pbar = progressbar.ProgressbarText(
rep_max, message="Simulating for SNR: {0}".format(SNR_dB))
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxxxxxxxxx Dependent parameters (don't change these) xxxxxxxxxxxx
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# Path loss (in linear scale) from the cell center to
path_loss_border = path_loss_obj.calc_path_loss(cell_radius)
noise_var = conversion.dBm2Linear(N0_dBm)
snr = conversion.dB2Linear(SNR_dB)
transmit_power = snr * noise_var / path_loss_border
# External interference power
pe = conversion.dBm2Linear(Pe_dBm)
# Cell Grid
cell_grid = cell.Grid()
cell_grid.create_clusters(num_clusters, num_cells, cell_radius)
# noinspection PyProtectedMember
cluster0 = cell_grid._clusters[0]
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxxxxxxxxx Create the scenario xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
cell_ids = np.arange(1, num_cells + 1)
angles = np.array([210, -30, 90])
cluster0.delete_all_users()
cluster0.add_border_users(cell_ids, angles, 0.7)
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
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 __init__(self, default_config_file, read_command_line_args=True):
"""
Constructor of the IASimulationRunner class.
"""
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=200)
SNR=real_numpy_array(min=-50, max=100, default=0:5:31)
M=integer(min=4, max=512, default=4)
modulator=option('QPSK', 'PSK', 'QAM', 'BPSK', default="PSK")
Nr=integer_scalar_or_integer_numpy_array_check(min=2,default=3)
Nt=integer_scalar_or_integer_numpy_array_check(min=2,default=3)
Ns=integer_scalar_or_integer_numpy_array_check(min=1,default=3)
N0=float(default=-116.4)
scenario=string_list(default=list('Random', 'NoPathLoss'))
[IA Algorithm]
max_iterations=integer(min=1, default=120)
initialize_with=string_list(default=list('random'))
stream_sel_method=string_list(default=list('greedy', 'brute'))
[General]
rep_max=integer(min=1, default=2000)
max_bit_errors=integer(min=1, default=3000)
unpacked_parameters=string_list(default=list('SNR','stream_sel_method','scenario','initialize_with'))
""".split("\n")
SimulationRunner.__init__(self, default_config_file, spec,
read_command_line_args)
# xxxxxxxxxx General Parameters xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# Maximum number of repetitions for each unpacked parameters set
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.params['max_bit_errors']
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxxxxxxxxx Channel and Path Loss Parameters xxxxxxxxxxxxxxxxxxxxx
# Create the channel object
self.multiUserChannel = multiuser.MultiUserChannelMatrix()
# Create the Path loss object
self.path_loss_obj = pathloss.PathLoss3GPP1()
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxxxxxxxxx RandomState objects seeds xxxxxxxxxxxxxxxxxxxxxxxxxxxx
# This is only useful to reproduce a simulation for debugging
# purposed
self.channel_seed = None # 22522
self.noise_seed = None # 4445
self.data_gen_seed = np.random.randint(10000) # 2105
#
self.multiUserChannel.set_channel_seed(self.channel_seed)
self.multiUserChannel.set_noise_seed(self.noise_seed)
self.data_RS = np.random.RandomState(self.data_gen_seed)
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxxxxxxxxx Create the modulator object xxxxxxxxxxxxxxxxxxxxxxxxxx
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)
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxxxxxxxxx Progress Bar xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# 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'
# Set the progressbar message
self.progressbar_message = "SNR: {{SNR}}".format(self.modulator.name)
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxxxxxxxxx Dependent parameters (don't change these) xxxxxxxxxxxx
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# Cell Grid
self.cell_grid = cell.Grid()
self.cell_grid.create_clusters(self.params['num_clusters'],
self.params['num_cells'],
self.params['cell_radius'])
# Note that the Noise variance will be set in the
# _on_simulate_current_params_start method. In the NoPathLoss
# scenario it will be set as 1.0 regardless of the value of
# params['N0'] to avoid problems in the IA algorithms. In the other
# scenarios it will be set to self.params['N0'].
#
# In any case the transmit power will be calculated accordingly in
# the _run_simulation method and the simulation results will still
# be correct.
self.noise_var = None
# Linear path loss from cell center to cell border.
self._path_loss_border = self.path_loss_obj.calc_path_loss(
self.params['cell_radius'])
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# TODO: Move code below to the _on_simulate_current_params_start
# method
# xxxxxxxxxx Interference Alignment objects xxxxxxxxxxxxxxxxxxxxxxx
# Create the basic IA Solver object
self.ia_solver = algorithms.MMSEIASolver(self.multiUserChannel)
# This will be created in the _on_simulate_current_params_start
# method. The class of self.ia_top_object will depend on the value
# of the 'stream_sel_method' parameter
self.ia_top_object = None
