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 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', n_transient=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', n_transient=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', n_transient=0)
self.assertEqual(Nsymb, Nsymb_test)
self.assertEqual(Nerr, Nerr_test)
self.assertEqual(SEP, SEP_test)
def test_qam_sep_16qam_no_error(self):
Nsymb_test, Nerr_test, SEP_test = (4986, 0, 0.0)
x, b, tx_data = dc.qam_bb(5000, 10, '16qam', '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], '16qam', n_transient=0)
self.assertEqual(Nsymb, Nsymb_test)
self.assertEqual(Nerr, Nerr_test)
self.assertEqual(SEP, SEP_test)
def test_fec_conv_viterbi_decoder(self):
cc1 = fec_conv.FECConv()
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_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_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.FECConv()
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)