def __init__(self, fft_length, pn_weights):
gr.hier_block2.__init__(self, "modified_timing_metric",
gr.io_signature(1,1,gr.sizeof_gr_complex),
gr.io_signature(1,1,gr.sizeof_float))
assert(len(pn_weights) == fft_length)
self.input = gr.kludge_copy(gr.sizeof_gr_complex)
self.connect(self,self.input)
# P(d) = sum(0 to L-1, conj(delayed(r)) * r)
conj = gr.conjugate_cc()
mixer = gr.multiply_cc()
nominator = gr.fir_filter_ccf(1,[pn_weights[fft_length-i-1]*pn_weights[fft_length/2-i-1] for i in range(fft_length/2)])
self.connect(self.input, delay(gr.sizeof_gr_complex,fft_length/2), conj, (mixer,0))
self.connect(self.input, (mixer,1))
self.connect(mixer, nominator)
# moving_avg = P(d)
# R(d)
denominator = schmidl_denominator(fft_length)
# |P(d)| ** 2 / (R(d)) ** 2
p_mag_sqrd = gr.complex_to_mag_squared()
r_sqrd = gr.multiply_ff()
self.timing_metric = gr.divide_ff()
self.connect(nominator, p_mag_sqrd, (self.timing_metric,0))
self.connect(self.input, denominator, (r_sqrd,0))
self.connect(denominator, (r_sqrd,1))
self.connect(r_sqrd, (self.timing_metric,1))
self.connect(self.timing_metric, self)
def __init__(self):
gr.hier_block2.__init__(
self, "s", gr.io_signature(2, 2, gr.sizeof_gr_complex),
gr.io_signature(1, 1, gr.sizeof_gr_complex))
conj = gr.conjugate_cc()
mult = gr.multiply_cc()
self.connect((self, 0), (mult, 0))
self.connect((self, 1), conj, (mult, 1))
self.connect(mult, self)
def __init__(self):
gr.hier_block2.__init__(self, "s",
gr.io_signature(2, 2, gr.sizeof_gr_complex),
gr.io_signature(1, 1, gr.sizeof_gr_complex))
conj = gr.conjugate_cc()
mult = gr.multiply_cc()
self.connect((self,0), (mult,0))
self.connect((self,1), conj, (mult,1))
self.connect(mult, self)
def test_000(self):
src_data = (-2 - 2j, -1 - 1j, -2 + 2j, -1 + 1j, 2 - 2j, 1 - 1j, 2 + 2j,
1 + 1j, 0 + 0j)
exp_data = (-2 + 2j, -1 + 1j, -2 - 2j, -1 - 1j, 2 + 2j, 1 + 1j, 2 - 2j,
1 - 1j, 0 - 0j)
src = gr.vector_source_c(src_data)
op = gr.conjugate_cc()
dst = gr.vector_sink_c()
self.tb.connect(src, op)
self.tb.connect(op, dst)
self.tb.run()
result_data = dst.data()
self.assertEqual(exp_data, result_data)
def __init__(self, vlen):
gr.hier_block2.__init__(
self, "snr_estimator",
gr.io_signature(2, 2, gr.sizeof_gr_complex * vlen),
gr.io_signature(1, 1, gr.sizeof_float))
reference = gr.kludge_copy(gr.sizeof_gr_complex * vlen)
received = gr.kludge_copy(gr.sizeof_gr_complex * vlen)
self.connect((self, 0), reference)
self.connect((self, 1), received)
received_conjugated = gr.conjugate_cc(vlen)
self.connect(received, received_conjugated)
R_innerproduct = gr.multiply_vcc(vlen)
self.connect(reference, R_innerproduct)
self.connect(received_conjugated, (R_innerproduct, 1))
R_sum = vector_sum_vcc(vlen)
self.connect(R_innerproduct, R_sum)
R = gr.complex_to_mag_squared()
self.connect(R_sum, R)
received_magsqrd = gr.complex_to_mag_squared(vlen)
reference_magsqrd = gr.complex_to_mag_squared(vlen)
self.connect(received, received_magsqrd)
self.connect(reference, reference_magsqrd)
received_sum = vector_sum_vff(vlen)
reference_sum = vector_sum_vff(vlen)
self.connect(received_magsqrd, received_sum)
self.connect(reference_magsqrd, reference_sum)
P = gr.multiply_ff()
self.connect(received_sum, (P, 0))
self.connect(reference_sum, (P, 1))
denominator = gr.sub_ff()
self.connect(P, denominator)
self.connect(R, (denominator, 1))
rho_hat = gr.divide_ff()
self.connect(R, rho_hat)
self.connect(denominator, (rho_hat, 1))
self.connect(rho_hat, self)
def __init__(self,vlen):
gr.hier_block2.__init__(self,"snr_estimator",
gr.io_signature(2,2,gr.sizeof_gr_complex*vlen),
gr.io_signature(1,1,gr.sizeof_float))
reference = gr.kludge_copy(gr.sizeof_gr_complex*vlen)
received = gr.kludge_copy(gr.sizeof_gr_complex*vlen)
self.connect((self,0),reference)
self.connect((self,1),received)
received_conjugated = gr.conjugate_cc(vlen)
self.connect(received,received_conjugated)
R_innerproduct = gr.multiply_vcc(vlen)
self.connect(reference,R_innerproduct)
self.connect(received_conjugated,(R_innerproduct,1))
R_sum = vector_sum_vcc(vlen)
self.connect(R_innerproduct,R_sum)
R = gr.complex_to_mag_squared()
self.connect(R_sum,R)
received_magsqrd = gr.complex_to_mag_squared(vlen)
reference_magsqrd = gr.complex_to_mag_squared(vlen)
self.connect(received,received_magsqrd)
self.connect(reference,reference_magsqrd)
received_sum = vector_sum_vff(vlen)
reference_sum = vector_sum_vff(vlen)
self.connect(received_magsqrd,received_sum)
self.connect(reference_magsqrd,reference_sum)
P = gr.multiply_ff()
self.connect(received_sum,(P,0))
self.connect(reference_sum,(P,1))
denominator = gr.sub_ff()
self.connect(P,denominator)
self.connect(R,(denominator,1))
rho_hat = gr.divide_ff()
self.connect(R,rho_hat)
self.connect(denominator,(rho_hat,1))
self.connect(rho_hat,self)
def test_000 (self):
src_data = (-2-2j, -1-1j, -2+2j, -1+1j,
2-2j, 1-1j, 2+2j, 1+1j,
0+0j)
exp_data = (-2+2j, -1+1j, -2-2j, -1-1j,
2+2j, 1+1j, 2-2j, 1-1j,
0-0j)
src = gr.vector_source_c(src_data)
op = gr.conjugate_cc ()
dst = gr.vector_sink_c ()
self.tb.connect(src, op)
self.tb.connect(op, dst)
self.tb.run()
result_data = dst.data ()
self.assertEqual (exp_data, result_data)
def __init__ ( self, fft_length ):
gr.hier_block2.__init__(self, "recursive_timing_metric",
gr.io_signature(1,1,gr.sizeof_gr_complex),
