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_ODFM_tx(self):
x_out_test = np.array([
0.00000000 + 0.125j,
-0.10185331 + 0.27369942j,
-0.10291586 + 0.12529202j,
-0.05485981 - 0.1015143j,
-0.02143872 - 0.09787268j,
-0.06906044 + 0.05231368j,
-0.18815224 + 0.050888j,
-0.26164122 - 0.15836327j,
-0.21940048 - 0.36048543j,
-0.14486054 - 0.38169759j,
-0.11830476 - 0.25561157j,
-0.07250935 - 0.12760226j,
0.05301567 - 0.08413918j,
0.14316564 - 0.07020723j,
0.07590886 + 0.01736066j,
-0.04551924 + 0.15686941j,
-0.03125000 + 0.21875j,
0.09755018 + 0.17168517j,
0.15431728 + 0.10974492j,
0.08889087 + 0.04259743j,
0.04284671 - 0.1107734j,
0.10071736 - 0.25986197j,
0.15582045 - 0.17226253j,
0.06652251 + 0.12312402j,
-0.15245874 + 0.29798543j,
-0.32346606 + 0.23845079j,
-0.25311017 + 0.21460293j,
0.07831717 + 0.3396657j,
0.43085592 + 0.30360811j,
0.48116320 - 0.0505655j,
0.16656460 - 0.32765262j,
-0.20071609 - 0.16142259j,
-0.25000000 + 0.1875j,
0.04290155 + 0.25900306j,
0.33313987 + 0.08484705j,
0.28478134 - 0.00986648j,
-0.05936711 + 0.00190181j,
-0.30195965 - 0.0628197j,
-0.12280721 - 0.1651266j,
0.31807654 - 0.16252886j,
0.53190048 - 0.13951457j,
0.31342228 - 0.20065005j,
0.00806130 - 0.17969398j,
-0.00105255 + 0.03378639j,
0.15279016 + 0.16494501j,
0.09844557 - 0.009236j,
-0.11589986 - 0.20597693j,
-0.10438721 - 0.09983656j,
0.15625000 + 0.09375j,
0.22805837 + 0.03951473j,
])
x1, b1, IQ_data1 = dc.QAM_bb(50000, 1, '16qam')
x_out = dc.OFDM_tx(IQ_data1, 32, 64, 0, True, 0)
npt.assert_almost_equal(x_out[:50], x_out_test)
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)
#0.707 #varianza np.var(symbols_comparison) #2 #the sign of a number. np.sign([-5., 4.5]) np.sign(0) #Test Array testarray = np.array([]) testarray = (2, 2) #Ejemplo Ofdm x1, b1, IQ_data1 = dc.QAM_bb(50000, 1, '16qam') x_out = dc.OFDM_tx(IQ_data1, 32, 64) plt.psd(x_out, 2**10, 1) plt.xlabel(r'Normalized Frequency ($\omega/(2\pi)=f/f_s$)') plt.ylim([-40, 0]) plt.xlim([-.5, .5]) #Round elements of the array to the nearest integer. a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0]) np.rint(a) #Draw samples from a binomial distribution. np.random.binomial(size=3, n=1, p=0.5) #size: number of experiments; n: number of trails; p: probability of success # Agregar elementos Array R2p = np.array([], dtype=float)