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-4
# # The modulator and packet_length are required in the
# # effective_throughput metric case
# psk_obj = modulators.PSK(4)
# packet_length = 120
multiUserChannel = multiuser.MultiUserChannelMatrixExtInt()
multiUserChannel.randomize(Nr, Nt, K, Nti)
# 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 whiteningBD object and the regular BD object
whiteningBD_obj = blockdiagonalization.WhiteningBD(
K, iPu, noise_var, pe)
# noise_plus_int_cov_matrix \
# = multiUserChannel.calc_cov_matrix_extint_plus_noise(
# noise_var, pe)
# xxxxx First we test without ext. int. handling xxxxxxxxxxxxxxxxxx
(Ms_all, Wk_all, Ns_all) \
= whiteningBD_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)
def test_calc_whitening_matrices(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-4
# Generate the multi-user channel
mu_channel = multiuser.MultiUserChannelMatrixExtInt()
mu_channel.randomize(Nr, Nt, K, Nti)
mu_channel.noise_var = noise_var
bd_obj = blockdiagonalization.BDWithExtIntBase(K, iPu, noise_var, pe)
W_all_k = bd_obj.calc_whitening_matrices(mu_channel)
R_all_k = mu_channel.calc_cov_matrix_extint_plus_noise(pe)
for W, R in zip(W_all_k, R_all_k):
np.testing.assert_array_almost_equal(
W,
calc_whitening_matrix(R).conjugate().T)
def __init__(self, read_command_line_args=True, save_parsed_file=False):
default_config_file = 'bd_config_file.txt'
# xxxxxxxxxx Simulation Parameters Specification xxxxxxxxxxxxxxxxxx
spec = """[Grid]
cell_radius=float(min=0.01, default=1.0)
num_cells=integer(min=3,default=3)
num_clusters=integer(min=1,default=1)
[Scenario]
NSymbs=integer(min=10, max=1000000, default=500)
SNR=real_numpy_array(min=-50, max=100, default=0:3:31)
Pe_dBm=real_numpy_array(min=-50, max=100, default=[-10. 0. 10.])
Nr=integer(default=2)
Nt=integer(default=2)
N0=float(default=-116.4)
ext_int_rank=integer(min=1,default=1)
user_positioning_method=option("Random", 'Symmetric Far Away', default="Symmetric Far Away")
[Modulation]
M=integer(min=4, max=512, default=4)
modulator=option('QPSK', 'PSK', 'QAM', 'BPSK', default="PSK")
packet_length=integer(min=1,default=60)
[General]
rep_max=integer(min=1, default=5000)
unpacked_parameters=string_list(default=list('SNR','Pe_dBm'))
""".split("\n")
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxxxxxxxxx Initialize parameters configuration xxxxxxxxxxxxxxxxxx
# Among other things, this will create the self.params object with
# the simulation parameters read from the config file.
SimulationRunner.__init__(
self,
default_config_file=default_config_file,
config_spec=spec,
read_command_line_args=read_command_line_args,
save_parsed_file=save_parsed_file)
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxxxxxxxxx Channel Parameters xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
self.path_loss_obj = pathloss.PathLoss3GPP1()
self.multiuser_channel = multiuser.MultiUserChannelMatrixExtInt()
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxxxxxxxxx RandomState objects seeds xxxxxxxxxxxxxxxxxxxxxxxxxxxx
# This is only useful to reproduce a simulation for debugging
# purposed
channel_seed = None # 22522
self.noise_seed = None # 4445
self.data_gen_seed = np.random.randint(10000) # 2105
ext_data_gen_seed = None # 6114
#
self.multiuser_channel.set_channel_seed(channel_seed)
self.multiuser_channel.set_noise_seed(self.noise_seed)
self.data_RS = np.random.RandomState(self.data_gen_seed)
self.ext_data_RS = np.random.RandomState(ext_data_gen_seed)
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxxxxxxxxx Creates the modulator object xxxxxxxxxxxxxxxxxxxxxxxxx
M = self.params['M']
modulator_options = {
'PSK': fundamental.PSK,
'QPSK': fundamental.QPSK,
'QAM': fundamental.QAM,
'BPSK': fundamental.BPSK
}
self.modulator = modulator_options[self.params['modulator']](M)
":type: fundamental.PSK | fundamental.QPSK | fundamental.QAM | fundamental.BPSK"
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxxxxxxxxx General Parameters xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# Maximum number of repetitions for each unpacked parameters set
# self.params self.results
self.rep_max = self.params['rep_max']
# # max_bit_errors is used in the _keep_going method to stop the
# # simulation earlier if possible. We stop the simulation if the
# # accumulated number of bit errors becomes greater then 5% of the
# # total number of simulated bits
# self.max_bit_errors = self.rep_max * NSymbs * 5. / 100.
self.progressbar_message = "SNR: {SNR}, Pe_dBm: {Pe_dBm}"
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxxxxxxxxx Dependent parameters (don't change these) xxxxxxxxxxxx
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# These two will be set in the _on_simulate_current_params_start
# method
self.pe = 0
# Path loss (in linear scale) from the cell center to
# self.path_loss_border = self.path_loss_obj.calc_path_loss(
# self.cell_radius)
# Cell Grid
self.cell_grid = cell.Grid()
self.cell_grid.create_clusters(self.params['num_clusters'],
self.params['num_cells'],
self.params['cell_radius'])
self.noise_var = dBm2Linear(self.params['N0'])
self.multiuser_channel.noise_var = self.noise_var
# This can be either 'screen' or 'file'. If it is 'file' then the
# progressbar will write the progress to a file with appropriated
# filename
self.progress_output_type = 'screen'
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)