gr.io_signature(1,1,gr.sizeof_float))
self.input = gr.kludge_copy(gr.sizeof_gr_complex)
self.connect(self, self.input)
# P(d) = sum(0 to L-1, conj(delayed(r)) * r)
conj = gr.conjugate_cc()
mixer = gr.multiply_cc()
mix_delay = delay(gr.sizeof_gr_complex,fft_length/2+1)
mix_diff = gr.sub_cc()
nominator = accumulator_cc()
inpdelay = delay(gr.sizeof_gr_complex,fft_length/2)
self.connect(self.input, inpdelay,
conj, (mixer,0))
self.connect(self.input, (mixer,1))
self.connect(mixer,(mix_diff,0))
self.connect(mixer, mix_delay, (mix_diff,1))
self.connect(mix_diff,nominator)
rmagsqrd = gr.complex_to_mag_squared()
rm_delay = delay(gr.sizeof_float,fft_length+1)
rm_diff = gr.sub_ff()
denom = accumulator_ff()
self.connect(self.input,rmagsqrd,rm_diff,gr.multiply_const_ff(0.5),denom)
self.connect(rmagsqrd,rm_delay,(rm_diff,1))
ps = gr.complex_to_mag_squared()
rs = gr.multiply_ff()
self.connect(nominator,ps)
self.connect(denom,rs)
self.connect(denom,(rs,1))
div = gr.divide_ff()
self.connect(ps,div)
self.connect(rs,(div,1))
self.connect(div,self)
def __init__(self, fft_length):
gr.hier_block2.__init__(self, "recursive_timing_metric",
gr.io_signature(1, 1, gr.sizeof_gr_complex),
gr.io_signature(1, 1, gr.sizeof_float))
self.input = gr.kludge_copy(gr.sizeof_gr_complex)
self.connect(self, self.input)
# P(d) = sum(0 to L-1, conj(delayed(r)) * r)
conj = gr.conjugate_cc()
mixer = gr.multiply_cc()
mix_delay = delay(gr.sizeof_gr_complex, fft_length / 2 + 1)
mix_diff = gr.sub_cc()
nominator = accumulator_cc()
inpdelay = delay(gr.sizeof_gr_complex, fft_length / 2)
self.connect(self.input, inpdelay, conj, (mixer, 0))
self.connect(self.input, (mixer, 1))
self.connect(mixer, (mix_diff, 0))
self.connect(mixer, mix_delay, (mix_diff, 1))
self.connect(mix_diff, nominator)
rmagsqrd = gr.complex_to_mag_squared()
rm_delay = delay(gr.sizeof_float, fft_length + 1)
rm_diff = gr.sub_ff()
denom = accumulator_ff()
self.connect(self.input, rmagsqrd, rm_diff, gr.multiply_const_ff(0.5),
denom)
self.connect(rmagsqrd, rm_delay, (rm_diff, 1))
ps = gr.complex_to_mag_squared()
rs = gr.multiply_ff()
self.connect(nominator, ps)
self.connect(denom, rs)
self.connect(denom, (rs, 1))
div = gr.divide_ff()
self.connect(ps, div)
self.connect(rs, (div, 1))
self.connect(div, self)
def __init__(self, fft_length):
gr.hier_block2.__init__(self, "schmidl_nominator",
gr.io_signature(1,1,gr.sizeof_gr_complex),
gr.io_signature(1,1,gr.sizeof_gr_complex))
self.input=gr.kludge_copy(gr.sizeof_gr_complex)
# P(d) = sum(0 to L-1, conj(delayed(r)) * r)
conj = gr.conjugate_cc()
mixer = gr.multiply_cc()
moving_avg = gr.fir_filter_ccf(1,[1.0 for i in range(fft_length/2)])
self.connect(self, self.input, delay(gr.sizeof_gr_complex,fft_length/2), conj, (mixer,0))
self.connect(self.input, (mixer,1))
self.connect(mixer, moving_avg, self)
# moving_avg = P(d)
try:
gr.hier_block.update_var_names(self, "schmidl_nom", vars())
gr.hier_block.update_var_names(self, "schmidl_nom", vars(self))
except:
pass
def __init__(self, fft_length):
gr.hier_block2.__init__(self, "schmidl_nominator",
gr.io_signature(1, 1, gr.sizeof_gr_complex),
gr.io_signature(1, 1, gr.sizeof_gr_complex))
self.input = gr.kludge_copy(gr.sizeof_gr_complex)
# P(d) = sum(0 to L-1, conj(delayed(r)) * r)
conj = gr.conjugate_cc()
mixer = gr.multiply_cc()
moving_avg = gr.fir_filter_ccf(1, [1.0 for i in range(fft_length / 2)])
self.connect(self, self.input,
delay(gr.sizeof_gr_complex, fft_length / 2), conj,
(mixer, 0))
self.connect(self.input, (mixer, 1))
self.connect(mixer, moving_avg, self)
# moving_avg = P(d)
try:
gr.hier_block.update_var_names(self, "schmidl_nom", vars())
gr.hier_block.update_var_names(self, "schmidl_nom", vars(self))
except:
pass
def __init__(self, fft_length, pn_weights):
gr.hier_block2.__init__(self, "modified_timing_metric",
gr.io_signature(1, 1, gr.sizeof_gr_complex),
gr.io_signature(1, 1, gr.sizeof_float))
assert (len(pn_weights) == fft_length)
self.input = gr.kludge_copy(gr.sizeof_gr_complex)
self.connect(self, self.input)
# P(d) = sum(0 to L-1, conj(delayed(r)) * r)
conj = gr.conjugate_cc()
mixer = gr.multiply_cc()
nominator = gr.fir_filter_ccf(1, [
pn_weights[fft_length - i - 1] * pn_weights[fft_length / 2 - i - 1]
for i in range(fft_length / 2)
])
self.connect(self.input, delay(gr.sizeof_gr_complex, fft_length / 2),
conj, (mixer, 0))
self.connect(self.input, (mixer, 1))
self.connect(mixer, nominator)
# moving_avg = P(d)
# R(d)
denominator = schmidl_denominator(fft_length)
# |P(d)| ** 2 / (R(d)) ** 2
p_mag_sqrd = gr.complex_to_mag_squared()
r_sqrd = gr.multiply_ff()
self.timing_metric = gr.divide_ff()
self.connect(nominator, p_mag_sqrd, (self.timing_metric, 0))
self.connect(self.input, denominator, (r_sqrd, 0))
self.connect(denominator, (r_sqrd, 1))
self.connect(r_sqrd, (self.timing_metric, 1))
self.connect(self.timing_metric, self)
def conjugate_cc(N):
op = gr.conjugate_cc()
tb = helper(N, op, gr.sizeof_gr_complex, gr.sizeof_gr_complex, 1, 1)
return tb
def __init__(self, fft_length, cp_length, snr, kstime, logging):
''' Maximum Likelihood OFDM synchronizer:
J. van de Beek, M. Sandell, and P. O. Borjesson, "ML Estimation
of Time and Frequency Offset in OFDM Systems," IEEE Trans.
Signal Processing, vol. 45, no. 7, pp. 1800-1805, 1997.
