Nt = np.ones(K) * 4
Ns = np.array([2, 2, 2]) # np.ones(K) * 2
multiuserchannel = MultiUserChannelMatrix()
modulator = PSK(4)
SNR = 40
noise_var = 1 / dB2Linear(SNR)
print("SNR: {0}".format(SNR))
print("noise_var: {0}".format(noise_var))
multiuserchannel.randomize(Nr, Nt, K)
multiuserchannel.noise_var = noise_var
ia_solver = algorithms.AlternatingMinIASolver(multiuserchannel)
ia_solver2 = algorithms.MMSEIASolver(multiuserchannel)
ia_solver3 = algorithms.MaxSinrIASolver(multiuserchannel)
ia_solver4 = algorithms.AlternatingMinIASolver(multiuserchannel)
# ia_solver.initialize_with_closed_form = True
# ia_solver2.initialize_with_closed_form = True
# ia_solver3.initialize_with_closed_form = True
ia_solver.randomizeF(Ns)
ia_solver.max_iterations = 400
ia_solver.solve(Ns)
ia_solver2.randomizeF(Ns)
ia_solver2.max_iterations = 100
ia_solver2.solve(Ns)
def main():
"""Main function.
"""
K = 3
Nr = 4
Nt = 4
Ns = 2
SNR = 30.0
P = 1.0
# Dependent parameters
noise_var = 1 / dB2Linear(SNR)
RepMax = 1
mmse_sinrs = np.empty([RepMax, K, Ns], dtype=float)
max_sinr_sinrs = np.empty([RepMax, K, Ns], dtype=float)
mmse_capacity = np.empty(RepMax, dtype=float)
max_sinr_capacity = np.empty(RepMax, dtype=float)
pbar = ProgressbarText(RepMax, message="Simulating for SNR: {0}".format(SNR))
for rep in range(RepMax):
# Creat the channel
multiUserChannel = pyphysim.channels.multiuser.MultiUserChannelMatrix()
multiUserChannel.randomize(Nr, Nt, K)
multiUserChannel.noise_var = noise_var
# Creat the IA solver object
mmse_ia_solver = algorithms.MMSEIASolver(multiUserChannel)
max_sinr_ia_solver = algorithms.MaxSinrIASolver(multiUserChannel)
mmse_ia_solver.randomizeF(Ns, P)
mmse_ia_solver.initialize_with = 'fix'
max_sinr_ia_solver.initialize_with = 'fix'
# noinspection PyProtectedMember
max_sinr_ia_solver._F = mmse_ia_solver._F
#mmse_ia_solver.initialize_with = 'fix'
# We wouldn't need to explicitly set ia_solver.noise_var
# variable if the multiUserChannel object had the correct value at
# this point.
# mmse_ia_solver.noise_var = noise_var
mmse_ia_solver.max_iterations = 200
mmse_ia_solver.solve(Ns)
# max_sinr_ia_solver.noise_var = noise_var
max_sinr_ia_solver.max_iterations = 200
max_sinr_ia_solver.solve(Ns)
mmse_sinrs[rep] = list(map(linear2dB, mmse_ia_solver.calc_SINR()))
max_sinr_sinrs[rep] = list(map(linear2dB, max_sinr_ia_solver.calc_SINR()))
mmse_capacity[rep] = np.sum(calc_capacity(mmse_ia_solver.calc_SINR()))
max_sinr_capacity[rep] = np.sum(calc_capacity(max_sinr_ia_solver.calc_SINR()))
# print "MMSE Alt. SINRs:\n{0}".format(np.vstack(mmse_sinrs[rep]))
# print "Max SINR Alg. SINRs:\n{0}".format(np.vstack(max_sinr_sinrs[rep]))
# print "MMSE Alt. Capacity: {0}".format(np.sum(calc_capacity(mmse_sinrs[rep])))
# print "Max SINR Alg. Capacity: {0}".format(np.sum(calc_capacity(max_sinr_sinrs[rep])))
# print
pbar.progress(rep)
print("MMSE Average SINRs:\n{0}".format(mmse_sinrs.mean(0)))
print("Max SINR Average SINRs:\n{0}".format(max_sinr_sinrs.mean(0)))
print("MMSE Average Capacity: {0}".format(mmse_capacity.mean()))
print("Max SINR Average Capacity: {0}".format(max_sinr_capacity.mean()))
print("\nEnd!")
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
