def _run_simulation(self, current_parameters): # pylint: disable=R0914
"""The _run_simulation method is where the actual code to simulate
the system is.
The implementation of this method is required by every subclass of
SimulationRunner.
Parameters
----------
current_parameters : SimulationParameters object
SimulationParameters object with the parameters for the
simulation. The self.params variable is not used directly. It
is first unpacked (in the SimulationRunner.simulate method
which then calls _run_simulation) for each combination.
"""
# xxxxxxxxxx Prepare the scenario for this iteration. xxxxxxxxxxxxx
# This will place the users at the locations specified by the
# scenario (random locations or not), calculate the path loss and
# generate a new random channel (in the self.multiuser_channel
# variable).
self._create_users_according_to_scenario(current_parameters)
# This will calculate pathloss and generate random channels from
# all transmitters to all receivers as well as from the external
# interference sources to all receivers. This method must be called
# after the _create_users_according_to_scenario method so that the
# users are already created (we need their positions for the
# pathloss)
self._create_users_channels(current_parameters)
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxxxx Perform the block diagonalization for each metric xxxxxxxxx
# None Metric
(MsPk_all_users_None,
Wk_all_users_None,
Ns_all_users_None) \
= self.bd_obj_None.block_diagonalize_no_waterfilling(
self.multiuser_channel)
# Naive Metric
(MsPk_all_users_naive,
Wk_all_users_naive,
Ns_all_users_naive) \
= self.bd_obj_naive.block_diagonalize_no_waterfilling(
self.multiuser_channel)
# Fixed Metric
(MsPk_all_users_fixed,
Wk_all_users_fixed,
Ns_all_users_fixed) \
= self.bd_obj_fixed.block_diagonalize_no_waterfilling(
self.multiuser_channel)
# Capacity Metric
(MsPk_all_users_capacity,
Wk_all_users_capacity,
Ns_all_users_capacity) \
= self.bd_obj_capacity.block_diagonalize_no_waterfilling(
self.multiuser_channel)
# effective_throughput Metric
(MsPk_all_users_effec_throughput,
Wk_all_users_effec_throughput,
Ns_all_users_effec_throughput) \
= self.bd_obj_effec_throughput.block_diagonalize_no_waterfilling(
self.multiuser_channel)
(Ms_all_users_Whitening,
Wk_all_users_Whitening,
Ns_all_users_Whitening) \
= self.bd_obj_whitening.block_diagonalize_no_waterfilling(
self.multiuser_channel)
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# Since we will use the same data for the external interference no
# matter which metric is used, lets create that data here.
external_int_data_all_metrics = (np.sqrt(self.pe) * misc.randn_c_RS(
self.ext_data_RS, current_parameters['ext_int_rank'],
current_parameters['NSymbs']))
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxx Run the Simulation and get the results for each metric xxxxxx
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# None metric
(ber_result_None, ser_result_None, per_result_None,
spec_effic_result_None,
sinr_result_None) = self.__simulate_for_one_metric(
Ns_all_users_None, external_int_data_all_metrics,
MsPk_all_users_None, Wk_all_users_None, 'None',
current_parameters)
# naive metric
(ber_result_naive, ser_result_naive, per_result_naive,
spec_effic_result_naive,
sinr_result_naive) = self.__simulate_for_one_metric(
Ns_all_users_naive, external_int_data_all_metrics,
MsPk_all_users_naive, Wk_all_users_naive, 'naive',
current_parameters)
# fixed metric
(ber_result_fixed, ser_result_fixed, per_result_fixed,
spec_effic_result_fixed,
sinr_result_fixed) = self.__simulate_for_one_metric(
Ns_all_users_fixed, external_int_data_all_metrics,
MsPk_all_users_fixed, Wk_all_users_fixed, 'fixed',
current_parameters)
# capacity metric
(ber_result_capacity, ser_result_capacity, per_result_capacity,
spec_effic_result_capacity,
sinr_result_capacity) = self.__simulate_for_one_metric(
Ns_all_users_capacity, external_int_data_all_metrics,
MsPk_all_users_capacity, Wk_all_users_capacity, 'capacity',
current_parameters)
# effective throughput metric
(ber_result_effec_throughput, ser_result_effec_throughput,
per_result_effec_throughput, spec_effic_result_effec_throughput,
sinr_result_effec_throughput) = self.__simulate_for_one_metric(
Ns_all_users_effec_throughput, external_int_data_all_metrics,
MsPk_all_users_effec_throughput, Wk_all_users_effec_throughput,
'effec_throughput', current_parameters)