'''
gr.hier_block2.__init__(self, "ofdm_sync_ml",
gr.io_signature(1, 1, gr.sizeof_gr_complex), # Input signature
gr.io_signature2(2, 2, gr.sizeof_float, gr.sizeof_char)) # Output signature
self.input = gr.add_const_cc(0)
SNR = 10.0**(snr/10.0)
rho = SNR / (SNR + 1.0)
symbol_length = fft_length + cp_length
# ML Sync
# Energy Detection from ML Sync
self.connect(self, self.input)
# Create a delay line
self.delay = gr.delay(gr.sizeof_gr_complex, fft_length)
self.connect(self.input, self.delay)
# magnitude squared blocks
self.magsqrd1 = gr.complex_to_mag_squared()
self.magsqrd2 = gr.complex_to_mag_squared()
self.adder = gr.add_ff()
moving_sum_taps = [rho/2 for i in range(cp_length)]
self.moving_sum_filter = gr.fir_filter_fff(1,moving_sum_taps)
self.connect(self.input,self.magsqrd1)
self.connect(self.delay,self.magsqrd2)
self.connect(self.magsqrd1,(self.adder,0))
self.connect(self.magsqrd2,(self.adder,1))
self.connect(self.adder,self.moving_sum_filter)
# Correlation from ML Sync
self.conjg = gr.conjugate_cc();
self.mixer = gr.multiply_cc();
movingsum2_taps = [1.0 for i in range(cp_length)]
self.movingsum2 = gr.fir_filter_ccf(1,movingsum2_taps)
# Correlator data handler
self.c2mag = gr.complex_to_mag()
self.angle = gr.complex_to_arg()
self.connect(self.input,(self.mixer,1))
self.connect(self.delay,self.conjg,(self.mixer,0))
self.connect(self.mixer,self.movingsum2,self.c2mag)
self.connect(self.movingsum2,self.angle)
# ML Sync output arg, need to find maximum point of this
self.diff = gr.sub_ff()
self.connect(self.c2mag,(self.diff,0))
self.connect(self.moving_sum_filter,(self.diff,1))
#ML measurements input to sampler block and detect
self.f2c = gr.float_to_complex()
self.pk_detect = gr.peak_detector_fb(0.2, 0.25, 30, 0.0005)
self.sample_and_hold = gr.sample_and_hold_ff()
# use the sync loop values to set the sampler and the NCO
# self.diff = theta
# self.angle = epsilon
self.connect(self.diff, self.pk_detect)
# The DPLL corrects for timing differences between CP correlations
use_dpll = 0
if use_dpll:
self.dpll = gr.dpll_bb(float(symbol_length),0.01)
self.connect(self.pk_detect, self.dpll)
self.connect(self.dpll, (self.sample_and_hold,1))
else:
self.connect(self.pk_detect, (self.sample_and_hold,1))
self.connect(self.angle, (self.sample_and_hold,0))
################################
# correlate against known symbol
# This gives us the same timing signal as the PN sync block only on the preamble
# we don't use the signal generated from the CP correlation because we don't want
# to readjust the timing in the middle of the packet or we ruin the equalizer settings.
kstime = [k.conjugate() for k in kstime]
kstime.reverse()
self.kscorr = gr.fir_filter_ccc(1, kstime)
self.corrmag = gr.complex_to_mag_squared()
self.div = gr.divide_ff()
# The output signature of the correlation has a few spikes because the rest of the
# system uses the repeated preamble symbol. It needs to work that generically if
# anyone wants to use this against a WiMAX-like signal since it, too, repeats.
# The output theta of the correlator above is multiplied with this correlation to
# identify the proper peak and remove other products in this cross-correlation
self.threshold_factor = 0.1
self.slice = gr.threshold_ff(self.threshold_factor, self.threshold_factor, 0)
self.f2b = gr.float_to_char()
self.b2f = gr.char_to_float()
self.mul = gr.multiply_ff()
# Normalize the power of the corr output by the energy. This is not really needed
# and could be removed for performance, but it makes for a cleaner signal.
# if this is removed, the threshold value needs adjustment.
self.connect(self.input, self.kscorr, self.corrmag, (self.div,0))
self.connect(self.moving_sum_filter, (self.div,1))
self.connect(self.div, (self.mul,0))
self.connect(self.pk_detect, self.b2f, (self.mul,1))
self.connect(self.mul, self.slice)
# Set output signals
# Output 0: fine frequency correction value
# Output 1: timing signal
self.connect(self.sample_and_hold, (self,0))
self.connect(self.slice, self.f2b, (self,1))
if logging:
self.connect(self.moving_sum_filter, gr.file_sink(gr.sizeof_float, "ofdm_sync_ml-energy_f.dat"))
self.connect(self.diff, gr.file_sink(gr.sizeof_float, "ofdm_sync_ml-theta_f.dat"))
self.connect(self.angle, gr.file_sink(gr.sizeof_float, "ofdm_sync_ml-epsilon_f.dat"))
self.connect(self.corrmag, gr.file_sink(gr.sizeof_float, "ofdm_sync_ml-corrmag_f.dat"))
self.connect(self.kscorr, gr.file_sink(gr.sizeof_gr_complex, "ofdm_sync_ml-kscorr_c.dat"))
self.connect(self.div, gr.file_sink(gr.sizeof_float, "ofdm_sync_ml-div_f.dat"))
self.connect(self.mul, gr.file_sink(gr.sizeof_float, "ofdm_sync_ml-mul_f.dat"))
self.connect(self.slice, gr.file_sink(gr.sizeof_float, "ofdm_sync_ml-slice_f.dat"))
self.connect(self.pk_detect, gr.file_sink(gr.sizeof_char, "ofdm_sync_ml-peaks_b.dat"))
if use_dpll:
self.connect(self.dpll, gr.file_sink(gr.sizeof_char, "ofdm_sync_ml-dpll_b.dat"))
self.connect(self.sample_and_hold, gr.file_sink(gr.sizeof_float, "ofdm_sync_ml-sample_and_hold_f.dat"))
self.connect(self.input, gr.file_sink(gr.sizeof_gr_complex, "ofdm_sync_ml-input_c.dat"))
def __init__(self):
grc_wxgui.top_block_gui.__init__(self, title="Ofdm Rx")
##################################################
# Variables
##################################################
self.window_size = window_size = 48
self.sync_length = sync_length = 320 - 64
self.samp_rate = samp_rate = 10e6
self.gain = gain = 0
self.freq = freq = 5.825e9
##################################################
# Blocks
##################################################
self._samp_rate_chooser = forms.radio_buttons(
parent=self.GetWin(),
value=self.samp_rate,
callback=self.set_samp_rate,
label="Sample Rate",
choices=[10e6, 20e6],
labels=["10 Mhz", "20 Mhz"],
style=wx.RA_HORIZONTAL,
)
self.Add(self._samp_rate_chooser)
_gain_sizer = wx.BoxSizer(wx.VERTICAL)
self._gain_text_box = forms.text_box(
parent=self.GetWin(),
sizer=_gain_sizer,
value=self.gain,
callback=self.set_gain,
label='gain',
converter=forms.float_converter(),
