def test_ChannelM2ULA(self):
np.set_printoptions(linewidth=np.inf)
wavelength = frequencyToWavelength(5.0 * 10 ** 9) # 5 [GHz]
IT, M, N, ae_spacing, distance_tx_rx = 10000, 2, 2, wavelength / 2, 5.0
tx, ty, tz = IdealRicianChannel.getPositionsUniformLinearArray(M, ae_spacing, 0)
rx, ry, rz = IdealRicianChannel.getPositionsUniformLinearArray(N, ae_spacing, distance_tx_rx)
channel = IdealRicianChannel(IT, 10, wavelength, tx, ty, tz, rx, ry, rz)
channel.randomize()
H = channel.getChannel().reshape(IT, N, M)
norms = np.square(np.linalg.norm(H, axis=(1, 2)))
self.assertAlmostEqual(np.mean(norms), M * N, places=1)
meanrank = np.mean(np.linalg.matrix_rank(H))
self.assertAlmostEqual(meanrank, M)
def test_ChannelM16N8Rec(self):
np.set_printoptions(linewidth=np.inf)
wavelength = frequencyToWavelength(5.0 * 10 ** 9) # 5 [GHz]
IT, M, N = 100000, 16, 8
tx, ty, tz = IdealRicianChannel.getPositionsRectangular2d(M, wavelength, 3.0)
rx, ry, rz = IdealRicianChannel.getPositionsRectangular2d(N, wavelength, 0.0)
channel = IdealRicianChannel(IT, 2.5, wavelength, tx, ty, tz, rx, ry, rz)
channel.randomize()
H = channel.getChannel().reshape(IT, N, M)
norms = np.square(np.linalg.norm(H, axis=(1, 2)))
self.assertAlmostEqual(np.mean(norms), M * N, places=1)
meanrank = np.mean(np.linalg.matrix_rank(H))
self.assertAlmostEqual(meanrank, N)
print(np.mean(np.linalg.svd(H)[1], axis=0))
def test_ChannelM16ULAbohagen(self):
np.set_printoptions(linewidth=np.inf)
wavelength = frequencyToWavelength(5.0 * 10 ** 9) # 5 [GHz]
IT, M, N, ae_spacing, distance_tx_rx = 100000, 16, 4, wavelength, 5.0
rx, ry, rz = IdealRicianChannel.getPositionsUniformLinearArray(N, ae_spacing, distance_tx_rx)
dtx = distance_tx_rx / max(M, N)
tx, ty, tz = IdealRicianChannel.getPositionsUniformLinearArray(M, dtx, 0)
channel = IdealRicianChannel(IT, 2.5, wavelength, tx, ty, tz, rx, ry, rz)
channel.randomize()
H = channel.getChannel().reshape(IT, N, M)
norms = np.square(np.linalg.norm(H, axis=(1, 2)))
self.assertAlmostEqual(np.mean(norms), M * N, places=1)
meanrank = np.mean(np.linalg.matrix_rank(H))
self.assertAlmostEqual(meanrank, N)
print(np.mean(np.linalg.svd(H)[1], axis=0))