def build_and_impair_signal(set, sigdir):
nmodes = set['nmodes']
N = set['N']
t_conv = N - 50000
t_stop = N - 1000
sig = signals.SignalQAMGrayCoded(4, N, fb=25e9, nmodes=nmodes)
sig_Martin = sig.copy()
trainingSyms = sig.copy()
ovsmpl = set['ovsmpl']
if ovsmpl > 1:
sig = sig.resample(ovsmpl * sig.fb, beta=0.1, renormalise=True)
if set['impulse_impaired']:
sig = apply_impulse_response_impairment(sig)
matrix = build_mixing_matrix(nmodes, set['mixing'], set['loss'])
sig = apply_mltichnl_delayed_matrix_impairment(sig, set['delay'], matrix)
if set['pmd'] > 0:
for i_dmode in range(int(nmodes / 2)):
sig[i_dmode * 2:i_dmode * 2 + 2] = impairments.apply_PMD(
sig[i_dmode * 2:i_dmode * 2 + 2], np.pi / 5.6, set['pmd'])
sig = impairments.change_snr(sig, set['snr'])
sig = impairments.apply_phase_noise(sig, set['phase_noise'])
plot_constellation(sig[:, t_conv:t_stop], "Recieved", True, sigdir)
return sig, sig_Martin, trainingSyms
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=',')
if apply_impulse:
sig = apply_impulse_response_impairment(sig)
if apply_matrix:
if delay_after_matrix:
sig[:, :] = apply_2chnl_delayed_matrix_impairment(
sig, 1e-9, 25e9)
else:
sig[:, :] = apply_2chnl_delayed_after_mix_matrix_impairment(
sig, 1e-9, 25e9)
sig = impairments.apply_PMD(sig, np.pi / 5.6, pmd)
sig = impairments.change_snr(sig, snr)
sig = impairments.apply_phase_noise(sig, phase_noise)
plot_constellation(sig[:, t_conv:t_stop], "Recieved", True, sigdir)
filename = sigdir + "/_rep" + str(r) + ".txt"
file = open(filename, 'wb')
pickle.dump(sig, file)
for mu_q in list_mu_q:
for ntaps in list_ntaps:
mimodir = create_dir_for_mimo_result(
sigdir, mu_q, ntaps, "Qampy")
taps_qampy, err = equalisation.equalise_signal(
sig, mu_q, Ntaps=ntaps, method="cma")
sig_qampy = equalisation.apply_filter(sig, taps_qampy)
sig_qampy, ph = phaserec.viterbiviterbi(sig_qampy, 11)
err_qampy = []
for i_mode in range(2):