proportion=0,
)
self._gain_slider = forms.slider(
parent=self.GetWin(),
sizer=_gain_sizer,
value=self.gain,
callback=self.set_gain,
minimum=0,
maximum=100,
num_steps=100,
style=wx.SL_HORIZONTAL,
cast=float,
proportion=1,
)
self.Add(_gain_sizer)
self._freq_chooser = forms.drop_down(
parent=self.GetWin(),
value=self.freq,
callback=self.set_freq,
label="Channel",
choices=[2412000000.0, 2417000000.0, 2422000000.0, 2427000000.0, 2432000000.0, 2437000000.0, 2442000000.0, 2447000000.0, 2452000000.0, 2457000000.0, 2462000000.0, 2467000000.0, 2472000000.0, 2484000000.0, 5170000000.0, 5180000000.0, 5190000000.0, 5200000000.0, 5210000000.0, 5220000000.0, 5230000000.0, 5240000000.0, 5260000000.0, 5280000000.0, 5300000000.0, 5320000000.0, 5500000000.0, 5520000000.0, 5540000000.0, 5560000000.0, 5580000000.0, 5600000000.0, 5620000000.0, 5640000000.0, 5660000000.0, 5680000000.0, 5700000000.0, 5745000000.0, 5765000000.0, 5785000000.0, 5805000000.0, 5825000000.0, 5860000000.0, 5870000000.0, 5880000000.0, 5890000000.0, 5900000000.0, 5910000000.0, 5920000000.0],
labels=[' 1 | 2412.0 | 11g', ' 2 | 2417.0 | 11g', ' 3 | 2422.0 | 11g', ' 4 | 2427.0 | 11g', ' 5 | 2432.0 | 11g', ' 6 | 2437.0 | 11g', ' 7 | 2442.0 | 11g', ' 8 | 2447.0 | 11g', ' 9 | 2452.0 | 11g', ' 10 | 2457.0 | 11g', ' 11 | 2462.0 | 11g', ' 12 | 2467.0 | 11g', ' 13 | 2472.0 | 11g', ' 14 | 2484.0 | 11g', ' 34 | 5170.0 | 11a', ' 36 | 5180.0 | 11a', ' 38 | 5190.0 | 11a', ' 40 | 5200.0 | 11a', ' 42 | 5210.0 | 11a', ' 44 | 5220.0 | 11a', ' 46 | 5230.0 | 11a', ' 48 | 5240.0 | 11a', ' 52 | 5260.0 | 11a', ' 56 | 5280.0 | 11a', ' 58 | 5300.0 | 11a', ' 60 | 5320.0 | 11a', '100 | 5500.0 | 11a', '104 | 5520.0 | 11a', '108 | 5540.0 | 11a', '112 | 5560.0 | 11a', '116 | 5580.0 | 11a', '120 | 5600.0 | 11a', '124 | 5620.0 | 11a', '128 | 5640.0 | 11a', '132 | 5660.0 | 11a', '136 | 5680.0 | 11a', '140 | 5700.0 | 11a', '149 | 5745.0 | 11a', '153 | 5765.0 | 11a', '157 | 5785.0 | 11a', '161 | 5805.0 | 11a', '165 | 5825.0 | 11a', '172 | 5860.0 | 11p', '174 | 5870.0 | 11p', '176 | 5880.0 | 11p', '178 | 5890.0 | 11p', '180 | 5900.0 | 11p', '182 | 5910.0 | 11p', '184 | 5920.0 | 11p'],
)
self.Add(self._freq_chooser)
self.uhd_usrp_source_0 = uhd.usrp_source(
device_addr="",
stream_args=uhd.stream_args(
cpu_format="fc32",
channels=range(1),
),
)
self.uhd_usrp_source_0.set_samp_rate(samp_rate)
self.uhd_usrp_source_0.set_center_freq(freq, 0)
self.uhd_usrp_source_0.set_gain(gain, 0)
self.ieee802_1_ofdm_sync_short_0 = gr_ieee802_11.ofdm_sync_short(0.8, 80 * 80, 2, False)
self.ieee802_1_ofdm_sync_long_0 = gr_ieee802_11.ofdm_sync_long(sync_length, 100, False)
self.ieee802_1_ofdm_equalize_symbols_0 = gr_ieee802_11.ofdm_equalize_symbols(False)
self.ieee802_1_ofdm_decode_signal_0 = gr_ieee802_11.ofdm_decode_signal(False)
self.ieee802_1_ofdm_decode_mac_0 = gr_ieee802_11.ofdm_decode_mac(False)
self.ieee802_11_ofdm_parse_mac_0 = gr_ieee802_11.ofdm_parse_mac(True)
self.gr_stream_to_vector_0 = gr.stream_to_vector(gr.sizeof_gr_complex*1, 64)
self.gr_socket_pdu_0 = gr.socket_pdu("UDP_SERVER", "", "12345", 10000)
self.gr_skiphead_0 = gr.skiphead(gr.sizeof_gr_complex*1, 20000000)
self.gr_multiply_xx_0 = gr.multiply_vcc(1)
self.gr_divide_xx_0 = gr.divide_ff(1)
self.gr_delay_0_0 = gr.delay(gr.sizeof_gr_complex*1, sync_length)
self.gr_delay_0 = gr.delay(gr.sizeof_gr_complex*1, 16)
self.gr_conjugate_cc_0 = gr.conjugate_cc()
self.gr_complex_to_mag_squared_0 = gr.complex_to_mag_squared(1)
self.gr_complex_to_mag_0 = gr.complex_to_mag(1)
self.fir_filter_xxx_0_0 = filter.fir_filter_ccf(1, ([1]*window_size))
self.fir_filter_xxx_0 = filter.fir_filter_fff(1, ([1]*window_size))
self.fft_vxx_0 = fft.fft_vcc(64, True, (), True, 1)
##################################################
# Connections
##################################################
self.connect((self.uhd_usrp_source_0, 0), (self.gr_skiphead_0, 0))
self.connect((self.gr_skiphead_0, 0), (self.gr_complex_to_mag_squared_0, 0))
self.connect((self.fir_filter_xxx_0, 0), (self.gr_divide_xx_0, 1))
self.connect((self.gr_complex_to_mag_squared_0, 0), (self.fir_filter_xxx_0, 0))
self.connect((self.gr_skiphead_0, 0), (self.gr_multiply_xx_0, 0))
self.connect((self.gr_conjugate_cc_0, 0), (self.gr_multiply_xx_0, 1))
self.connect((self.gr_complex_to_mag_0, 0), (self.gr_divide_xx_0, 0))
self.connect((self.gr_multiply_xx_0, 0), (self.fir_filter_xxx_0_0, 0))
self.connect((self.fir_filter_xxx_0_0, 0), (self.gr_complex_to_mag_0, 0))
self.connect((self.gr_skiphead_0, 0), (self.gr_delay_0, 0))
self.connect((self.gr_delay_0, 0), (self.gr_conjugate_cc_0, 0))
self.connect((self.fft_vxx_0, 0), (self.ieee802_1_ofdm_equalize_symbols_0, 0))
self.connect((self.ieee802_1_ofdm_equalize_symbols_0, 0), (self.ieee802_1_ofdm_decode_signal_0, 0))
self.connect((self.ieee802_1_ofdm_decode_signal_0, 0), (self.ieee802_1_ofdm_decode_mac_0, 0))
self.connect((self.ieee802_1_ofdm_sync_short_0, 0), (self.gr_delay_0_0, 0))
self.connect((self.gr_delay_0, 0), (self.ieee802_1_ofdm_sync_short_0, 0))
self.connect((self.gr_divide_xx_0, 0), (self.ieee802_1_ofdm_sync_short_0, 1))
self.connect((self.gr_delay_0_0, 0), (self.ieee802_1_ofdm_sync_long_0, 1))
self.connect((self.ieee802_1_ofdm_sync_short_0, 0), (self.ieee802_1_ofdm_sync_long_0, 0))
self.connect((self.ieee802_1_ofdm_sync_long_0, 0), (self.gr_stream_to_vector_0, 0))
self.connect((self.gr_stream_to_vector_0, 0), (self.fft_vxx_0, 0))
##################################################
# Asynch Message Connections
##################################################
self.msg_connect(self.ieee802_1_ofdm_decode_mac_0, "out", self.ieee802_11_ofdm_parse_mac_0, "in")
self.msg_connect(self.ieee802_11_ofdm_parse_mac_0, "out", self.gr_socket_pdu_0, "pdus")
def __init__(self, fft_length, cp_length, logging=False):
"""
OFDM synchronization using PN Correlation:
T. M. Schmidl and D. C. Cox, "Robust Frequency and Timing
Synchonization for OFDM," IEEE Trans. Communications, vol. 45,
no. 12, 1997. Improved with averaging over the whole FFT and
peak averaging over cp_length.
