def test_002_t (self):
vlen=16
skip=4
dc_null=2
ofdm_sym=2
expected_snr = ((1-0.01)/(0.01), (1-0.01)/(0.01))
expected_noise = (0.01, 0.01)
# define the blocks
src_data = [0.1]*(skip/2) + [skip] + [0.1]*(skip/2-1)
src_data = src_data * (vlen/skip)
src_data = [0]*(dc_null/2) + src_data[:(vlen/2-dc_null/2)] + src_data[(vlen/2+dc_null/2):] + [0]*(dc_null/2)
src_data = src_data * (ofdm_sym)
src = blocks.vector_source_c(src_data,False,vlen)
snr_estim = ofdm.snr_estimator_dc_null(vlen,skip,dc_null)
dst = blocks.vector_sink_f()
dst_noise = blocks.vector_sink_f()
# construct the flowgraph
self.tb.connect(src,snr_estim)
self.tb.connect((snr_estim,0),dst)
self.tb.connect((snr_estim,1),dst_noise)
# set up fg
self.tb.run ()
print "snr", dst.data()
print "noise", dst_noise.data()
# Compare with reference data from above
self.assertFloatTuplesAlmostEqual(dst.data(),expected_snr)
self.assertFloatTuplesAlmostEqual(dst_noise.data(),expected_noise)
def test_002_t(self):
vlen = 16
skip = 4
dc_null = 2
ofdm_sym = 2
expected_snr = ((1 - 0.01) / (0.01), (1 - 0.01) / (0.01))
expected_noise = (0.01, 0.01)
# define the blocks
src_data = [0.1] * (skip / 2) + [skip] + [0.1] * (skip / 2 - 1)
src_data = src_data * (vlen / skip)
src_data = [0] * (dc_null / 2) + src_data[:(
vlen / 2 - dc_null / 2)] + src_data[
(vlen / 2 + dc_null / 2):] + [0] * (dc_null / 2)
src_data = src_data * (ofdm_sym)
src = blocks.vector_source_c(src_data, False, vlen)
snr_estim = ofdm.snr_estimator_dc_null(vlen, skip, dc_null)
dst = blocks.vector_sink_f()
dst_noise = blocks.vector_sink_f()
# construct the flowgraph
self.tb.connect(src, snr_estim)
self.tb.connect((snr_estim, 0), dst)
self.tb.connect((snr_estim, 1), dst_noise)
# set up fg
self.tb.run()
print "snr", dst.data()
print "noise", dst_noise.data()
# Compare with reference data from above
self.assertFloatTuplesAlmostEqual(dst.data(), expected_snr)
self.assertFloatTuplesAlmostEqual(dst_noise.data(), expected_noise)
def test_001_t(self):
d_vlen = 4
d_skip = 2
d_dc_null = 2
sum_load = 0
sum_null = 0
#noise
noise = np.random.normal(0, 1, 1000)
src_data = [2, 1] * 10
# src_data=[sum(x) for x in zip(data,noise)]
src = blocks.vector_source_c(src_data, vlen=4)
snr = ofdm.snr_estimator(d_vlen, d_skip)
snr_dc_null = ofdm.snr_estimator_dc_null(d_vlen, d_skip, d_dc_null)
dst = blocks.vector_sink_f()
dst_noise = blocks.vector_sink_f()
dst_dc_null = blocks.vector_sink_f()
dst_dc_null_noise = blocks.vector_sink_f()
#construct the flowgraph
self.tb.connect(src, snr_dc_null)
self.tb.connect(src, snr)
self.tb.connect((snr_dc_null, 0), dst_dc_null)
self.tb.connect((snr_dc_null, 1), dst_dc_null_noise)
self.tb.connect((snr, 0), dst)
self.tb.connect((snr, 1), dst_noise)
# set up fg
self.tb.run()
print "output", dst.data()
print "output dc_null", dst_dc_null.data()
# calculate reference data
dc_null_noise = dst_dc_null.data()
if not d_dc_null:
for index, number in enumerate(src_data):
square = number * number.conjugate()
if not index % d_skip:
sum_load += square
else:
sum_null += square
estim = (1. / d_skip) * ((d_skip - 1) * sum_load / sum_null - 1)
estim_noise = sum_null * d_skip / (d_skip - 1) / d_vlen
else:
results = [0] * 80
estim_noise = results
estim = results
#Compare with snr_estimator_block
self.assertEqual(dst_dc_null.data(), dst.data())
self.assertEqual(dst_dc_null_noise.data(), dst_noise.data())
def test_001_t (self):
d_vlen=4
d_skip=2
d_dc_null=2
sum_load=0
sum_null=0
#noise
noise=np.random.normal(0,1,1000)
src_data=[2,1]*10
# src_data=[sum(x) for x in zip(data,noise)]
src=blocks.vector_source_c(src_data,vlen=4)
