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!")
# print "\nMax SINR"
(max_sinr_SINRs[rep], max_sinr_capacity[rep],
max_sinr_sum_capacity[rep]) = calc_SINRs_and_capacity(max_sinr_solver)
# print "SINRs:\n{0}".format(max_sinr_SINRs[rep])
# print "Capacity:\n{0}".format(max_sinr_capacity[rep])
# print "Sum_Capacity: {0}".format(max_sinr_sum_capacity[rep])
# print "\nMMSE"
(mmse_SINRs[rep], mmse_capacity[rep],
mmse_sum_capacity[rep]) = calc_SINRs_and_capacity(mmse_solver)
# print "SINRs:\n{0}".format(mmse_SINRs[rep])
# print "Capacity:\n{0}".format(mmse_capacity[rep])
# print "Sum_Capacity: {0}".format(mmse_sum_capacity[rep])
pbar.progress(rep)
df = DataFrame({
'Min. Leakage': alt_min_sum_capacity,
'Max SINR': max_sinr_sum_capacity,
'MMSE': mmse_sum_capacity
})
df.to_csv(
'sum_capacity_{Nr}x{Nt}_{Ns}_SNR_{SNR}_{initialize_with}_init.txt'.
format(Nr=Nr, Ns=Ns, Nt=Nt, SNR=SNR, initialize_with=initialize_with),
index_label="Index")
plt.plot([sum(alt_min_capacity[a]) for a in range(50)])
plt.plot([sum(max_sinr_capacity[a]) for a in range(50)])
plt.plot([sum(mmse_capacity[a]) for a in range(50)])
plt.legend(["Min Leakage", "Max SINR", "MMSE"])
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!")
(max_sinr_SINRs[rep],
max_sinr_capacity[rep],
max_sinr_sum_capacity[rep]) = calc_SINRs_and_capacity(max_sinr_solver)
# print "SINRs:\n{0}".format(max_sinr_SINRs[rep])
# print "Capacity:\n{0}".format(max_sinr_capacity[rep])
# print "Sum_Capacity: {0}".format(max_sinr_sum_capacity[rep])
# print "\nMMSE"
(mmse_SINRs[rep],
mmse_capacity[rep],
mmse_sum_capacity[rep]) = calc_SINRs_and_capacity(mmse_solver)
# print "SINRs:\n{0}".format(mmse_SINRs[rep])
# print "Capacity:\n{0}".format(mmse_capacity[rep])
# print "Sum_Capacity: {0}".format(mmse_sum_capacity[rep])
pbar.progress(rep)
df = DataFrame({'Min. Leakage':alt_min_sum_capacity,
'Max SINR': max_sinr_sum_capacity,
'MMSE': mmse_sum_capacity})
df.to_csv(
'sum_capacity_{Nr}x{Nt}_{Ns}_SNR_{SNR}_{initialize_with}_init.txt'.format(
Nr=Nr, Ns=Ns, Nt=Nt, SNR=SNR, initialize_with=initialize_with),
index_label="Index")
plt.plot([sum(alt_min_capacity[a]) for a in range(50)])
plt.plot([sum(max_sinr_capacity[a]) for a in range(50)])
plt.plot([sum(mmse_capacity[a]) for a in range(50)])
plt.legend(["Min Leakage", "Max SINR", "MMSE"])