"""
gr.hier_block2.__init__(self, "ofdm_sync_pn",
gr.io_signature(1, 1, gr.sizeof_gr_complex), # Input signature
gr.io_signature2(2, 2, gr.sizeof_float, gr.sizeof_char)) # Output signature
self.input = gr.add_const_cc(0)
self.delay = gr.delay(gr.sizeof_gr_complex, fft_length/2)
self.conjg = gr.conjugate_cc();
self.corr = gr.multiply_cc();
moving_sum_taps = [1.0 for i in range(fft_length//2)]
self.moving_sum_filter = gr.fir_filter_ccf(1,moving_sum_taps)
self.inputmag2 = gr.complex_to_mag_squared()
#movingsum2_taps = [0.125 for i in range(fft_length*4)]
movingsum2_taps = [0.5 for i in range(fft_length*4)]
self.inputmovingsum = gr.fir_filter_fff(1,movingsum2_taps)
self.square = gr.multiply_ff()
self.normalize = gr.divide_ff()
self.c2mag = gr.complex_to_mag_squared()
self.angle = gr.complex_to_arg()
self.sample_and_hold = flex.sample_and_hold_ff()
self.sub1 = gr.add_const_ff(-1)
# linklab, change peak detector parameters: use higher threshold to detect rise/fall of peak
#self.pk_detect = gr.peak_detector_fb(0.20, 0.20, 30, 0.001)
self.pk_detect = flex.peak_detector_fb(0.7, 0.7, 30, 0.001) # linklab
#self.pk_detect = gr.peak_detector2_fb(9)
self.connect(self, self.input)
# Lower branch:
self.connect(self.input, self.delay)
self.connect(self.input, (self.corr,0))
self.connect(self.delay, self.conjg)
self.connect(self.conjg, (self.corr,1))
self.connect(self.corr, self.moving_sum_filter)
self.connect(self.moving_sum_filter, self.c2mag)
self.connect(self.moving_sum_filter, self.angle)
self.connect(self.angle, (self.sample_and_hold,0))
self.connect(self.c2mag, (self.normalize,0))
# Upper branch
self.connect(self.input, self.inputmag2, self.inputmovingsum)
self.connect(self.inputmovingsum, (self.square,0))
self.connect(self.inputmovingsum, (self.square,1))
self.connect(self.square, (self.normalize,1))
matched_filter_taps = [1.0/cp_length for i in range(cp_length)]
self.matched_filter = gr.fir_filter_fff(1,matched_filter_taps)
self.connect(self.normalize, self.matched_filter)
# linklab, provide the signal power into peak detector, linklab
self.connect(self.square, (self.pk_detect,1))
self.connect(self.matched_filter, self.sub1, (self.pk_detect, 0))
self.connect(self.pk_detect, (self.sample_and_hold,1))
# Set output signals
# Output 0: fine frequency correction value
# Output 1: timing signal
self.connect(self.sample_and_hold, (self,0))
self.connect(self.pk_detect, (self,1))
if logging:
self.connect(self.square, gr.file_sink(gr.sizeof_float, "ofdm_sync_pn-square.dat"))
self.connect(self.c2mag, gr.file_sink(gr.sizeof_float, "ofdm_sync_pn-c2mag.dat"))
self.connect(self.matched_filter, gr.file_sink(gr.sizeof_float, "ofdm_sync_pn-mf_f.dat"))
self.connect(self.sub1, gr.file_sink(gr.sizeof_float, "ofdm_sync_pn-sub1.dat"))
self.connect(self.normalize, gr.file_sink(gr.sizeof_float, "ofdm_sync_pn-theta_f.dat"))
self.connect(self.angle, gr.file_sink(gr.sizeof_float, "ofdm_sync_pn-epsilon_f.dat"))
self.connect(self.pk_detect, gr.file_sink(gr.sizeof_char, "ofdm_sync_pn-peaks_b.dat"))
self.connect(self.sample_and_hold, gr.file_sink(gr.sizeof_float, "ofdm_sync_pn-sample_and_hold_f.dat"))
self.connect(self.input, gr.file_sink(gr.sizeof_gr_complex, "ofdm_sync_pn-input_c.dat"))
def __init__(
self,
sample_rate,
ber_threshold=0, # Above which to do search
ber_smoothing=0, # Alpha of BER smoother (0.01)
ber_duration=0, # Length before trying next combo
ber_sample_decimation=1,
settling_period=0,
pre_lock_duration=0,
#ber_sample_skip=0
**kwargs):
use_throttle = False
base_duration = 1024
if sample_rate > 0:
use_throttle = True
base_duration *= 4 # Has to be high enough for block-delay
if ber_threshold == 0:
ber_threshold = 512 * 4
if ber_smoothing == 0:
ber_smoothing = 0.01
if ber_duration == 0:
ber_duration = base_duration * 2 # 1000ms
if settling_period == 0:
settling_period = base_duration * 1 # 500ms
if pre_lock_duration == 0:
pre_lock_duration = base_duration * 2 #1000ms
print "Creating Auto-FEC:"
print "\tsample_rate:\t\t", sample_rate
print "\tber_threshold:\t\t", ber_threshold
print "\tber_smoothing:\t\t", ber_smoothing
print "\tber_duration:\t\t", ber_duration
print "\tber_sample_decimation:\t", ber_sample_decimation
print "\tsettling_period:\t", settling_period
print "\tpre_lock_duration:\t", pre_lock_duration
print ""
self.sample_rate = sample_rate
self.ber_threshold = ber_threshold
#self.ber_smoothing = ber_smoothing
self.ber_duration = ber_duration
self.settling_period = settling_period
self.pre_lock_duration = pre_lock_duration
#self.ber_sample_skip = ber_sample_skip
self.data_lock = threading.Lock()
gr.hier_block2.__init__(
self,
"auto_fec",
gr.io_signature(
1, 1,
gr.sizeof_gr_complex), # Post MPSK-receiver complex input
gr.io_signature3(
3, 3, gr.sizeof_char, gr.sizeof_float,
gr.sizeof_float)) # Decoded packed bytes, BER metric, lock
self.input_watcher = auto_fec_input_watcher(self)
default_xform = self.input_watcher.xform_lock
self.gr_conjugate_cc_0 = gr.conjugate_cc()
self.connect((self, 0), (self.gr_conjugate_cc_0, 0)) # Input
self.blks2_selector_0 = grc_blks2.selector(
item_size=gr.sizeof_gr_complex * 1,
num_inputs=2,
num_outputs=1,
input_index=default_xform.get_conjugation_index(),
output_index=0,
)
self.connect((self.gr_conjugate_cc_0, 0), (self.blks2_selector_0, 0))
self.connect((self, 0), (self.blks2_selector_0, 1)) # Input
self.gr_multiply_const_vxx_3 = gr.multiply_const_vcc(
(0.707 * (1 + 1j), ))
self.connect((self.blks2_selector_0, 0),
(self.gr_multiply_const_vxx_3, 0))
self.gr_multiply_const_vxx_2 = gr.multiply_const_vcc(
(default_xform.get_rotation(), )) # phase_mult
self.connect((self.gr_multiply_const_vxx_3, 0),
(self.gr_multiply_const_vxx_2, 0))
self.gr_complex_to_float_0_0 = gr.complex_to_float(1)
self.connect((self.gr_multiply_const_vxx_2, 0),
(self.gr_complex_to_float_0_0, 0))
self.gr_interleave_1 = gr.interleave(gr.sizeof_float * 1)
self.connect((self.gr_complex_to_float_0_0, 1),
(self.gr_interleave_1, 1))
self.connect((self.gr_complex_to_float_0_0, 0),
(self.gr_interleave_1, 0))
self.gr_multiply_const_vxx_0 = gr.multiply_const_vff((1, )) # invert
self.connect((self.gr_interleave_1, 0),
(self.gr_multiply_const_vxx_0, 0))
self.baz_delay_2 = baz.delay(
gr.sizeof_float * 1,
default_xform.get_puncture_delay()) # delay_puncture
self.connect((self.gr_multiply_const_vxx_0, 0), (self.baz_delay_2, 0))
self.depuncture_ff_0 = baz.depuncture_ff(
(_puncture_matrices[self.input_watcher.puncture_matrix][1]
)) # puncture_matrix
self.connect((self.baz_delay_2, 0), (self.depuncture_ff_0, 0))
self.baz_delay_1 = baz.delay(
