def polar_codes_mapping(metric, N=8, k=4, nb_pkts = 100, graph = 'BER',channel='BSC'):
print('-------------------Polar Codes + Mapping-----------------------------')
cont = 2
if channel == 'AWGN':
design_parameter = np.linspace(0.0, 10, cont)
else:
design_parameter = np.linspace(0.0001, 0.1, cont)
for key in [0.5]:
e_design = 0.1
# print('===============Design================',key)
G,infoBits = polar.polar_generator_matrix(64, k, channel, e_design)
k = len(G)
Nt = len(G[1])
t = int(Nt /N)
U_k = utils.symbols_generator(k) # all possible messages
X_m = utils.symbols_generator(t) # all possible symbol sequences
C = utils.matrix_codes(U_k, k, G, Nt)
nx = 2**t*key
# print('nx', nx, 't', t)
x = utils.mapping2(C, X_m, t, nx)
N = len(x[1])
if graph == 'BLER':
metric[f"P({e_design})+M({key})"] = utils.block_error_probability(N,k,x,e0,e1)
else:
metric[f"P({e_design})+M({key})"] = utils.bit_error_rate(k,x,nb_pkts,e0,e1)
def autoencoder_NN(metric, N=8, k=4, nb_pkts = 100, graph = 'BER',interval=False):
print('------------------- NN-autoencoder -----------------------------', interval)
key = 'NN_auto'
C = utils.NN_encoder(k,N)
# print(np.array(C))
print('k ', k, 'N ', N)
if graph == 'BLER':
metric[key] = utils.block_error_probability(N, k, C, e0, e1)
a,metric['BLER'] = utils.bit_error_rate_NN_predict(N, k, C, 10000, e0, e1, inter=interval)
else:
print("NN BER")
t = time.time()
metric[key],a = utils.bit_error_rate_NN_predict(N, k, C, nb_pkts, e0, e1, inter=interval)
t = time.time() - t
print(f"NN time = {t}s ========================")
print(metric[key])
MAP_test = False
if MAP_test:
print("MAP BER")
t = time.time()
metric['MAP_dec'],a = utils.bit_error_rate(k, C, nb_pkts, e0, e1)
t = time.time() - t
print(f"MAP time = {t}s =======================")
print(metric['MAP_dec'])
def integrated_scheme(metric,
N=8,
k=4,
nb_pkts=100,
graph='BER',
channel='BSC'):
print(
'-------------------Integrated Scheme Code-----------------------------'
)
for key in [0.5]:
G, infoBits = polar.polar_generator_matrix(64, k, channel, 0.1)
k = len(G)
Nt = len(G[1])
t = int(Nt / N)
U_k = utils.symbols_generator(k) # all possible messages
C = utils.integrated_function(infoBits, U_k, k, Nt, -1)
X_m = utils.symbols_generator(t) # all possible symbol sequences
nx = 2**t * key
# print('nx', nx, 't', t)
x = utils.mapping(C, X_m, t, nx)
N = len(x[1])
if graph == 'BLER':
metric[f"Int_P({key})"] = utils.block_error_probability(
N, k, C, e0, e1)
else:
metric[f"Int_P({key})"] = utils.bit_error_rate(
k, x, nb_pkts, e0, e1)
def BER_NN(nb_pkts=100):
# e0 = np.logspace(-3, 0, 15)
# e0 = np.linspace(0.001, 0.999, 11)
e0 = np.concatenate(
(np.array([0.001]), np.linspace(
0.01, 0.1, 10, endpoint=False), np.linspace(0.1, 1, 15)),
axis=0)
e0[len(e0) - 1] = e0[len(e0) - 1] - 0.001
e1 = [t for t in e0 if t <= 0.5]
inter_list = np.array(np.tile([0, 0, 0, 1], (2**k, 1)))
C = np.round(model_encoder.predict([np.array(u_k),
inter_list])).astype('int')
print('codebook \n', C)
print('codebook C is Linear? ', utils.isLinear(C))
aux = []
for code in C.tolist():
if code not in aux:
aux.append(code)
nb_repeated_codes = len(C) - len(aux)
print('+++++++++++++++++++ Repeated Codes NN encoder = ',
nb_repeated_codes)
print('dist = ', sum([sum(codeword) for codeword in C]) * 1.0 / (N * 2**k))
print('***************************************************************')
if nb_repeated_codes == 0:
BER = test.read_ber_file(N, k, 'BER')
BER = test.saved_results(BER, N, k)
BLER = test.read_ber_file(N, k, 'BLER')
BLER = test.saved_results(BLER, N, k, 'BLER')
print("NN BER")
t = time.time()
BER['auto-array-inter'], BLER['auto-array-inter'] = bit_error_rate_NN(
N, k, C, nb_pkts, e0, e1, channel)
t = time.time() - t
print(f"NN time = {t}s ========================")
print("BER['auto-array-inter'] = ", BER['auto-array-inter'])
print("BLER['auto-array-inter'] = ", BLER['auto-array-inter'])
if MAP_test:
print("MAP BER")
t = time.time()
BER['MAP'] = utils.bit_error_rate(k, C, 1000, e0, e1)
t = time.time() - t
print(f"MAP time = {t}s =======================")
print("NN BLEP")
