def test_convolutional_stream_encoder():
# Abrantes.10, p. 307.
code = komm.ConvolutionalCode(feedforward_polynomials=[[0b111, 0b101]])
convolutional_encoder = komm.ConvolutionalStreamEncoder(code)
assert np.array_equal(convolutional_encoder([1, 0, 1, 1, 1, 0, 1, 1, 0, 0]), [1,1, 1,0, 0,0, 0,1, 1,0, 0,1, 0,0, 0,1, 0,1, 1,1])
# Lin.Costello.04, p. 454--456.
code = komm.ConvolutionalCode(feedforward_polynomials=[[0b1101, 0b1111]])
convolutional_encoder = komm.ConvolutionalStreamEncoder(code)
assert np.array_equal(convolutional_encoder([1, 0, 1, 1, 1, 0, 0, 0]), [1,1, 0,1, 0,0, 0,1, 0,1, 0,1, 0,0, 1,1])
# Lin.Costello.04, p. 456--458.
code = komm.ConvolutionalCode(feedforward_polynomials=[[0b11, 0b10, 0b11], [0b10, 0b1, 0b1]])
convolutional_encoder = komm.ConvolutionalStreamEncoder(code)
assert np.array_equal(convolutional_encoder([1,1, 0,1, 1,0, 0,0]), [1,1,0, 0,0,0, 0,0,1, 1,1,1])
# Ryan.Lin.09, p. 154.
code = komm.ConvolutionalCode(feedforward_polynomials=[[0b111, 0b101]])
convolutional_encoder = komm.ConvolutionalStreamEncoder(code)
assert np.array_equal(convolutional_encoder([1, 0, 0, 0]), [1,1, 1,0, 1,1, 0,0])
# Ibid.
code = komm.ConvolutionalCode(feedforward_polynomials=[[0b111, 0b101]], feedback_polynomials=[0b111])
convolutional_encoder = komm.ConvolutionalStreamEncoder(code)
assert np.array_equal(convolutional_encoder([1, 1, 1, 0]), [1,1, 1,0, 1,1, 0,0])
def __init__(self, TX_BIN, SNR, MODULATION, BCH_CODE, CCODE, TBLEN, METHOD):
self.tx_bin = TX_BIN
self.snr = SNR
self.modulation = MODULATION
self.BCH_code = BCH_CODE
self.C_encoder = komm.ConvolutionalStreamEncoder(CCODE)
self.C_decoder = komm.ConvolutionalStreamDecoder(CCODE, traceback_length=TBLEN, input_type=METHOD)
self.tblen = TBLEN
self.method = METHOD
def test_convolutional_space_state_representation_2(feedforward_polynomials, feedback_polynomials):
code = komm.ConvolutionalCode(feedforward_polynomials, feedback_polynomials)
n, k, nu = code.num_output_bits, code.num_input_bits, code.overall_constraint_length
A = code.state_matrix
B = code.control_matrix
C = code.observation_matrix
D = code.transition_matrix
input_bits = np.random.randint(2, size=100*k)
output_bits = np.empty(n * input_bits.size // k, dtype=np.int)
s = np.zeros(nu, dtype=np.int)
for t, u in enumerate(np.reshape(input_bits, newshape=(-1, k))):
s, v = (np.dot(s, A) + np.dot(u, B)) % 2, (np.dot(s, C) + np.dot(u, D)) % 2
output_bits[t*n : (t+1)*n] = v
convolutional_encoder = komm.ConvolutionalStreamEncoder(code)
assert np.array_equal(output_bits, convolutional_encoder(input_bits))
def test_convolutional_stream_encoder_2(feedforward_polynomials, feedback_polynomials, message, codeword):
code = komm.ConvolutionalCode(feedforward_polynomials, feedback_polynomials)
convolutional_encoder = komm.ConvolutionalStreamEncoder(code)
assert np.array_equal(convolutional_encoder(message), codeword)
awgn = [komm.AWGNChannel(snr=10**(x / 10.)) for x in range(max_snr)]
# array to store simulated bit error ratio
ber = np.zeros(max_snr)
# array to store signal-to-noise[dB] ratio for the channel
snr = np.zeros(max_snr)
# rate 1/2 code with [0o7, 0o5] as the feedforward polynomial
code, tblen = FEC.ConvCode.create_conv_codes()
# flatten the image into a 1D array and
# append tblen zeros to compensate for the decoder delay
tx_bin = np.append(np.unpackbits(np.array(tx_im)), np.zeros(tblen))
# simulate channel encoding
encoder = komm.ConvolutionalStreamEncoder(code)
code_word = encoder(tx_bin)
# simulate modulation
tx_data = qpsk.modulate(code_word)
# list containing decoding decision methods
d = args.d
decoder = komm.ConvolutionalStreamDecoder(code,
traceback_length=tblen,
input_type=d)
string = " ".join([d, "decoding"])
print(" ".join(["Simulating", string, "..."]))
# loop to to simulate transmission with the SNR value in range 0 to max_snr