gr.sizeof_float * 1,
default_xform.get_viterbi_delay()) # delay_viterbi
self.connect((self.depuncture_ff_0, 0), (self.baz_delay_1, 0))
self.swap_ff_0 = baz.swap_ff(
default_xform.get_viterbi_swap()) # swap_viterbi
self.connect((self.baz_delay_1, 0), (self.swap_ff_0, 0))
self.gr_decode_ccsds_27_fb_0 = gr.decode_ccsds_27_fb()
if use_throttle:
print "==> Using throttle at sample rate:", self.sample_rate
self.gr_throttle_0 = gr.throttle(gr.sizeof_float, self.sample_rate)
self.connect((self.swap_ff_0, 0), (self.gr_throttle_0, 0))
self.connect((self.gr_throttle_0, 0),
(self.gr_decode_ccsds_27_fb_0, 0))
else:
self.connect((self.swap_ff_0, 0),
(self.gr_decode_ccsds_27_fb_0, 0))
self.connect((self.gr_decode_ccsds_27_fb_0, 0),
(self, 0)) # Output bytes
self.gr_add_const_vxx_1 = gr.add_const_vff((-4096, ))
self.connect((self.gr_decode_ccsds_27_fb_0, 1),
(self.gr_add_const_vxx_1, 0))
self.gr_multiply_const_vxx_1 = gr.multiply_const_vff((-1, ))
self.connect((self.gr_add_const_vxx_1, 0),
(self.gr_multiply_const_vxx_1, 0))
self.connect((self.gr_multiply_const_vxx_1, 0),
(self, 1)) # Output BER
self.gr_single_pole_iir_filter_xx_0 = gr.single_pole_iir_filter_ff(
ber_smoothing, 1)
self.connect((self.gr_multiply_const_vxx_1, 0),
(self.gr_single_pole_iir_filter_xx_0, 0))
self.gr_keep_one_in_n_0 = blocks.keep_one_in_n(gr.sizeof_float,
ber_sample_decimation)
self.connect((self.gr_single_pole_iir_filter_xx_0, 0),
(self.gr_keep_one_in_n_0, 0))
self.const_source_x_0 = gr.sig_source_f(0, gr.GR_CONST_WAVE, 0, 0,
0) # Last param is const value
if use_throttle:
lock_throttle_rate = self.sample_rate // 16
print "==> Using lock throttle rate:", lock_throttle_rate
self.gr_throttle_1 = gr.throttle(gr.sizeof_float,
lock_throttle_rate)
self.connect((self.const_source_x_0, 0), (self.gr_throttle_1, 0))
self.connect((self.gr_throttle_1, 0), (self, 2))
else:
self.connect((self.const_source_x_0, 0), (self, 2))
self.msg_q = gr.msg_queue(
2 * 256
) # message queue that holds at most 2 messages, increase to speed up process
self.msg_sink = gr.message_sink(
gr.sizeof_float, self.msg_q,
dont_block=0) # Block to speed up process
self.connect((self.gr_keep_one_in_n_0, 0), self.msg_sink)
self.input_watcher.start()
def __init__(self, fft_length, cp_length, snr=30):
''' Maximum Likelihood OFDM synchronizer:
J. van de Beek, M. Sandell, and P. O. Borjesson, "ML Estimation
of Time and Frequency Offset in OFDM Systems," IEEE Trans.
Signal Processing, vol. 45, no. 7, pp. 1800-1805, 1997.
'''
gr.hier_block2.__init__(self, "ofdm_sync_ml",
gr.io_signature(1, 1, gr.sizeof_gr_complex), # Input signature
gr.io_signature(1, 1, gr.sizeof_float)) # Output signature
self.input = gr.add_const_cc(0)
SNR = 10.0**(snr/10.0) # 10**2=10*10=100
rho = SNR / (SNR + 1.0)
symbol_length = fft_length + cp_length
# ML Sync
# Energy Detection from ML Sync
self.connect(self, self.input)
# Create a delay line
self.delay = gr.delay(gr.sizeof_gr_complex, fft_length)
self.connect(self.input, self.delay)
# magnitude squared blocks
self.magsqrd1 = gr.complex_to_mag_squared()
self.magsqrd2 = gr.complex_to_mag_squared()
self.adder = gr.add_ff()
moving_sum_taps = [rho/2 for i in range(cp_length)]
self.moving_sum_filter = gr.fir_filter_fff(1,moving_sum_taps)
self.connect(self.input,self.magsqrd1)
self.connect(self.delay,self.magsqrd2)
self.connect(self.magsqrd1,(self.adder,0))
self.connect(self.magsqrd2,(self.adder,1))
self.connect(self.adder,self.moving_sum_filter)
# Correlation from ML Sync
self.conjg = gr.conjugate_cc();
self.mixer = gr.multiply_cc();
movingsum2_taps = [1.0 for i in range(cp_length)]
self.movingsum2 = gr.fir_filter_ccf(1,movingsum2_taps)
# Correlator data handler
self.c2mag = gr.complex_to_mag()
self.connect(self.input,(self.mixer,1))
self.connect(self.delay,self.conjg,(self.mixer,0))
self.connect(self.mixer,self.movingsum2,self.c2mag)
# ML Sync output arg, need to find maximum point of this
self.diff = gr.sub_ff()
self.connect(self.c2mag,(self.diff,0))
self.connect(self.moving_sum_filter,(self.diff,1))
self.symbol_finder = Heyutu.symbol_finder_ff(fft_length, cp_length)
self.connect(self.diff, self.symbol_finder)
#ML measurements input to sampler block and detect
#self.f2c = gr.float_to_complex()
#self.pk_detect = gr.peak_detector_fb(0.2, 0.25, 30, 0.0005)
# self.pk_detect = gr.peak_detector_fb(0.3, 0.4, 30, 0.001)
# use the sync loop values to set the sampler and the NCO
# self.diff = theta
# self.connect(self.diff, self.pk_detect)
# The DPLL corrects for timing differences between CP correlations
# self.dpll = gr.dpll_bb(float(symbol_length),0.01)
# self.connect(self.pk_detect, self.dpll)
# self.b2f = gr.char_to_float()
# self.connect(self.dpll, self.b2f)
# Set output signals
# Output 0: timing signal
# self.connect(self.b2f, (self,0))
# self.connect(self.diff, (self,0))
self.connect(self.symbol_finder, (self, 0))
def __init__(self, vlen):
gr.hier_block2.__init__(
self, "snr_estimator",
gr.io_signature2(2, 2, gr.sizeof_gr_complex, gr.sizeof_char),
gr.io_signature(1, 1, gr.sizeof_float))
data_in = (self, 0)
trig_in = (self, 1)
snr_out = (self, 0)
## Preamble Extraction
sampler = vector_sampler(gr.sizeof_gr_complex, vlen)
self.connect(data_in, sampler)
self.connect(trig_in, (sampler, 1))
## Algorithm implementation
estim = sc_snr_estimator(vlen)
self.connect(sampler, estim)
self.connect(estim, snr_out)
return
## Split block into two parts
splitter = gr.vector_to_streams(gr.sizeof_gr_complex * vlen / 2, 2)
self.connect(sampler, splitter)
## Conjugate first half block
conj = gr.conjugate_cc(vlen / 2)
self.connect(splitter, conj)
## Vector multiplication of both half blocks
vmult = gr.multiply_vcc(vlen / 2)
self.connect(conj, vmult)
self.connect((splitter, 1), (vmult, 1))
## Sum of Products
psum = vector_sum_vcc(vlen / 2)
self.connect(vmult, psum)
## Magnitude of P(d)
p_mag = gr.complex_to_mag()
self.connect(psum, p_mag)
## Squared Magnitude of block
r_magsqrd = gr.complex_to_mag_squared(vlen)
self.connect(sampler, r_magsqrd)
## Sum of squared second half block
r_sum = vector_sum_vff(vlen)
self.connect(r_magsqrd, r_sum)
## Square Root of Metric
m_sqrt = gr.divide_ff()
self.connect(p_mag, (m_sqrt, 0))
self.connect(r_sum, gr.multiply_const_ff(0.5), (m_sqrt, 1))
## Denominator of SNR estimate
denom = gr.add_const_ff(1)
neg_m_sqrt = gr.multiply_const_ff(-1.0)
self.connect(m_sqrt, limit_vff(1, 1 - 2e-5, -1000), neg_m_sqrt, denom)
## SNR estimate
snr_est = gr.divide_ff()
self.connect(m_sqrt, (snr_est, 0))
self.connect(denom, (snr_est, 1))
## Setup Output Connections
self.connect(snr_est, self)
