import csv
try:
import pylab
except ImportError:
print "Error: could not import pylab (http://matplotlib.sourceforge.net/)"
sys.exit(1)
# Number of samples to use
N = 10000
# After this many samples, a tag containing the SNR (key='snr') will be sent
ntag = 10000
# The running-average coefficient
alpha = 0.001
# BPSK
bits = 2 * scipy.complex64(scipy.random.randint(0, 2, N)) - 1
general_snr = list()
snr_svr = list()
snr_m2m4 = list()
snr_skew = list()
snr_simple = list()
snr_real_value = list()
def noise_voltage(SNR):
""" Calculate noise voltage from SNR assuming unit signal power
:param SNR: signal to noise ratio
"""
return 10 ** (float(-SNR) / 20)
import csv
try:
import pylab
except ImportError:
print "Error: could not import pylab (http://matplotlib.sourceforge.net/)"
sys.exit(1)
# Number of samples to use
N = 10000
# After this many samples, a tag containing the SNR (key='snr') will be sent
ntag = 10000
# The running-average coefficient
alpha = 0.001
# QPSK
bits = (2*scipy.complex64(scipy.random.randint(0, 2, N)) - 1) + \
1j*(2*scipy.complex64(scipy.random.randint(0, 2, N)) - 1)
general_snr = list()
snr_svr = list()
snr_m2m4 = list()
snr_skew = list()
snr_simple = list()
snr_real_value = list()
def noise_voltage(SNR):
""" Calculate noise voltage from SNR assuming unit signal power
:param SNR: signal to noise ratio
"""
return 10 ** (float(-SNR) / 20)
def main():
gr_estimators = {"simple": digital.SNR_EST_SIMPLE,
"skew": digital.SNR_EST_SKEW,
"m2m4": digital.SNR_EST_M2M4,
"svr": digital.SNR_EST_SVR}
py_estimators = {"simple": snr_est_simple,
"skew": snr_est_skew,
"m2m4": snr_est_m2m4,
"svr": snr_est_svr}
parser = OptionParser(option_class=eng_option, conflict_handler="resolve")
parser.add_option("-N", "--nsamples", type="int", default=10000,
help="Set the number of samples to process [default=%default]")
parser.add_option("", "--snr-min", type="float", default=-5,
help="Minimum SNR [default=%default]")
parser.add_option("", "--snr-max", type="float", default=20,
help="Maximum SNR [default=%default]")
parser.add_option("", "--snr-step", type="float", default=0.5,
help="SNR step amount [default=%default]")
parser.add_option("-t", "--type", type="choice",
choices=gr_estimators.keys(), default="simple",
help="Estimator type {0} [default=%default]".format(
gr_estimators.keys()))
(options, args) = parser.parse_args ()
N = options.nsamples
xx = scipy.random.randn(N)
xy = scipy.random.randn(N)
bits = 2*scipy.complex64(scipy.random.randint(0, 2, N)) - 1
snr_known = list()
snr_python = list()
snr_gr = list()
# when to issue an SNR tag; can be ignored in this example.
ntag = 10000
n_cpx = xx + 1j*xy
py_est = py_estimators[options.type]
gr_est = gr_estimators[options.type]
SNR_min = options.snr_min
SNR_max = options.snr_max
SNR_step = options.snr_step
SNR_dB = scipy.arange(SNR_min, SNR_max+SNR_step, SNR_step)
for snr in SNR_dB:
SNR = 10.0**(snr/10.0)
scale = scipy.sqrt(SNR)
yy = bits + n_cpx/scale
print "SNR: ", snr
Sknown = scipy.mean(yy**2)
Nknown = scipy.var(n_cpx/scale)/2
snr0 = Sknown/Nknown
snr0dB = 10.0*scipy.log10(snr0)
snr_known.append(snr0dB)
snrdB, snr = py_est(yy)
snr_python.append(snrdB)
gr_src = gr.vector_source_c(bits.tolist(), False)
gr_snr = digital.mpsk_snr_est_cc(gr_est, ntag, 0.001)
gr_chn = gr.channel_model(1.0/scale)
gr_snk = gr.null_sink(gr.sizeof_gr_complex)
tb = gr.top_block()
tb.connect(gr_src, gr_chn, gr_snr, gr_snk)
tb.run()
snr_gr.append(gr_snr.snr())
f1 = pylab.figure(1)
s1 = f1.add_subplot(1,1,1)
s1.plot(SNR_dB, snr_known, "k-o", linewidth=2, label="Known")
s1.plot(SNR_dB, snr_python, "b-o", linewidth=2, label="Python")
s1.plot(SNR_dB, snr_gr, "g-o", linewidth=2, label="GNU Radio")
s1.grid(True)
s1.set_title('SNR Estimators')
s1.set_xlabel('SNR (dB)')
s1.set_ylabel('Estimated SNR')
s1.legend()
pylab.show()
import csv
try:
import pylab
except ImportError:
print "Error: could not import pylab (http://matplotlib.sourceforge.net/)"
sys.exit(1)
# Number of samples to use
N = 10000
# After this many samples, a tag containing the SNR (key='snr') will be sent
ntag = 10000
# The running-average coefficient
alpha = 0.001
# QPSK
bits = (2*scipy.complex64(scipy.random.randint(0, 2, N)) - 1) + \
1j*(2*scipy.complex64(scipy.random.randint(0, 2, N)) - 1)
general_snr = list()
snr_svr = list()
snr_m2m4 = list()
snr_skew = list()
snr_simple = list()
snr_real_value = list()
def noise_voltage(SNR):
""" Calculate noise voltage from SNR assuming unit signal power
:param SNR: signal to noise ratio
"""
return 10**(float(-SNR) / 20)
