def test_OFDM_rx_channel_estimate(self):
z_out_test, H_out_test = (
np.array(
[
-2.91356233 - 0.93854058j, -3.03083561 - 1.01177886j,
3.10687062 + 1.09962706j, 2.91679784 + 2.79392693j,
2.95621370 + 0.87789714j, 2.93521287 + 1.12869418j,
-3.17675560 - 1.0834705j, 1.25700626 + 1.19497994j,
1.16433902 + 2.62068101j, -3.10408334 + 1.08514004j,
1.02623864 + 3.01672402j, -0.98366297 - 1.21602375j,
0.89577012 + 1.07687508j, 1.05852406 + 1.05134363j,
0.93287609 - 3.11042385j, 0.99965390 + 0.88124784j,
-1.16293758 + 3.08562314j, -2.84891079 - 1.07199168j,
-3.22236927 - 2.90425199j, 3.07028549 - 2.88413491j,
-3.12192058 + 2.89625467j, 3.18017151 - 1.09375776j,
-2.78212772 + 3.05087219j, 1.13471595 - 2.89218144j,
-3.17092453 - 1.11298847j, 3.10927184 + 0.86801524j,
-0.76520964 - 3.32101721j, -0.94935570 + 2.86081052j,
0.93535950 + 1.10545223j, 1.09394518 - 1.17966519j,
3.10748055 + 1.12377382j, -3.12337017 - 0.89848715j,
-2.95725651 + 0.97491592j, 3.14041238 - 3.01998896j,
-1.05440640 + 3.04843936j, -0.94130790 - 0.82179287j,
-0.79049810 - 1.04083796j, 2.96004080 + 1.01692442j,
-3.13063510 + 1.32083138j, -2.58084447 - 3.28171534j,
3.09664605 + 0.82140179j, 2.87565015 - 1.17002378j,
2.82351021 + 2.83242155j, 2.99238994 + 3.06883778j,
-0.83601519 - 2.8886988j, 3.05383614 + 1.22402533j,
-0.92550302 + 0.92366226j, -0.97707573 + 3.08608891j,
0.73489228 - 2.99163649j, 2.89544691 + 2.76671634j
]),
np.array(
[
1.49261307 - 0.12886832j, 1.36399692 - 0.24831791j,
1.24438887 - 0.41524198j, 1.15276504 - 0.47480443j,
1.09981815 - 0.35438673j, 0.86684483 - 0.31710329j,
0.75885865 - 0.23542562j, 0.76309583 - 0.19374055j,
0.61556098 + 0.09731796j, 0.77281595 + 0.07096727j,
0.87593303 + 0.15642133j, 1.06728467 + 0.29788462j,
1.08613086 + 0.23650714j, 1.12082635 + 0.09129381j,
1.31026672 + 0.17419224j, 1.19459330 + 0.01027668j,
1.19745209 + 0.11471611j, 1.36689249 - 0.07997548j,
1.26471663 - 0.07505238j, 1.14356226 - 0.19961235j,
0.84149706 - 0.21609579j, 0.85489994 - 0.18101042j,
0.79502365 - 0.17155484j, 0.71666634 - 0.02650505j,
0.82384118 + 0.0565963j, 0.74313589 + 0.28403893j,
0.88570493 + 0.29345603j, 0.95203301 + 0.37888469j,
0.98676887 + 0.4108844j, 1.26869289 + 0.35672436j,
1.44594176 + 0.3296819j, 1.48817425 + 0.07577518j
]))
hc = np.array([1.0, 0.1, -0.05, 0.15, 0.2, 0.05])
x1, b1, IQ_data1 = dc.QAM_bb(50000, 1, '16qam')
x_out = dc.OFDM_tx(IQ_data1, 32, 64, 100, True, 10)
c_out = signal.lfilter(hc, 1, x_out) # Apply channel distortion
r_out = dc.cpx_AWGN(c_out, 25, 64 / 32) # Es/N0 = 25 dB
z_out, H = dc.OFDM_rx(r_out, 32, 64, 100, True, 10, alpha=0.95, ht=hc)
npt.assert_almost_equal(z_out[:50], z_out_test)
npt.assert_almost_equal(H[:50], H_out_test)
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_QAM_SEP_256qam(self):
Nsymb_test, Nerr_test, SEP_test = (4986, 2190, 0.43922984356197353)
x, b, tx_data = dc.QAM_bb(5000, 10, '256qam', 'src')
x = dc.cpx_AWGN(x, 20, 10)
y = signal.lfilter(b, 1, x)
Nsymb, Nerr, SEP = dc.QAM_SEP(tx_data,
y[10 + 10 * 12::10],
'256qam',
Ntransient=0)
self.assertEqual(Nsymb, Nsymb_test)
self.assertEqual(Nerr, Nerr_test)
self.assertEqual(SEP, SEP_test)
def test_QAM_SEP_64qam(self):
Nsymb_test, Nerr_test, SEP_test = (4986, 245, 0.04913758523866827)
x, b, tx_data = dc.QAM_bb(5000, 10, '64qam', 'src')
x = dc.cpx_AWGN(x, 20, 10)
y = signal.lfilter(b, 1, x)
Nsymb, Nerr, SEP = dc.QAM_SEP(tx_data,
y[10 + 10 * 12::10],
'64qam',
Ntransient=0)
self.assertEqual(Nsymb, Nsymb_test)
self.assertEqual(Nerr, Nerr_test)
