def test_calc_effective_throughput(self):
psk_obj = fundamental.PSK(8)
packet_length = 60
SINRs_dB = np.array([11.4, 20.3])
sinrs_linear = dB2Linear(SINRs_dB)
expected_spectral_efficiency = np.sum(
psk_obj.calcTheoreticalSpectralEfficiency(SINRs_dB, packet_length))
spectral_efficiency = blockdiagonalization._calc_effective_throughput(
sinrs_linear, psk_obj, packet_length)
np.testing.assert_array_almost_equal(spectral_efficiency,
expected_spectral_efficiency)
def __init__(self, ):
SimulationRunner.__init__(self)
SNR = np.array([0, 3, 6, 9, 12])
M = 4
self.modulator = fundamental.PSK(M)
self.NSymbs = 500
self.rep_max = 1000
self.max_bit_errors = 1. / 100. * self.NSymbs * self.rep_max
# self.progressbar_message = None
self.progressbar_message = "{0}-PSK".format(M) + \
" Simulation - SNR: {SNR}"
# Add the parameters to the self.params variable
self.params.add('SNR', SNR)
self.params.set_unpack_parameter('SNR')
def __init__(self, ):
super().__init__()
SNR = np.array([0, 3, 6, 9, 12])
M = 4
modulator = fundamental.PSK(M)
NSymbs = 500
self.params.add('modulator', modulator)
self.params.add('NSymbs', NSymbs)
self.rep_max = 1000
# self.max_bit_errors = 1. / 100. * NSymbs * self.rep_max
max_bit_errors = 1. / 100. * NSymbs * self.rep_max
self.params.add('max_bit_errors', max_bit_errors)
# self.progressbar_message = None
self.progressbar_message = "{0}-PSK".format(M) + \
" Simulation - SNR: {SNR}"
# Add the parameters to the self.params variable
self.params.add('SNR', SNR)
self.params.set_unpack_parameter('SNR')
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# Cell and Grid Parameters
cell_radius = 1.0 # Cell radius (in Km)
num_cells = 3
num_clusters = 1
# Channel Parameters
Nr = np.ones(num_cells) * 2 # Number of receive antennas
Nt = np.ones(num_cells) * 2 # Number of transmit antennas
Ns_BD = Nt # Number of streams (per user) in the BD algorithm
path_loss_obj = pathloss.PathLoss3GPP1()
multiuser_channel = pyphysim.channels.multiuser.MultiUserChannelMatrixExtInt()
# Modulation Parameters
M = 4
modulator = fundamental.PSK(M)
# Transmission Parameters
NSymbs = 500 # Number of symbols (/stream /user simulated at each iteration
SNR_dB = 15.
N0_dBm = -116.4 # Noise power (in dBm)
# External Interference Parameters
Pe_dBm = -10000 # transmit power (in dBm) of the ext. interference
ext_int_rank = 1 # Rank of the external interference
# xxxxxxxxxx General Parameters xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
rep_max = 20000 # Maximum number of repetitions for each
pbar = progressbar.ProgressbarText(
rep_max, message="Simulating for SNR: {0}".format(SNR_dB))
def test_block_diagonalize_no_waterfilling(self):
Nr = np.array([2, 2])
Nt = np.array([2, 2])
K = Nt.size
Nti = 1
iPu = 1e-1 # Power for each user (linear scale)
pe = 1e-3 # External interference power (in linear scale)
noise_var = 1e-1
# The modulator and packet_length are required in the
# effective_throughput metric case
psk_obj = fundamental.PSK(4)
packet_length = 120
multiUserChannel = multiuser.MultiUserChannelMatrixExtInt()
multiUserChannel.randomize(Nr, Nt, K, Nti)
multiUserChannel.noise_var = noise_var
# Channel from all transmitters to the first receiver
H1 = multiUserChannel.get_Hk_without_ext_int(0)
# Channel from all transmitters to the second receiver
H2 = multiUserChannel.get_Hk_without_ext_int(1)
# Create the enhancedBD object
enhancedBD_obj = blockdiagonalization.EnhancedBD(K, iPu, noise_var, pe)
noise_plus_int_cov_matrix \
= multiUserChannel.calc_cov_matrix_extint_plus_noise(pe)
# xxxxx First we test without ext. int. handling xxxxxxxxxxxxxxxxxx
enhancedBD_obj.set_ext_int_handling_metric(None)
(Ms_all, Wk_all, Ns_all) \
= enhancedBD_obj.block_diagonalize_no_waterfilling(
multiUserChannel)
Ms1 = Ms_all[0]
Ms2 = Ms_all[1]
self.assertEqual(Ms1.shape[1], Ns_all[0])
self.assertEqual(Ms2.shape[1], Ns_all[1])