snr=ofdm.snr_estimator(d_vlen,d_skip)
snr_dc_null=ofdm.snr_estimator_dc_null(d_vlen,d_skip,d_dc_null)
dst=blocks.vector_sink_f()
dst_noise=blocks.vector_sink_f()
dst_dc_null=blocks.vector_sink_f()
dst_dc_null_noise=blocks.vector_sink_f()
#construct the flowgraph
self.tb.connect(src,snr_dc_null)
self.tb.connect(src,snr)
self.tb.connect((snr_dc_null,0),dst_dc_null)
self.tb.connect((snr_dc_null,1),dst_dc_null_noise)
self.tb.connect((snr,0),dst)
self.tb.connect((snr,1),dst_noise)
# set up fg
self.tb.run ()
print "output", dst.data()
print "output dc_null", dst_dc_null.data()
# calculate reference data
dc_null_noise=dst_dc_null.data()
if not d_dc_null:
for index,number in enumerate(src_data):
square=number*number.conjugate()
if not index%d_skip:
sum_load+=square
else:
sum_null+=square
estim =(1./d_skip)*((d_skip-1)*sum_load/sum_null-1)
estim_noise = sum_null*d_skip/(d_skip-1)/d_vlen
else:
results=[0]*80
estim_noise=results
estim=results
#Compare with snr_estimator_block
self.assertEqual(dst_dc_null.data(),dst.data())
self.assertEqual(dst_dc_null_noise.data(),dst_noise.data())
def setup_snr_measurement_2(self):
"""
Perform SNR measurement.
It uses the data reference from the BER measurement. I.e. if that is not
setup, it will be setup. Only data subcarriers that are assigned to the
station are considered in the measurement. Note that there is no sink
prepared. You need to setup a sink, e.g. with one or more invocation
of a "publish.."-function.
SNR output is in dB.
"""
if not self.measuring_ber():
self.setup_ber_measurement()
print "Warning: Setup BER Measurement forced"
if self.measuring_snr():
return
config = station_configuration()
vlen = config.subcarriers
frame_length = config.frame_length
L = config.periodic_parts
snr_est_filt_2_1 = skip(gr.sizeof_gr_complex*vlen,frame_length)
for x in range(1,frame_length):
snr_est_filt_2_1.skip_call(x)
# log_to_file(self, snr_est_filt_2_1, "data/snr_est_filt_2_1.float")
## NOTE HACK!! first preamble is not equalized
self.connect(self.symbol_output_2,snr_est_filt_2_1)
self.connect(self.frame_trigger_2,(snr_est_filt_2_1,1))
# snrm = self._snr_measurement = milans_snr_estimator( vlen, vlen, L )
#
# self.connect(snr_est_filt,snrm)
#
# if self._options.log:
# log_to_file(self, self._snr_measurement, "data/milan_snr.float")
#Addition for SINR estimation
if self._options.sinr_est:
snr_est_filt_2_2 = skip(gr.sizeof_gr_complex*vlen,frame_length)
for x in range(frame_length):
if x != config.training_data.channel_estimation_pilot[0]:
snr_est_filt_2_2.skip_call(x)
self.connect(self.symbol_output,snr_est_filt_2_2)
self.connect(self.frame_trigger,(snr_est_filt_2_2,1))
sinrm_2 = self._sinr_measurement_2 = milans_sinr_sc_estimator2( vlen, vlen, L )
self.connect(snr_est_filt_2_1,sinrm_2)
self.connect(snr_est_filt_2_2,(sinrm_2,1))
if self._options.log:
log_to_file(self, (self._sinr_measurement_2,0), "data/milan_sinr_sc.float")
log_to_file(self, (self._sinr_measurement_2,1), "data/milan_snr.float")
else:
#snrm = self._snr_measurement = milans_snr_estimator( vlen, vlen, L )
snr_estim_2 = snr_estimator_dc_null(vlen, L, config.dc_null)
scsnrdb_2 = filter.single_pole_iir_filter_ff(0.1)
snrm_2 = self._snr_measurement_2 = blocks.nlog10_ff(10,1,0)
self.connect(snr_est_filt_2_1,snr_estim_2,scsnrdb_2,snrm_2)
self.connect((snr_estim_2,1),blocks.null_sink(gr.sizeof_float))
#log_to_file(self, snrm_2, "data/snrm_2.float")
if self._options.log:
log_to_file(self, self._snr_measurement_2, "data/milan_snr_2.float")
def setup_snr_measurement(self):
"""
Perform SNR measurement.