def __init__(self, fft_length, cp_length, half_sync, logging=False):
gr.hier_block2.__init__(
self,
"ofdm_sync_pn",
gr.io_signature(1, 1, gr.sizeof_gr_complex), # Input signature
gr.io_signature3(
3,
3, # Output signature
gr.sizeof_gr_complex, # delayed input
gr.sizeof_float, # fine frequency offset
gr.sizeof_char # timing indicator
))
if half_sync:
period = fft_length / 2
window = fft_length / 2
else: # full symbol
period = fft_length + cp_length
window = fft_length # makes the plateau cp_length long
# Calculate the frequency offset from the correlation of the preamble
x_corr = gr.multiply_cc()
self.connect(self, gr.conjugate_cc(), (x_corr, 0))
self.connect(self, gr.delay(gr.sizeof_gr_complex, period), (x_corr, 1))
P_d = gr.moving_average_cc(window, 1.0)
self.connect(x_corr, P_d)
P_d_angle = gr.complex_to_arg()
self.connect(P_d, P_d_angle)
# Get the power of the input signal to normalize the output of the correlation
R_d = gr.moving_average_ff(window, 1.0)
self.connect(self, gr.complex_to_mag_squared(), R_d)
R_d_squared = gr.multiply_ff() # this is retarded
self.connect(R_d, (R_d_squared, 0))
self.connect(R_d, (R_d_squared, 1))
M_d = gr.divide_ff()
self.connect(P_d, gr.complex_to_mag_squared(), (M_d, 0))
self.connect(R_d_squared, (M_d, 1))
# Now we need to detect peak of M_d
# NOTE: replaced fir_filter with moving_average for clarity
# the peak is up to cp_length long, but noisy, so average it out
#matched_filter_taps = [1.0/cp_length for i in range(cp_length)]
#matched_filter = gr.fir_filter_fff(1, matched_filter_taps)
matched_filter = gr.moving_average_ff(cp_length, 1.0 / cp_length)
# NOTE: the look_ahead parameter doesn't do anything
# these parameters are kind of magic, increase 1 and 2 (==) to be more tolerant
#peak_detect = raw.peak_detector_fb(0.55, 0.55, 30, 0.001)
peak_detect = raw.peak_detector_fb(0.25, 0.25, 30, 0.001)
# NOTE: gr.peak_detector_fb is broken!
#peak_detect = gr.peak_detector_fb(0.55, 0.55, 30, 0.001)
#peak_detect = gr.peak_detector_fb(0.45, 0.45, 30, 0.001)
#peak_detect = gr.peak_detector_fb(0.30, 0.30, 30, 0.001)
# offset by -1
self.connect(M_d, matched_filter, gr.add_const_ff(-1), peak_detect)
# peak_detect indicates the time M_d is highest, which is the end of the symbol.
# We should try to sample in the middle of the plateau!!
# FIXME until we figure out how to do this, just offset by cp_length/2
offset = 6 #cp_length/2
# nco(t) = P_d_angle(t-offset) sampled at peak_detect(t)
# modulate input(t - fft_length) by nco(t)
# signal to sample input(t) at t-offset
#
# We can't delay by < 0 so instead:
# input is delayed by fft_length
# P_d_angle is delayed by offset
# signal to sample is delayed by fft_length - offset
#
phi = gr.sample_and_hold_ff()
self.connect(peak_detect, (phi, 1))
self.connect(P_d_angle, gr.delay(gr.sizeof_float, offset), (phi, 0))
#self.connect(P_d_angle, matched_filter2, (phi,0)) # why isn't this better?!?
# FIXME: we add fft_length delay so that the preamble is nco corrected too
# BUT is this buffering worth it? consider implementing sync as a proper block
# delay the input signal to follow the frequency offset signal
self.connect(self, gr.delay(gr.sizeof_gr_complex,
(fft_length + offset)), (self, 0))
self.connect(phi, (self, 1))
self.connect(peak_detect, (self, 2))
if logging:
self.connect(matched_filter,
gr.file_sink(gr.sizeof_float, "sync-mf.dat"))
self.connect(M_d, gr.file_sink(gr.sizeof_float, "sync-M.dat"))
self.connect(P_d_angle,
gr.file_sink(gr.sizeof_float, "sync-angle.dat"))
self.connect(peak_detect,
gr.file_sink(gr.sizeof_char, "sync-peaks.datb"))
self.connect(phi, gr.file_sink(gr.sizeof_float, "sync-phi.dat"))
def __init__(self, fft_length, cp_length, half_sync, logging=False):
gr.hier_block2.__init__(self, "ofdm_sync_pn",
gr.io_signature(1, 1, gr.sizeof_gr_complex), # Input signature
gr.io_signature3(3, 3, # Output signature
gr.sizeof_gr_complex, # delayed input
gr.sizeof_float, # fine frequency offset
gr.sizeof_char # timing indicator
))
if half_sync:
period = fft_length/2
window = fft_length/2
else: # full symbol
period = fft_length + cp_length
window = fft_length # makes the plateau cp_length long
# Calculate the frequency offset from the correlation of the preamble
x_corr = gr.multiply_cc()
self.connect(self, gr.conjugate_cc(), (x_corr, 0))
self.connect(self, gr.delay(gr.sizeof_gr_complex, period), (x_corr, 1))
P_d = gr.moving_average_cc(window, 1.0)
self.connect(x_corr, P_d)
P_d_angle = gr.complex_to_arg()
self.connect(P_d, P_d_angle)
# Get the power of the input signal to normalize the output of the correlation
R_d = gr.moving_average_ff(window, 1.0)
self.connect(self, gr.complex_to_mag_squared(), R_d)
R_d_squared = gr.multiply_ff() # this is retarded
self.connect(R_d, (R_d_squared, 0))
self.connect(R_d, (R_d_squared, 1))
M_d = gr.divide_ff()
self.connect(P_d, gr.complex_to_mag_squared(), (M_d, 0))
self.connect(R_d_squared, (M_d, 1))
# Now we need to detect peak of M_d
# NOTE: replaced fir_filter with moving_average for clarity
# the peak is up to cp_length long, but noisy, so average it out
#matched_filter_taps = [1.0/cp_length for i in range(cp_length)]
#matched_filter = gr.fir_filter_fff(1, matched_filter_taps)
matched_filter = gr.moving_average_ff(cp_length, 1.0/cp_length)
# NOTE: the look_ahead parameter doesn't do anything
# these parameters are kind of magic, increase 1 and 2 (==) to be more tolerant
#peak_detect = raw.peak_detector_fb(0.55, 0.55, 30, 0.001)
peak_detect = raw.peak_detector_fb(0.25, 0.25, 30, 0.001)
# NOTE: gr.peak_detector_fb is broken!
#peak_detect = gr.peak_detector_fb(0.55, 0.55, 30, 0.001)
#peak_detect = gr.peak_detector_fb(0.45, 0.45, 30, 0.001)
#peak_detect = gr.peak_detector_fb(0.30, 0.30, 30, 0.001)
# offset by -1
self.connect(M_d, matched_filter, gr.add_const_ff(-1), peak_detect)
# peak_detect indicates the time M_d is highest, which is the end of the symbol.
# We should try to sample in the middle of the plateau!!
# FIXME until we figure out how to do this, just offset by cp_length/2
offset = 6 #cp_length/2
# nco(t) = P_d_angle(t-offset) sampled at peak_detect(t)
# modulate input(t - fft_length) by nco(t)
# signal to sample input(t) at t-offset
#
# We can't delay by < 0 so instead:
# input is delayed by fft_length
# P_d_angle is delayed by offset
# signal to sample is delayed by fft_length - offset
#
phi = gr.sample_and_hold_ff()
self.connect(peak_detect, (phi,1))
self.connect(P_d_angle, gr.delay(gr.sizeof_float, offset), (phi,0))
#self.connect(P_d_angle, matched_filter2, (phi,0)) # why isn't this better?!?