# Most likely only one base station (the one with the worst
# channel) will employ a precoder with total power of `Pu`,
# while the other base stations will use less power.
tol = 1e-10
self.assertGreaterEqual(iPu + tol, np.linalg.norm(Ms1, 'fro')**2)
# 1e-12 is included to avoid false test fails due to small
# precision errors
self.assertGreaterEqual(iPu + tol, np.linalg.norm(Ms2, 'fro')**2)
# Test if the precoder block diagonalizes the channel
self.assertNotAlmostEqual(np.linalg.norm(np.dot(H1, Ms1), 'fro'), 0)
self.assertAlmostEqual(np.linalg.norm(np.dot(H1, Ms2), 'fro'), 0)
self.assertNotAlmostEqual(np.linalg.norm(np.dot(H2, Ms2), 'fro'), 0)
self.assertAlmostEqual(np.linalg.norm(np.dot(H2, Ms1), 'fro'), 0)
# Equivalent sinrs (in linear scale)
sinrs = np.empty(K, dtype=np.ndarray)
sinrs[0] = blockdiagonalization.EnhancedBD._calc_linear_SINRs(
np.dot(H1, Ms1), Wk_all[0], noise_plus_int_cov_matrix[0])
sinrs[1] = blockdiagonalization.EnhancedBD._calc_linear_SINRs(
np.dot(H2, Ms2), Wk_all[1], noise_plus_int_cov_matrix[1])
# Spectral efficiency
# noinspection PyPep8
se = (np.sum(
psk_obj.calcTheoreticalSpectralEfficiency(linear2dB(
sinrs[0]), packet_length)) + np.sum(
psk_obj.calcTheoreticalSpectralEfficiency(
linear2dB(sinrs[1]), packet_length)))
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxxxx Now with the Naive Stream Reduction xxxxxxxxxxxxxxxxxxxxxxx
num_streams = 1
enhancedBD_obj.set_ext_int_handling_metric(
'naive', {'num_streams': num_streams})
(MsPk_naive_all, Wk_naive_all, Ns_naive_all) \
= enhancedBD_obj.block_diagonalize_no_waterfilling(
multiUserChannel)
MsPk_naive_1 = MsPk_naive_all[0]
MsPk_naive_2 = MsPk_naive_all[1]
self.assertEqual(MsPk_naive_1.shape[1], Ns_naive_all[0])
self.assertEqual(MsPk_naive_2.shape[1], Ns_naive_all[1])
self.assertEqual(Ns_naive_all[0], num_streams)
self.assertEqual(Ns_naive_all[1], num_streams)