It uses the data reference from the BER measurement. I.e. if that is not
setup, it will be setup. Only data subcarriers that are assigned to the
station are considered in the measurement. Note that there is no sink
prepared. You need to setup a sink, e.g. with one or more invocation
of a "publish.."-function.
SNR output is in dB.
"""
if not self.measuring_ber():
self.setup_ber_measurement()
print "Warning: Setup BER Measurement forced"
if self.measuring_snr():
return
config = station_configuration()
vlen = config.subcarriers
frame_length = config.frame_length
L = config.periodic_parts
snr_est_filt = skip(gr.sizeof_gr_complex*vlen,frame_length)
for x in range(1,frame_length):
snr_est_filt.skip_call(x)
## NOTE HACK!! first preamble is not equalized
self.connect(self.symbol_output,snr_est_filt)
self.connect(self.frame_trigger,(snr_est_filt,1))
# snrm = self._snr_measurement = milans_snr_estimator( vlen, vlen, L )
#
# self.connect(snr_est_filt,snrm)
#
# if self._options.log:
# log_to_file(self, self._snr_measurement, "data/milan_snr.float")
#Addition for SINR estimation
if self._options.sinr_est:
snr_est_filt_2 = skip(gr.sizeof_gr_complex*vlen,frame_length)
for x in range(frame_length):
if x != config.training_data.channel_estimation_pilot[0]:
snr_est_filt_2.skip_call(x)
self.connect(self.symbol_output,snr_est_filt_2)
self.connect(self.frame_trigger,(snr_est_filt_2,1))
sinrm = self._sinr_measurement = milans_sinr_sc_estimator2( vlen, vlen, L )
self.connect(snr_est_filt,sinrm)
self.connect(snr_est_filt_2,(sinrm,1))
self.feedback_sink = ofdm.feedback_sink_vf(config.data_subcarriers,"tcp://*:3322")
self.connect(self.id_dec, self.feedback_sink)
#self.connect((sinrm,1),blocks.null_sink(gr.sizeof_float))
self.connect((sinrm,0),pilot_subcarrier_filter(False), (self.feedback_sink,1))
if self._options.log:
log_to_file(self, (self._sinr_measurement,0), "data/milan_sinr_sc.float")
log_to_file(self, (self._sinr_measurement,1), "data/milan_snr.float")
else:
#snrm = self._snr_measurement = milans_snr_estimator( vlen, vlen, L )
snr_estim = snr_estimator_dc_null(vlen, L, config.dc_null)
scsnrdb = filter.single_pole_iir_filter_ff(0.1)
snrm = self._snr_measurement = blocks.nlog10_ff(10,1,0)
self.connect(snr_est_filt,snr_estim,scsnrdb,snrm)
self.connect((snr_estim,1),blocks.null_sink(gr.sizeof_float))
#log_to_file(self, snrm, "data/snrm.float")
if self._options.log:
log_to_file(self, self._snr_measurement, "data/milan_snr.float")
