if args.code_rate == 2:
generator_matrix = np.array([[args.enc1, args.enc2]])
elif args.code_rate == 3:
generator_matrix = np.array([[args.enc1, args.enc2, args.enc3]])
elif args.code_rate == 4:
generator_matrix = np.array(
[[args.enc1, args.enc2, args.enc3, args.enc4]])
else:
print 'Not supported!'
sys.exit()
feedback = args.feedback
print '[testing] Convolutional Code Encoder: G: ', generator_matrix, 'Feedback: ', feedback, 'M: ', M
trellis1 = cc.Trellis(M, generator_matrix,
feedback=feedback) # Create trellis data structure
SNRS, test_sigmas = get_test_sigmas(args.snr_test_start, args.snr_test_end,
args.snr_points)
tic = time.time()
tb_depth = args.tb
def turbo_compute((idx, x)):
'''
Compute Turbo Decoding in 1 iterations for one SNR point.
'''
np.random.seed()
message_bits = np.random.randint(0, 2, args.block_len)
coded_bits = cc.conv_encode(message_bits, trellis1)
print args
print '[ID]', args.id
return args
if __name__ == '__main__':
args = get_args()
M = np.array([args.M
]) # Number of delay elements in the convolutional encoder
generator_matrix = np.array([[args.enc1, args.enc2]
]) # Encoder of convolutional encoder
feedback = args.feedback # Feedback of convolutional encoder
print '[testing] Turbo Code Encoder: G: ', generator_matrix, 'Feedback: ', feedback, 'M: ', M
trellis1 = cc.Trellis(M, generator_matrix, feedback=feedback)
trellis2 = cc.Trellis(M, generator_matrix, feedback=feedback)
interleaver = RandInterlv.RandInterlv(args.block_len, 0)
p_array = interleaver.p_array
codec = [trellis1, trellis2, interleaver]
snrs, test_sigmas = get_test_sigmas(args.snr_test_start, args.snr_test_end,
args.snr_points)
turbo_res_ber, turbo_res_bler = [], []
tic = time.time()
def turbo_compute((idx, x)):
'''
Compute Turbo Decoding in 1 iterations for one SNR point.
'''