.5) if ep0 < e_t else 3]
interval = np.eye(4)[int(9.30 *
ep0**0.5) if int(9.30 * ep0**0.5) < 4 else 3]
inter_list = np.array(np.tile(interval, (Nb_words, 1)))
for ep1 in (ep1 for ep1 in e1 if ep1 + ep0 <= 1 and ep1 <= ep0):
# if ep1 == ep0 or ep1 == e0[0]: #change to this if wants to compute for all epsilon
if ep1 == e0[0]: #just for the most asymmetric case
N_errors = 0
N_errors_bler = 0
N_iter = 0
while N_iter < Nb_iter_max: # and N_errors < N_errors_mini:
N_iter += 1
idx = np.random.randint(0,
len(U_k) - 1,
size=(1, Nb_words)).tolist()[0]
u = [U_k[a] for a in idx]
x = [C[a] for a in idx] # coded bits
# print('uk\n',u,'\nc\n',x)
y_bac = [utils.BAC_channel(xi, ep0, ep1)
for xi in x] # received symbols
yh = np.reshape(y_bac, [Nb_words, N]).astype(np.float64)
yh = np.concatenate((yh, inter_list), 1)
if output == 'one':
u_nn = [
U_k[idy]
for idy in np.argmax(model_decoder.predict(yh), 1)
] # NN Detector
elif output == 'array':
u_nn = [
idy for idy in np.round(
model_decoder.predict(yh + inter_list)).astype(
'int').tolist()
] # NN Detector
for i in range(len(u)):
N_errors += np.sum(
np.abs(np.array(u[i]) - np.array(u_nn[i]))
) # bit error rate compute with NN
N_errors_bler += np.sum(1.0 * (u[i] != u_nn[i]))
ber_row.append(
N_errors /
(k * 1.0 * nb_packets)) # bit error rate compute with NN
bler_row.append(
N_errors_bler /
(1.0 * nb_packets)) # block error rate compute with NN
ber[ep0] = ber_row
bler[ep0] = bler_row
# print("{:.2f}".format(ep0), '|', ["{:.4f}".format(a) for a in ber_row])
# print("{:.2f}".format(ep0), '|', ["{:.4f}".format(a) for a in bler_row])
count += 1
print("{:.3f}".format(count / len(e0) * 100), '% completed ')
return ber, bler