self.assertEqual(SEP, SEP_test)
def test_QAM_SEP_qpsk(self):
Nsymb_test, Nerr_test, SEP_test = (4986, 0, 0.0)
x, b, tx_data = dc.QAM_bb(5000, 10, 'qpsk', 'src')
x = dc.cpx_AWGN(x, 20, 10)
y = signal.lfilter(b, 1, x)
Nsymb, Nerr, SEP = dc.QAM_SEP(tx_data,
y[10 + 10 * 12::10],
'qpsk',
Ntransient=0)
self.assertEqual(Nsymb, Nsymb_test)
self.assertEqual(Nerr, Nerr_test)
self.assertEqual(SEP, SEP_test)
def test_QAM_SEP_16qam_error(self):
Nsymb_test, Nerr_test, SEP_test = (9976, 172, 0.017241379310344827)
x, b, tx_data = dc.QAM_bb(10000, 1, '16qam', 'rect')
x = dc.cpx_AWGN(x, 15, 1)
y = signal.lfilter(b, 1, x)
Nsymb, Nerr, SEP = dc.QAM_SEP(tx_data,
y[1 * 12::1],
'16qam',
Ntransient=0)
self.assertEqual(Nsymb, Nsymb_test)
self.assertEqual(Nerr, Nerr_test)
self.assertEqual(SEP, SEP_test)
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)
def test_OFDM_rx(self):
z_out_test, H_test = (
np.array(
[
-3.11740028 - 0.90748269j, -3.11628187 - 0.88948888j,
2.88565859 + 1.13255112j, 2.89076997 + 3.16052588j,
2.90396853 + 1.19595053j, 2.93439648 + 1.23703401j,
-3.00724063 - 0.72880083j, 1.07519281 + 1.27075039j,
1.14472192 + 3.22099905j, -2.82962216 + 1.15148633j,
1.16245397 + 3.09533441j, -0.85799363 - 0.94063529j,
1.12036257 + 1.03825793j, 1.10109739 + 1.02622557j,
1.08488052 - 2.98041713j, 1.07132873 + 1.01625511j,
-0.92119499 + 3.01872286j, -2.91683903 - 0.9906338j,
-2.91213253 - 3.00295552j, 3.09229992 - 3.01974828j
]),
np.array([
1.42289223 - 1.43696423e-01j, 1.34580486 - 2.66705232e-01j,
1.23071709 - 3.51736667e-01j, 1.09530096 - 3.87688911e-01j,
0.95992898 - 3.71339473e-01j, 0.84428862 - 3.07656711e-01j,
0.76421410 - 2.08710707e-01j, 0.72928932 - 9.14213562e-02j,
0.74161551 + 2.54124114e-02j, 0.79590656 + 1.24244087e-01j,
0.88082345 + 1.91510354e-01j, 0.98123573 + 2.19504072e-01j,
1.08094630 + 2.07118520e-01j, 1.16536231 + 1.59418930e-01j,
1.22364402 + 8.62177170e-02j, 1.25000000 - 2.77555756e-17j,
1.25000000 + 2.77555756e-17j, 1.22364402 - 8.62177170e-02j,
1.16536231 - 1.59418930e-01j, 1.08094630 - 2.07118520e-01j,
0.98123573 - 2.19504072e-01j, 0.88082345 - 1.91510354e-01j,
0.79590656 - 1.24244087e-01j, 0.74161551 - 2.54124114e-02j,
0.72928932 + 9.14213562e-02j, 0.76421410 + 2.08710707e-01j,
0.84428862 + 3.07656711e-01j, 0.95992898 + 3.71339473e-01j,
1.09530096 + 3.87688911e-01j, 1.23071709 + 3.51736667e-01j,
1.34580486 + 2.66705232e-01j, 1.42289223 + 1.43696423e-01j
]))
hc = np.array([1.0, 0.1, -0.05, 0.15, 0.2, 0.05])
x1, b1, IQ_data1 = dc.QAM_bb(50000, 1, '16qam')
x_out = dc.OFDM_tx(IQ_data1, 32, 64, 0, True, 0)
c_out = signal.lfilter(hc, 1, x_out) # Apply channel distortion
r_out = dc.cpx_AWGN(c_out, 100, 64 / 32) # Es/N0 = 100 dB
z_out, H = dc.OFDM_rx(r_out, 32, 64, -1, True, 0, alpha=0.95, ht=hc)
npt.assert_almost_equal(z_out[:20], z_out_test)
npt.assert_almost_equal(H, H_test)
def channelOutput(self,x):
#orig_shape = x.shape
x_flat = np.ravel(x)
if(self.channel_type=='OFDM'):
symbols = self.Modulation(x_flat);
orig_symb_size = np.size(symbols);
symbols = self.fit_to_input(x,self.num_carriers)
symb_rows = symbols.shape[0];
self.myOFDM = OFDM_module(symbols[0,:]);
self.myOFDM.setSNR(self.EbN0)
y = self.myOFDM.OFDM_run()
for i in range(1,symb_rows):
self.myOFDM = OFDM_module(symbols[i,:])
self.myOFDM.setSNR(self.EbN0)
y = np.append(y,self.myOFDM.OFDM_run())
y = np.ravel(y);
y = y[0:orig_symb_size];
else:
y = dc.cpx_AWGN(2*x_flat-1,self.EbN0+10*np.log10(self.k/self.N),1);
if((self.channel_type=='OFDM')):
return y;
else:
return y.real;