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)
pathlossInt = path_loss_obj.calc_path_loss(cluster0.external_radius -
distance_users_to_cluster_center)
pathlossInt.shape = (num_cells, 1)
num_symbol_errors = 0.
num_symbols = 0.
num_bit_errors = 0.
num_bits = 0.
for rep in range(rep_max):
# Randomize users channels
multiuser_channel.randomize(Nr, Nt, num_cells, ext_int_rank)
multiuser_channel.set_pathloss(pathloss, pathlossInt)
multiuser_channel.noise_var = noise_var
# Create the comp_obj
comp_obj = blockdiagonalization.EnhancedBD(num_cells, transmit_power,
noise_var, pe)
# comp_obj.set_ext_int_handling_metric('capacity')
comp_obj.set_ext_int_handling_metric('effective_throughput', {
'modulator': modulator,
'packet_length': packet_length
})
(MsPk_all_users,
Wk_all_users,
Ns_all_users) \
= comp_obj.block_diagonalize_no_waterfilling(multiuser_channel)
# xxxxx Performs the actual transmission for each user xxxxxxxxxxxxxxxx
# Generate input data and modulate it
input_data = np.random.randint(0, M, [np.sum(Ns_all_users), NSymbs])
symbols = modulator.modulate(input_data)
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)
SNR_dB = 15. N0_dBm = -116.4 # Noise power (in dBm) # External Interference Parameters Pe_dBm = -100 # transmit power (in dBm) of the ext. interference ext_int_rank = 1 # Rank of the external interference # xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx # xxxxxxxxxxxxxxx Dependent Variables xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx # xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx noise_var = conversion.dBm2Linear(N0_dBm) snr = conversion.dB2Linear(SNR_dB) transmit_power = 1.0 # snr * noise_var # External interference power pe = conversion.dBm2Linear(Pe_dBm) # xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx # Generate the channel multiuser_channel = pyphysim.channels.multiuser.MultiUserChannelMatrixExtInt() multiuser_channel.randomize(Nr, Nt, K, ext_int_rank) # Generate input data and modulate it input_data = np.random.randint(0, M, [np.sum(Ns_BD), NSymbs]) symbols = modulator.modulate(input_data) BD = blockdiagonalization.BlockDiagonalizer(K, transmit_power, noise_var) enhancedBD = blockdiagonalization.EnhancedBD(K, transmit_power, noise_var, pe) (newH, Ms) = BD.block_diagonalize_no_waterfilling(multiuser_channel.big_H_no_ext_int) (newH_ext, Ms_ext, Ns_all_users) = enhancedBD.block_diagonalize_no_waterfilling(multiuser_channel)