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conv_codes_benchmark.py
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conv_codes_benchmark.py
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__author__ = 'yihanjiang'
'''
Evaluate convolutional code benchmark.
'''
from utils import corrupt_signal, get_test_sigmas
import sys
import numpy as np
import time
import commpy.channelcoding.convcode as cc
from commpy.utilities import hamming_dist
import multiprocessing as mp
def get_args():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('-num_block', type=int, default=100)
parser.add_argument('-block_len', type=int, default=100)
parser.add_argument('-code_rate', type=int, default=2)
parser.add_argument('-enc1', type=int, default=7)
parser.add_argument('-enc2', type=int, default=5)
parser.add_argument('-enc3', type=int, default=1)
parser.add_argument('-feedback', type=int, default=7)
parser.add_argument('-num_cpu', type=int, default=4)
parser.add_argument('-snr_test_start', type=float, default=-1.0)
parser.add_argument('-snr_test_end', type=float, default=8.0)
parser.add_argument('-snr_points', type=int, default=10)
parser.add_argument('-noise_type', choices = ['awgn', 't-dist','hyeji_bursty'], default='awgn')
parser.add_argument('-radar_power', type=float, default=20.0)
parser.add_argument('-radar_prob', type=float, default=0.05)
parser.add_argument('-radar_denoise_thd', type=float, default=10.0)
parser.add_argument('-v', type=int, default=3)
parser.add_argument('-id', type=str, default=str(np.random.random())[2:8])
args = parser.parse_args()
print args
print '[ID]', args.id
return args
if __name__ == '__main__':
args = get_args()
##########################################
# Setting Up Codec
##########################################
M = np.array([2]) # Number of delay elements in the convolutional encoder
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]])
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 = 15
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)
received = corrupt_signal(coded_bits, noise_type =args.noise_type, sigma = test_sigmas[idx],
vv =args.v, radar_power = args.radar_power, radar_prob = args.radar_prob,
denoise_thd = args.radar_denoise_thd)
# make fair comparison between (100, 204) convolutional code and (100,200) RNN decoder, set the additional bit to 0
received[-2*int(M):] = 0.0
decoded_bits = cc.viterbi_decode(received.astype(float), trellis1, tb_depth, decoding_type='unquantized')
decoded_bits = decoded_bits[:-int(M)]
num_bit_errors = hamming_dist(message_bits, decoded_bits)
return num_bit_errors
commpy_res_ber = []
commpy_res_bler= []
nb_errors = np.zeros(test_sigmas.shape)
map_nb_errors = np.zeros(test_sigmas.shape)
nb_block_no_errors = np.zeros(test_sigmas.shape)
for idx in range(len(test_sigmas)):
start_time = time.time()
pool = mp.Pool(processes=args.num_cpu)
results = pool.map(turbo_compute, [(idx,x) for x in range(args.num_block)])
for result in results:
if result == 0:
nb_block_no_errors[idx] = nb_block_no_errors[idx]+1
nb_errors[idx]+= sum(results)
print '[testing]SNR: ' , SNRS[idx]
print '[testing]BER: ', sum(results)/float(args.block_len*args.num_block)
print '[testing]BLER: ', 1.0 - nb_block_no_errors[idx]/args.num_block
commpy_res_ber.append(sum(results)/float(args.block_len*args.num_block))
commpy_res_bler.append(1.0 - nb_block_no_errors[idx]/args.num_block)
end_time = time.time()
print '[testing] This SNR runnig time is', str(end_time-start_time)
print '[Result]SNR: ', SNRS
print '[Result]BER', commpy_res_ber
print '[Result]BLER', commpy_res_bler
toc = time.time()
print '[Result]Total Running time:', toc-tic