# Test if the square of the Frobenius norm of the precoder of each
# user is equal to the power available to that user.
self.assertAlmostEqual(iPu, np.linalg.norm(MsPk_naive_1, 'fro')**2)
self.assertAlmostEqual(iPu, np.linalg.norm(MsPk_naive_2, 'fro')**2)
# Test if MsPk really block diagonalizes the channel
self.assertNotAlmostEqual(
np.linalg.norm(np.dot(H1, MsPk_naive_1), 'fro'), 0)
self.assertAlmostEqual(np.linalg.norm(np.dot(H1, MsPk_naive_2), 'fro'),
0)
self.assertNotAlmostEqual(
np.linalg.norm(np.dot(H2, MsPk_naive_2), 'fro'), 0)
self.assertAlmostEqual(np.linalg.norm(np.dot(H2, MsPk_naive_1), 'fro'),
0)
sinrs4 = np.empty(K, dtype=np.ndarray)
sinrs4[0] = blockdiagonalization.EnhancedBD._calc_linear_SINRs(
np.dot(H1, MsPk_naive_1), Wk_naive_all[0],
noise_plus_int_cov_matrix[0])
sinrs4[1] = blockdiagonalization.EnhancedBD._calc_linear_SINRs(
np.dot(H2, MsPk_naive_2), Wk_naive_all[1],
noise_plus_int_cov_matrix[1])
# Spectral efficiency
# se4 = (
# np.sum(psk_obj.calcTheoreticalSpectralEfficiency(
# linear2dB(sinrs4[0]),
# packet_length))
# +
# np.sum(psk_obj.calcTheoreticalSpectralEfficiency(
# linear2dB(sinrs4[1]),
# packet_length)))
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxxxx Now with the Fixed Stream Reduction xxxxxxxxxxxxxxxxxxxxxxx
# The 'fixed' metric requires that metric_func_extra_args_dict is
# provided and has the 'num_streams' key. If this is not the case
# an exception is raised
with self.assertRaises(AttributeError):
enhancedBD_obj.set_ext_int_handling_metric('fixed')
# Now let's test the fixed metric
num_streams = 1
enhancedBD_obj.set_ext_int_handling_metric(
'fixed', {'num_streams': num_streams})
(MsPk_fixed_all, Wk_fixed_all, Ns_fixed_all) \
= enhancedBD_obj.block_diagonalize_no_waterfilling(
multiUserChannel)
MsPk_fixed_1 = MsPk_fixed_all[0]
MsPk_fixed_2 = MsPk_fixed_all[1]
self.assertEqual(MsPk_fixed_1.shape[1], Ns_fixed_all[0])
self.assertEqual(MsPk_fixed_2.shape[1], Ns_fixed_all[1])
self.assertEqual(Ns_fixed_all[0], num_streams)
self.assertEqual(Ns_fixed_all[1], num_streams)
# Test if the square of the Frobenius norm of the precoder of each
# user is equal to the power available to that user.
self.assertAlmostEqual(iPu, np.linalg.norm(MsPk_fixed_1, 'fro')**2)
self.assertAlmostEqual(iPu, np.linalg.norm(MsPk_fixed_2, 'fro')**2)
# Test if MsPk really block diagonalizes the channel
self.assertNotAlmostEqual(
np.linalg.norm(np.dot(H1, MsPk_fixed_1), 'fro'), 0)
self.assertAlmostEqual(np.linalg.norm(np.dot(H1, MsPk_fixed_2), 'fro'),
0)
self.assertNotAlmostEqual(
np.linalg.norm(np.dot(H2, MsPk_fixed_2), 'fro'), 0)
self.assertAlmostEqual(np.linalg.norm(np.dot(H2, MsPk_fixed_1), 'fro'),
0)
sinrs5 = np.empty(K, dtype=np.ndarray)
sinrs5[0] = blockdiagonalization.EnhancedBD._calc_linear_SINRs(
np.dot(H1, MsPk_fixed_1), Wk_fixed_all[0],
noise_plus_int_cov_matrix[0])
sinrs5[1] = blockdiagonalization.EnhancedBD._calc_linear_SINRs(
np.dot(H2, MsPk_fixed_2), Wk_fixed_all[1],
noise_plus_int_cov_matrix[1])
# Spectral efficiency
# se5 = (
# np.sum(psk_obj.calcTheoreticalSpectralEfficiency(
# linear2dB(sinrs5[0]),
# packet_length))
# +
# np.sum(psk_obj.calcTheoreticalSpectralEfficiency(
# linear2dB(sinrs5[1]),
# packet_length)))
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxxxx Handling external interference xxxxxxxxxxxxxxxxxxxxxxxxxxxx
# Handling external interference using the capacity metric
enhancedBD_obj.set_ext_int_handling_metric('capacity')
(MsPk_all, Wk_cap_all, Ns_cap_all) \
= enhancedBD_obj.block_diagonalize_no_waterfilling(
multiUserChannel)
MsPk_cap_1 = MsPk_all[0]
MsPk_cap_2 = MsPk_all[1]
self.assertEqual(MsPk_cap_1.shape[1], Ns_cap_all[0])
self.assertEqual(MsPk_cap_2.shape[1], Ns_cap_all[1])
