def try_mimo_setting(df, sig, set, mset, trainingSyms, mimodir):
N = set['N']
nmodes = set['nmodes']
n_training_syms = mset['n_training_syms']
lb = mset['lb']
mu = mset['mu']
nloops = mset['nloops']
ovsmpl = set['ovsmpl']
t_conv = N - 50000
t_stop = N - 1000
if mset['errorcalc'] == 'lms':
print('LMS Enabled')
unit = np.sqrt(2) * 0.5
constellation = [
unit + 1j * unit, unit - 1j * unit, -unit + 1j * unit,
-unit - 1j * unit
]
errorcalc = TrainedLMS(trainingSyms[:, :n_training_syms],
constellation, lb)
sig_with_loops = errorcalc.AddTrainingLoops(sig, ovsmpl, nloops)
sig_Martin = sig_with_loops.copy()[:, :N + n_training_syms * nloops]
mimo = FrequencyDomainBlockwizeMimo(nmodes, lb, ovsmpl, mu, errorcalc)
sig_Martin[:, :], taps_Martin = mimo.equalize_signal(sig_with_loops)
else:
errorcalc = CMAErrorCalculator()
sig_Martin = sig_with_loops.copy()[:, :N]
mimo = FrequencyDomainBlockwizeMimo(nmodes, lb, ovsmpl, mu, errorcalc)
sig_Martin[:, :], taps_Martin = mimo.equalize_signal(sig)
print(sig_Martin.shape)
#sig_Martin ,ph = phaserec.viterbiviterbi(sig_Martin, 11)
err_Martin = []
for i_mode in range(nmodes):
err_Martin_ = calculate_radius_directed_error(
sig_Martin[i_mode, 0:t_stop], 1)
err_Martin.append(mlab.movavg(abs(err_Martin_), movavg_taps))
try:
ber_martin = calculate_BER(sig_Martin, range(t_conv, t_stop))
except:
ber_martin = np.ones(nmodes)
title = "Martin_mu {mu}_lb{lb}"
plot_constellation(sig_Martin[:, t_conv:t_stop], title, True, mimodir)
plot_error(err_Martin, title, True, mimodir)
plot_taps(taps_Martin, True, mimodir)
final_error_Martin = calculate_final_error(err_Martin, t_conv, t_stop)
t_conv_martin = calculate_convergence(err_Martin, final_error_Martin)
df_martin = UpdateDataFrame(set, mset, ber_martin, t_conv_martin,
final_error_Martin, "Martin")
return df.append(df_martin)
else:
errorcalc = CMAErrorCalculator()
mimo = FrequencyDomainBlockwizeMimo(set['nmodes'], mset['lb'], ovsmpl,
mset['mu'], errorcalc)
sig_Martin[:, :], taps_Martin = mimo.equalize_signal(sig)
sig_Martin, ph = phaserec.viterbiviterbi(sig_Martin, 11)
err_Martin = []
for i_mode in range(set['nmodes']):
err_Martin_ = calculate_radius_directed_error(
sig_Martin[i_mode, 0:t_stop], 1)
err_Martin.append(mlab.movavg(abs(err_Martin_), movavg_taps))
try:
ber_martin = calculate_BER(sig_Martin, range(t_conv, t_stop))
except:
ber_martin = np.ones(nmodes)
title = "Martin_mu" + str(mset['mu']) + "_lb" + str(mset['lb'])
plot_constellation(sig_Martin[:, t_conv:t_stop], title, True, mimodir)
plot_error(err_Martin, title, True, mimodir)
plot_taps(taps_Martin[:, :, 0, :], True, mimodir)
final_error_Martin = calculate_final_error(err_Martin, t_conv, t_stop)
t_conv_martin = calculate_convergence(err_Martin, final_error_Martin)
df_martin = UpdateDataFrame(set, mset, ber_martin, t_conv_martin,
final_error_Martin, "Martin")
df = df.append(df_martin)
#df = df.sort_values(['mode','pmd','snr','ber'],ascending=[0,0,1,1])
print(df)
df.to_csv(testDir + "\Results.csv", sep=',')