# Whitening BD
(ber_result_Whitening, ser_result_Whitening, per_result_Whitening,
spec_effic_result_Whitening,
sinr_result_Whitening) = self.__simulate_for_one_metric(
Ns_all_users_Whitening, external_int_data_all_metrics,
Ms_all_users_Whitening, Wk_all_users_Whitening, 'Whitening',
current_parameters)
simResults = SimulationResults()
# Add the 'None' results
simResults.add_result(ber_result_None)
simResults.add_result(ser_result_None)
simResults.add_result(per_result_None)
simResults.add_result(spec_effic_result_None)
simResults.add_result(sinr_result_None)
# Add the naive results
simResults.add_result(ber_result_naive)
simResults.add_result(ser_result_naive)
simResults.add_result(per_result_naive)
simResults.add_result(spec_effic_result_naive)
simResults.add_result(sinr_result_naive)
# Add the fixed results
simResults.add_result(ber_result_fixed)
simResults.add_result(ser_result_fixed)
simResults.add_result(per_result_fixed)
simResults.add_result(spec_effic_result_fixed)
simResults.add_result(sinr_result_fixed)
# Add the capacity results
simResults.add_result(ber_result_capacity)
simResults.add_result(ser_result_capacity)
simResults.add_result(per_result_capacity)
simResults.add_result(spec_effic_result_capacity)
simResults.add_result(sinr_result_capacity)
# Add the effective throughput results
simResults.add_result(ber_result_effec_throughput)
simResults.add_result(ser_result_effec_throughput)
simResults.add_result(per_result_effec_throughput)
simResults.add_result(spec_effic_result_effec_throughput)
simResults.add_result(sinr_result_effec_throughput)
# Add the 'Whitening' results
simResults.add_result(ber_result_Whitening)
simResults.add_result(ser_result_Whitening)
simResults.add_result(per_result_Whitening)
simResults.add_result(spec_effic_result_Whitening)
simResults.add_result(sinr_result_Whitening)
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
return simResults
def _run_simulation(self, current_parameters): # pylint: disable=R0914
"""The _run_simulation method is where the actual code to simulate
the system is.
The implementation of this method is required by every subclass of
SimulationRunner.
Parameters
----------
current_parameters : SimulationParameters object
SimulationParameters object with the parameters for the
simulation. The self.params variable is not used directly. It
is first unpacked (in the SimulationRunner.simulate method
which then calls _run_simulation) for each combination.
"""
# xxxxxxxxxx Prepare the scenario for this iteration. xxxxxxxxxxxxx
# This will place the users at the locations specified by the
# scenario (random locations or not), calculate the path loss and
# generate a new random channel (in the self.multiuser_channel
# variable).
self._create_users_according_to_scenario(current_parameters)
# This will calculate pathloss and generate random channels from
# all transmitters to all receivers as well as from the external
# interference sources to all receivers. This method must be called
# after the _create_users_according_to_scenario method so that the
# users are already created (we need their positions for the
# pathloss)
self._create_users_channels(current_parameters)
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxxxx Perform the block diagonalization for each metric xxxxxxxxx
# None Metric
(MsPk_all_users_None,
Wk_all_users_None,
Ns_all_users_None) \
= self.bd_obj_None.block_diagonalize_no_waterfilling(
self.multiuser_channel)
# Naive Metric
(MsPk_all_users_naive,
Wk_all_users_naive,
Ns_all_users_naive) \
= self.bd_obj_naive.block_diagonalize_no_waterfilling(
self.multiuser_channel)
# Fixed Metric
(MsPk_all_users_fixed,
Wk_all_users_fixed,
Ns_all_users_fixed) \
= self.bd_obj_fixed.block_diagonalize_no_waterfilling(
self.multiuser_channel)
# Capacity Metric
(MsPk_all_users_capacity,
Wk_all_users_capacity,
Ns_all_users_capacity) \
= self.bd_obj_capacity.block_diagonalize_no_waterfilling(
self.multiuser_channel)
# effective_throughput Metric
(MsPk_all_users_effec_throughput,
Wk_all_users_effec_throughput,
Ns_all_users_effec_throughput) \
= self.bd_obj_effec_throughput.block_diagonalize_no_waterfilling(
self.multiuser_channel)
(Ms_all_users_Whitening,
Wk_all_users_Whitening,
Ns_all_users_Whitening) \
= self.bd_obj_whitening.block_diagonalize_no_waterfilling(
self.multiuser_channel)
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# Since we will use the same data for the external interference no