# Test if the square of the Frobenius norm of the precoder of each
# user is equal to the power available to that user.
self.assertAlmostEqual(iPu, np.linalg.norm(MsPk_cap_1, 'fro')**2)
self.assertAlmostEqual(iPu, np.linalg.norm(MsPk_cap_2, 'fro')**2)
# Test if MsPk really block diagonalizes the channel
self.assertNotAlmostEqual(
np.linalg.norm(np.dot(H1, MsPk_cap_1), 'fro'), 0)
self.assertAlmostEqual(np.linalg.norm(np.dot(H1, MsPk_cap_2), 'fro'),
0)
self.assertNotAlmostEqual(
np.linalg.norm(np.dot(H2, MsPk_cap_2), 'fro'), 0)
self.assertAlmostEqual(np.linalg.norm(np.dot(H2, MsPk_cap_1), 'fro'),
0)
sinrs2 = np.empty(K, dtype=np.ndarray)
sinrs2[0] = blockdiagonalization.EnhancedBD._calc_linear_SINRs(
np.dot(H1, MsPk_cap_1), Wk_cap_all[0],
noise_plus_int_cov_matrix[0])
sinrs2[1] = blockdiagonalization.EnhancedBD._calc_linear_SINRs(
np.dot(H2, MsPk_cap_2), Wk_cap_all[1],
noise_plus_int_cov_matrix[1])
# Spectral efficiency
# noinspection PyPep8
se2 = (np.sum(
psk_obj.calcTheoreticalSpectralEfficiency(linear2dB(
sinrs2[0]), packet_length)) + np.sum(
psk_obj.calcTheoreticalSpectralEfficiency(
linear2dB(sinrs2[1]), packet_length)))
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxxxx Handling external interference xxxxxxxxxxxxxxxxxxxxxxxxxxxx
# Handling external interference using the effective_throughput metric
enhancedBD_obj.set_ext_int_handling_metric(
'effective_throughput', {
'modulator': psk_obj,
'packet_length': packet_length
})
(MsPk_effec_all, Wk_effec_all, Ns_effec_all) \
= enhancedBD_obj.block_diagonalize_no_waterfilling(
multiUserChannel)
MsPk_effec_1 = MsPk_effec_all[0]
MsPk_effec_2 = MsPk_effec_all[1]
self.assertEqual(MsPk_effec_1.shape[1], Ns_effec_all[0])
self.assertEqual(MsPk_effec_2.shape[1], Ns_effec_all[1])
# Test if the square of the Frobenius norm of the precoder of each
# user is equal to the power available to that user.
self.assertAlmostEqual(iPu, np.linalg.norm(MsPk_effec_1, 'fro')**2)
self.assertAlmostEqual(iPu, np.linalg.norm(MsPk_effec_2, 'fro')**2)
# Test if MsPk really block diagonalizes the channel
self.assertNotAlmostEqual(
np.linalg.norm(np.dot(H1, MsPk_effec_1), 'fro'), 0)
self.assertAlmostEqual(np.linalg.norm(np.dot(H1, MsPk_effec_2), 'fro'),
0)
self.assertNotAlmostEqual(
np.linalg.norm(np.dot(H2, MsPk_effec_2), 'fro'), 0)
self.assertAlmostEqual(np.linalg.norm(np.dot(H2, MsPk_effec_1), 'fro'),
0)
sinrs3 = np.empty(K, dtype=np.ndarray)
sinrs3[0] = blockdiagonalization.EnhancedBD._calc_linear_SINRs(
np.dot(H1, MsPk_effec_1), Wk_effec_all[0],
noise_plus_int_cov_matrix[0])
sinrs3[1] = blockdiagonalization.EnhancedBD._calc_linear_SINRs(
np.dot(H2, MsPk_effec_2), Wk_effec_all[1],
noise_plus_int_cov_matrix[1])
# Spectral efficiency
# noinspection PyPep8
se3 = (np.sum(
psk_obj.calcTheoreticalSpectralEfficiency(linear2dB(
sinrs3[0]), packet_length)) + np.sum(
psk_obj.calcTheoreticalSpectralEfficiency(
linear2dB(sinrs3[1]), packet_length)))
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# Test if the effective_throughput obtains a better spectral
