signal_power = mapper.get_signal_average_power()
# what is the noise power and standard deviation when SNR is 10dB?
noise_power = signal_power / 10.0
noise_std_dev = math.sqrt(noise_power)
# initialize random number generator. this seed is fixed constant in order
# to get deterministic results in this example.
random.seed(314159265359)
# add white gaussian noise at 10dB to signal
print "Adding white gaussian noise at 10dB."
noisy_symbols = [sym + random.gauss(0, noise_std_dev) for sym in symbols]
# round to closest integer
noisy_symbols = [int(x + 0.5) for x in noisy_symbols]
print "noisy symbols:", noisy_symbols
# instantiate decoder
decoder = Decoder(k, B, d, map_func)
# update decoder with gathered points
for i in xrange(spine_length):
decoder.advance([
noisy_symbols[i], noisy_symbols[i + spine_length],
noisy_symbols[i + 2 * spine_length]
])
print "decoded hex:", decoder.get_most_likely().encode("hex")
# make sure we got the message we started with
assert (decoder.get_most_likely() == message)
# get average signal power
signal_power = mapper.get_signal_average_power()
# what is the noise power and standard deviation when SNR is 10dB?
noise_power = signal_power / 10.0
noise_std_dev = math.sqrt(noise_power)
# initialize random number generator. this seed is fixed constant in order
# to get deterministic results in this example.
random.seed(314159265359)
# add white gaussian noise at 10dB to signal
print "Adding white gaussian noise at 10dB."
noisy_symbols = [sym + random.gauss(0, noise_std_dev) for sym in symbols]
# round to closest integer
noisy_symbols = [int(x + 0.5) for x in noisy_symbols]
print "noisy symbols:", noisy_symbols
# instantiate decoder
decoder = Decoder(k, B, d, map_func)
# update decoder with gathered points
for i in xrange(spine_length):
decoder.advance([noisy_symbols[i],
noisy_symbols[i+spine_length],
noisy_symbols[i+2*spine_length]])
print "decoded hex:", decoder.get_most_likely().encode("hex")
# make sure we got the message we started with
assert(decoder.get_most_likely() == message)
# print "Adding white gaussian noise at 10dB."
# noisy_symbols = [sym + random.gauss(0, noise_std_dev) for sym in symbols]
# # round to closest integer
# noisy_symbols = [int(x + 0.5) for x in noisy_symbols]
# print "noisy symbols:", noisy_symbols
# [cgl] myself noise symbols. if the prr is low, while the number of errors in one packet is rare, then Spinal code can also achieve very good performance.
noisy_symbols = [sym for sym in symbols]
noisy_symbols[3] += 3
###===============================================================================
# instantiate decoder
decoder = Decoder(k, B, d, map_func)
# update decoder with gathered points
for i in xrange(spine_length):
decodeAdv = []
for j in range(numPssMsg):
decodeAdv.append(noisy_symbols[i + j * spine_length])
decoder.advance(decodeAdv)
# decoder.advance([noisy_symbols[i],
# noisy_symbols[i+spine_length]])
# decoder.advance([noisy_symbols[i],
# noisy_symbols[i+spine_length],
# noisy_symbols[i+2*spine_length]])
print "decoded hex:", decoder.get_most_likely().encode("hex")
# make sure we got the message we started with
assert (decoder.get_most_likely() == message)
