def test_fec_conv_Pb_bound_1(self):
Pb_s_test = np.array([ 4.38709821e-02, 1.10836941e-02, 2.49608462e-03,
5.06172637e-04, 9.14155349e-05, 1.41608827e-05,
1.77960366e-06, 1.69942208e-07, 1.14537090e-08,
5.00593591e-10, 1.28592062e-11, 1.73135359e-13,
1.06932930e-15, 2.59433518e-18])
SNRdB = np.arange(2, 12, .75)
Pb_s = fec_conv.conv_Pb_bound(1./2.,5,[1,4,12,32,80,192,448,1024],SNRdB,1)
npt.assert_almost_equal(Pb_s_test, Pb_s)
def test_fec_conv_conv_Pb_bound_2(self):
Pb_uc_test = np.array([ 3.75061284e-02, 2.61319950e-02, 1.71725417e-02,
1.05322374e-02, 5.95386715e-03, 3.05639669e-03,
1.39980484e-03, 5.60054773e-04, 1.90907774e-04,
5.38158927e-05, 1.21088933e-05, 2.08499657e-06,
2.61306795e-07, 2.24575706e-08])
SNRdB = np.arange(2, 12, .75)
Pb_uc = fec_conv.conv_Pb_bound(1./2., 5, [1, 4, 12, 32, 80, 192, 448, 1024], SNRdB, 2)
npt.assert_almost_equal(Pb_uc_test, Pb_uc)
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.FecConv()
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()