t = time.time()
BLER['auto_BLEP'] = utils.block_error_probability(N, k, C, e0, e1)
t = time.time() - t
print(f"NN time = {t}s ========================")
utils.plot_BSC_BAC(f'BER Coding Mechanism N={N} k={k} - NN Interval',
BER, k / N)
utils.plot_BSC_BAC(f'BLER Coding Mechanism N={N} k={k} - NN Interval',
BLER, k / N)
else:
print('Bad codebook repeated codewords')
def uncoded(metric, k=4, nb_pkts = 100, graph = 'BER'):
print('-------------------Uncoded-----------------------------')
key = f"Uncode"
N = k
U_k = utils.symbols_generator(k) # all possible messages
if graph == 'BLER':
metric[key] = utils.block_error_probability(N,k,U_k,e0,e1)
else:
metric[key] = utils.bit_error_rate(k,U_k,nb_pkts,e0,e1,False)
def flip_codes(metric, N=8, k=4, nb_pkts=100, graph='BER', channel='BSC'):
print('-------------------Flip Code-----------------------------')
key = '2,4,6'
all_C = flip.codebook_generator_k4(N)
C = all_C[2, 4, 6]
print('Flip codebook', np.array(C))
if graph == 'BLER':
metric[f"Flip({key})"] = utils.block_error_probability(N, k, C, e0, e1)
else:
metric[f"Flip({key})"] = utils.bit_error_rate(k, C, nb_pkts, e0, e1)
def polar_codes(metric, N=8, k=4, nb_pkts = 100, graph = 'BER',channel='BSC'):
print('-------------------Polar Code-----------------------------')
for key in [0.1]:
G, infoBits = polar.polar_generator_matrix(N,k, channel, key)
k = len(G)
N = len(G[1])
U_k = utils.symbols_generator(k) # all possible messages
C = utils.matrix_codes(U_k, k, G, N)
# print('Polar codebook', np.array(C))
if graph == 'BLER':
metric[f"Polar({key})"] = utils.block_error_probability(N,k,C,e0,e1)
else:
metric[f"Polar({key})"] = utils.bit_error_rate(k,C,nb_pkts,e0,e1)
print(metric[f"Polar({key})"])
def polar_codes_NN(metric, N=8, k=4, nb_pkts = 100, graph = 'BER',channel='BSC'):
print('-------------------Polar Code + NN decoder -----------------------------')
key = 'NN_dec'
G,infoBits = polar.polar_generator_matrix(N, k, channel, 0.1)
# print('G = ', np.array(G))
k = len(G)
N = len(G[1])
U_k = utils.symbols_generator(k) # all possible messages
C = utils.matrix_codes(U_k, k, G, N)
print('k ', k, 'N ', N)
if graph == 'BLER':
metric[key] = utils.block_error_probability(N, k, C, e0, e1)
else:
metric[key] = utils.bit_error_rate_NN(N, k, C, nb_pkts, e0, e1, channel)
def bch_codes(metric, N=8, k=4, nb_pkts = 100, graph = 'BER'):
print('-------------------BCH Code-----------------------------')
G = mat_gen.matrix_codes(N, k, 'bch')
if G != []:
for key in [0]:
# print('G = ', np.array(G))
k = len(G)
N = len(G[1])
U_k = utils.symbols_generator(k) # all possible messages
C = utils.matrix_codes(U_k, k, G, N)
print('k ',k,'N ',N)
if graph == 'BLER':
metric[f"BCH({key})"] = utils.block_error_probability(N,k,C,e0,e1)
else:
metric[f"BCH({key})"] = utils.bit_error_rate(k,C,nb_pkts,e0,e1)
def linear_codes(metric, N=8, k=4, nb_pkts=100, graph='BER'):
print('-------------------Linear Code-----------------------------')
for key in ['BKLC']:
print(key)
G = mat_gen.matrix_codes(N, k, key)
if G != []:
# print('G = ', np.array(G))
k = len(G)
N = len(G[1])
U_k = utils.symbols_generator(k) # all possible messages
C = utils.matrix_codes(U_k, k, G, N)
print(np.array(C))
print('k ', k, 'N ', N)
if graph == 'BLER':
metric[key] = utils.block_error_probability(N, k, C, e0, e1)
else:
metric[key] = utils.bit_error_rate(k, C, nb_pkts, e0, e1)
def linear_codes_mapping(metric, N=8, k=4, nb_pkts = 100, graph = 'BER'):
print('-------------------Linear Code + Mapping-----------------------------')
G = mat_gen.matrix_codes(64,k,'linear')
if G!= []:
for key in [0.55]:
k = len(G)
Nt = len(G[1])
t = int(Nt/N)
U_k= utils.symbols_generator(k) # all possible messages
X_m = utils.symbols_generator(t) # all possible symbol sequences
C = utils.matrix_codes(U_k, k, G, Nt)
nx = 2**t*key
# print('nx', nx, 't', t)
x = utils.mapping(C, X_m, t, nx) #codebook after mapping
N = len(x[1])
if graph == 'BLER':
metric[f"L+M({key})"] = utils.block_error_probability(N,k,x,e0,e1)
else:
metric[f"L+M({key})"] = utils.bit_error_rate(k,x,nb_pkts,e0,e1)