# matter which metric is used, lets create that data here.
external_int_data_all_metrics = (
np.sqrt(self.pe) *
misc.randn_c_RS(
self.ext_data_RS,
current_parameters['ext_int_rank'],
current_parameters['NSymbs']))
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# xxx Run the Simulation and get the results for each metric xxxxxx
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# None metric
(ber_result_None,
ser_result_None,
per_result_None,
spec_effic_result_None,
sinr_result_None) = self.__simulate_for_one_metric(
Ns_all_users_None,
external_int_data_all_metrics,
MsPk_all_users_None,
Wk_all_users_None,
'None',
current_parameters)
# naive metric
(ber_result_naive,
ser_result_naive,
per_result_naive,
spec_effic_result_naive,
sinr_result_naive) = self.__simulate_for_one_metric(
Ns_all_users_naive,
external_int_data_all_metrics,
MsPk_all_users_naive,
Wk_all_users_naive,
'naive',
current_parameters)
# fixed metric
(ber_result_fixed,
ser_result_fixed,
per_result_fixed,
spec_effic_result_fixed,
sinr_result_fixed) = self.__simulate_for_one_metric(
Ns_all_users_fixed,
external_int_data_all_metrics,
MsPk_all_users_fixed,
Wk_all_users_fixed,
'fixed',
current_parameters)
# capacity metric
(ber_result_capacity,
ser_result_capacity,
per_result_capacity,
spec_effic_result_capacity,
sinr_result_capacity) = self.__simulate_for_one_metric(
Ns_all_users_capacity,
external_int_data_all_metrics,
MsPk_all_users_capacity,
Wk_all_users_capacity,
'capacity',
current_parameters)
# effective throughput metric
(ber_result_effec_throughput,
ser_result_effec_throughput,
per_result_effec_throughput,
spec_effic_result_effec_throughput,
sinr_result_effec_throughput) = self.__simulate_for_one_metric(
Ns_all_users_effec_throughput,
external_int_data_all_metrics,
MsPk_all_users_effec_throughput,
Wk_all_users_effec_throughput,
'effec_throughput',
current_parameters)
# Whitening BD
(ber_result_Whitening,
ser_result_Whitening,
per_result_Whitening,
spec_effic_result_Whitening,
sinr_result_Whitening) = self.__simulate_for_one_metric(
Ns_all_users_Whitening,
external_int_data_all_metrics,
Ms_all_users_Whitening,
Wk_all_users_Whitening,
'Whitening',
current_parameters)
simResults = SimulationResults()
# Add the 'None' results
simResults.add_result(ber_result_None)
simResults.add_result(ser_result_None)
simResults.add_result(per_result_None)
simResults.add_result(spec_effic_result_None)
simResults.add_result(sinr_result_None)
# Add the naive results
simResults.add_result(ber_result_naive)
simResults.add_result(ser_result_naive)
simResults.add_result(per_result_naive)
simResults.add_result(spec_effic_result_naive)
simResults.add_result(sinr_result_naive)
# Add the fixed results
simResults.add_result(ber_result_fixed)
simResults.add_result(ser_result_fixed)
simResults.add_result(per_result_fixed)
simResults.add_result(spec_effic_result_fixed)
simResults.add_result(sinr_result_fixed)
# Add the capacity results
simResults.add_result(ber_result_capacity)
simResults.add_result(ser_result_capacity)
simResults.add_result(per_result_capacity)
simResults.add_result(spec_effic_result_capacity)
simResults.add_result(sinr_result_capacity)
# Add the effective thoughput results
simResults.add_result(ber_result_effec_throughput)
simResults.add_result(ser_result_effec_throughput)
simResults.add_result(per_result_effec_throughput)
simResults.add_result(spec_effic_result_effec_throughput)
simResults.add_result(sinr_result_effec_throughput)
# Add the 'Whitening' results
simResults.add_result(ber_result_Whitening)
simResults.add_result(ser_result_Whitening)
simResults.add_result(per_result_Whitening)
simResults.add_result(spec_effic_result_Whitening)
simResults.add_result(sinr_result_Whitening)
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
return simResults