def test_fec_conv_puncture(self):
yp_test = [0., 0., 0., 1., 0., 1., 1., 0., 0., 1., 1., 0., 0., 0., 0., 1., 1., 0.,
1., 0., 0., 0., 1., 0.]
cc1 = fec_conv.fec_conv()
x = np.random.randint(0, 2, 20)
state = '00'
y, state = cc1.conv_encoder(x, state)
yp = cc1.puncture(y, ('110', '101'))
npt.assert_almost_equal(yp_test, yp)
def test_fec_conv_viterbi_decoder(self):
cc1 = fec_conv.fec_conv()
x = np.random.randint(0,2,20)
state = '00'
y, state = cc1.conv_encoder(x, state)
z_test = [ 0., 0., 1., 1., 1., 1., 0., 0., 0., 0., 0.]
yn = dc.cpx_AWGN(2 * y - 1, 5, 1)
yn = (yn.real + 1) / 2 * 7
z = cc1.viterbi_decoder(yn)
npt.assert_almost_equal(z_test, z)
def test_fec_conv_conv_encoder(self):
cc1 = fec_conv.fec_conv()
x = np.random.randint(0, 2, 20)
state = '00'
y_test, state_test = (np.array([ 0., 0., 0., 0., 1., 1., 0., 1., 1., 0., 1., 0., 0.,
1., 1., 1., 0., 0., 0., 0., 0., 0., 1., 1., 1., 0.,
1., 1., 0., 0., 0., 0., 1., 1., 1., 0., 1., 1., 1., 1.]), '10')
y, state = cc1.conv_encoder(x, state)
npt.assert_almost_equal(y_test, y)
self.assertEqual(state_test, state)
def test_fec_conv_depuncture(self):
zdpn_test = [-0.18077499, 0.24326595, -0.43694799, 3.5, 3.5, 7.41513671,
-0.55673726, 7.77925472, 7.64176133, 3.5, 3.5, -0.09960601,
-0.50683017, 7.98234306, 6.58202794, 3.5, 3.5, -1.0668518,
1.54447404, 1.47065852, -0.24028734, 3.5, 3.5, 6.19633424,
7.1760269, 0.89395647, 7.69735877, 3.5, 3.5, 1.29889556,
-0.31122416, 0.05311373, 7.21216449, 3.5, 3.5, -1.37679829]
cc1 = fec_conv.fec_conv()
x = np.random.randint(0, 2, 20)
state = '00'
y, state = cc1.conv_encoder(x, state)
yp = cc1.puncture(y, ('110', '101'))
ypn = dc.cpx_AWGN(2 * yp - 1, 8, 1)
ypn = (ypn.real + 1) / 2 * 7
zdpn = cc1.depuncture(ypn, ('110', '101'), 3.5) # set erase threshold to 7/2
npt.assert_almost_equal(zdpn_test, zdpn)
import numpy as np
import sk_dsp_comm.sigsys as ss
import scipy.signal as signal
import scipy.special as special
import sk_dsp_comm.digitalcom as dc
import sk_dsp_comm.fec_conv as fec
#Como siempre gracias stackoverflow
#https://stackoverflow.com/questions/10321978/integer-to-bitfield-as-a-list
def bitfield(n):
return [int(digit) for digit in '{0:04b}'.format(n)]
cc1 = fec.fec_conv(('1111001', '1011011'))
#Generamos el arreglo con todos los datos necesarios en formato de bits.
#En nuestro testbench tenemos desde 0 a 15, de a 4 bits.
int_array = np.arange(0, 16)
#Nos quedamos con los ultimos 4 bits o sea los menos significativos
bit_array = np.array([bitfield(item) for item in int_array])
bit_array = bit_array.reshape(bit_array.size, 1)
#Aca todo perfectamente serializado. Siendo el orden de envio MSb FIRST+
# Cadena a enviar 0x0 0x0 ........................0xE 0xF
# Cadena a enviar 0000 0001 1110 1111
y, state = cc1.conv_encoder(bit_array, '0000000')
y = np.array(y, dtype=np.int8)
y = y.reshape(y.size // 4, 4)[:, ::-1]
def test_fec_conv_Nstates(self):
G = ('111', '101')
cc = fec_conv.fec_conv(G)
self.assertEqual(4, cc.Nstates)
def test_fec_conv_inst(self):
cc1 = fec_conv.fec_conv(('101', '111'), Depth=10) # decision depth is 10
import numpy as np
from sk_dsp_comm import fec_conv
from sk_dsp_comm import digitalcom as dc
np.random.seed(100)
cc = fec_conv.fec_conv()
print(cc.Nstates)
import matplotlib.pyplot as plt
import numpy as np
from sk_dsp_comm import fec_conv as fc
SNRdB = np.arange(2,12,.1)
Pb_uc = fc.conv_Pb_bound(1/2,5,[1,4,12,32,80,192,448,1024],SNRdB,2)
Pb_s = fc.conv_Pb_bound(1/2,5,[1,4,12,32,80,192,448,1024],SNRdB,1)
plt.figure(figsize=(5,5))
plt.semilogy(SNRdB,Pb_uc)
plt.semilogy(SNRdB,Pb_s)
plt.axis([2,12,1e-7,1e0])
plt.xlabel(r'$E_b/N_0$ (dB)')
plt.ylabel(r'Symbol Error Probability')
#plt.legend(('Uncoded BPSK','R=1/2, K=5, Soft'),loc='best')
plt.grid();
plt.show()