# efficiency then the capacity and not handling interference.
self.assertGreater(se3 + tol, se2)
self.assertGreater(se3 + tol, se)
def test_set_ext_int_handling_metric(self):
K = 3
iPu = 1e-3 # Power for each user (linear scale)
noise_var = 1e-4
pe = 0
# Create the EnhancedBD object
enhancedBD_obj = blockdiagonalization.EnhancedBD(K, iPu, noise_var, pe)
# xxxxx Test if an assert is raised for invalid arguments xxxxxxxxx
with self.assertRaises(AttributeError):
enhancedBD_obj.set_ext_int_handling_metric('lala')
with self.assertRaises(AttributeError):
# If we set the metric to effective_throughput but not provide
# the modulator and packet_length attributes.
enhancedBD_obj.set_ext_int_handling_metric('effective_throughput')
with self.assertRaises(AttributeError):
enhancedBD_obj.set_ext_int_handling_metric('naive')
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxxxx Test setting the metric to effective_throughput xxxxxxxxxxx
psk_obj = fundamental.PSK(4)
enhancedBD_obj.set_ext_int_handling_metric('effective_throughput', {
'modulator': psk_obj,
'packet_length': 120
})
self.assertEqual(enhancedBD_obj._metric_func,
blockdiagonalization._calc_effective_throughput)
self.assertEqual(enhancedBD_obj.metric_name, "effective_throughput")
metric_func_extra_args = enhancedBD_obj._metric_func_extra_args
self.assertEqual(metric_func_extra_args['modulator'], psk_obj)
self.assertEqual(metric_func_extra_args['packet_length'], 120)
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxxxx Test setting the metric to capacity xxxxxxxxxxxxxxxxxxxxxxx
enhancedBD_obj.set_ext_int_handling_metric('capacity')
self.assertEqual(enhancedBD_obj._metric_func,
calc_shannon_sum_capacity)
self.assertEqual(enhancedBD_obj.metric_name, "capacity")
# metric_func_extra_args is an empty dictionary for the capacity
# metric
self.assertEqual(enhancedBD_obj._metric_func_extra_args, {})
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxxxx Test setting the metric to None xxxxxxxxxxxxxxxxxxxxxxxxxxx
enhancedBD_obj.set_ext_int_handling_metric(None)
self.assertIsNone(enhancedBD_obj._metric_func)
self.assertEqual(enhancedBD_obj.metric_name, "None")
# metric_func_extra_args is an empty dictionary for the None metric
self.assertEqual(enhancedBD_obj._metric_func_extra_args, {})
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxxxx Test setting the metric to naive xxxxxxxxxxxxxxxxxxxxxxxxxx
enhancedBD_obj.set_ext_int_handling_metric('naive', {'num_streams': 2})
self.assertIsNone(enhancedBD_obj._metric_func)
self.assertEqual(enhancedBD_obj.metric_name, "naive")
metric_func_extra_args = enhancedBD_obj._metric_func_extra_args
self.assertEqual(metric_func_extra_args['num_streams'], 2)
def setUp(self):
"""Called before each test."""
self.psk_obj = fundamental.PSK(4)
self.psk_obj2 = fundamental.PSK(8)
def _update_modulator_object(self, ):
"""Updates the modulator object whenever M changes
"""
self.modulator = mod.PSK(self.M)