# FIXME: we add fft_length delay so that the preamble is nco corrected too
# BUT is this buffering worth it? consider implementing sync as a proper block
# delay the input signal to follow the frequency offset signal
self.connect(self, gr.delay(gr.sizeof_gr_complex, (fft_length+offset)), (self,0))
self.connect(phi, (self,1))
self.connect(peak_detect, (self,2))
if logging:
self.connect(matched_filter, gr.file_sink(gr.sizeof_float, "sync-mf.dat"))
self.connect(M_d, gr.file_sink(gr.sizeof_float, "sync-M.dat"))
self.connect(P_d_angle, gr.file_sink(gr.sizeof_float, "sync-angle.dat"))
self.connect(peak_detect, gr.file_sink(gr.sizeof_char, "sync-peaks.datb"))
self.connect(phi, gr.file_sink(gr.sizeof_float, "sync-phi.dat"))
def __init__(self, fft_length, cp_length, kstime, threshold, threshold_type, threshold_gap, logging=False):
"""
OFDM synchronization using PN Correlation:
T. M. Schmidl and D. C. Cox, "Robust Frequency and Timing
Synchonization for OFDM," IEEE Trans. Communications, vol. 45,
no. 12, 1997.
"""
gr.hier_block2.__init__(self, "ofdm_sync_pn",
gr.io_signature(1, 1, gr.sizeof_gr_complex), # Input signature
gr.io_signature2(2, 2, gr.sizeof_float, gr.sizeof_char)) # Output signature
self.input = gr.add_const_cc(0)
# PN Sync
# Create a delay line
self.delay = gr.delay(gr.sizeof_gr_complex, fft_length/2)
# Correlation from ML Sync
self.conjg = gr.conjugate_cc();
self.corr = gr.multiply_cc();
# Create a moving sum filter for the corr output
if 1:
moving_sum_taps = [1.0 for i in range(fft_length//2)]
self.moving_sum_filter = gr.fir_filter_ccf(1,moving_sum_taps)
else:
moving_sum_taps = [complex(1.0,0.0) for i in range(fft_length//2)]
self.moving_sum_filter = gr.fft_filter_ccc(1,moving_sum_taps)
# Create a moving sum filter for the input
self.inputmag2 = gr.complex_to_mag_squared()
movingsum2_taps = [1.0 for i in range(fft_length//2)]
#movingsum2_taps = [0.5 for i in range(fft_length*4)] #apurv - implementing Veljo's suggestion, when pause b/w packets
if 1:
self.inputmovingsum = gr.fir_filter_fff(1,movingsum2_taps)
else:
self.inputmovingsum = gr.fft_filter_fff(1,movingsum2_taps)
self.square = gr.multiply_ff()
self.normalize = gr.divide_ff()
# Get magnitude (peaks) and angle (phase/freq error)
self.c2mag = gr.complex_to_mag_squared()
self.angle = gr.complex_to_arg()
self.sample_and_hold = gr.sample_and_hold_ff()
#ML measurements input to sampler block and detect
self.sub1 = gr.add_const_ff(-1)
self.pk_detect = gr.peak_detector_fb(0.20, 0.20, 30, 0.001) #apurv - implementing Veljo's suggestion, when pause b/w packets
self.connect(self, self.input)
# Calculate the frequency offset from the correlation of the preamble
self.connect(self.input, self.delay)
self.connect(self.input, (self.corr,0))
self.connect(self.delay, self.conjg)
self.connect(self.conjg, (self.corr,1))
self.connect(self.corr, self.moving_sum_filter)
#self.connect(self.moving_sum_filter, self.c2mag)
self.connect(self.moving_sum_filter, self.angle)
self.connect(self.angle, (self.sample_and_hold,0)) # apurv--
#self.connect(self.angle, gr.delay(gr.sizeof_float, offset), (self.sample_and_hold, 0)) #apurv++
cross_correlate = 1
if cross_correlate==1:
# cross-correlate with the known symbol
kstime = [k.conjugate() for k in kstime]
kstime.reverse()
self.crosscorr_filter = gr.fir_filter_ccc(1, kstime)
# get the magnitude #
self.corrmag = gr.complex_to_mag_squared()
self.f2b = gr.float_to_char()
self.threshold_factor = threshold #0.0012 #0.012 #0.0015
if 0:
self.slice = gr.threshold_ff(self.threshold_factor, self.threshold_factor, 0, fft_length)
else:
#thresholds = [self.threshold_factor, 9e-5]
self.slice = gr.threshold_ff(threshold, threshold, 0, fft_length, threshold_type, threshold_gap)
self.connect(self.input, self.crosscorr_filter, self.corrmag, self.slice, self.f2b)
# some debug dump #
self.connect(self.corrmag, gr.file_sink(gr.sizeof_float, "ofdm_corrmag.dat"))
#self.connect(self.f2b, gr.file_sink(gr.sizeof_char, "ofdm_f2b.dat"))
self.connect(self.f2b, (self.sample_and_hold,1))
# Set output signals
# Output 0: fine frequency correction value
# Output 1: timing signal
self.connect(self.sample_and_hold, (self,0))
#self.connect(self.pk_detect, (self,1)) #removed
#self.connect(self.f2b, gr.delay(gr.sizeof_char, 1), (self, 1))
self.connect(self.f2b, (self, 1))
if logging:
self.connect(self.matched_filter, gr.file_sink(gr.sizeof_float, "ofdm_sync_pn-mf_f.dat"))
self.connect(self.normalize, gr.file_sink(gr.sizeof_float, "ofdm_sync_pn-theta_f.dat"))
self.connect(self.angle, gr.file_sink(gr.sizeof_float, "ofdm_sync_pn-epsilon_f.dat"))
self.connect(self.pk_detect, gr.file_sink(gr.sizeof_char, "ofdm_sync_pn-peaks_b.dat"))
self.connect(self.sample_and_hold, gr.file_sink(gr.sizeof_float, "ofdm_sync_pn-sample_and_hold_f.dat"))
self.connect(self.input, gr.file_sink(gr.sizeof_gr_complex, "ofdm_sync_pn-input_c.dat"))
def __init__(self,
fft_length,
cp_length,
kstime,
threshold,
threshold_type,
threshold_gap,
logging=False):
"""
OFDM synchronization using PN Correlation:
T. M. Schmidl and D. C. Cox, "Robust Frequency and Timing
Synchonization for OFDM," IEEE Trans. Communications, vol. 45,
no. 12, 1997.
"""
gr.hier_block2.__init__(
self,
"ofdm_sync_pn",
gr.io_signature(1, 1, gr.sizeof_gr_complex), # Input signature
gr.io_signature2(2, 2, gr.sizeof_float,
gr.sizeof_char)) # Output signature
self.input = gr.add_const_cc(0)
# PN Sync
# Create a delay line
self.delay = gr.delay(gr.sizeof_gr_complex, fft_length / 2)
# Correlation from ML Sync
self.conjg = gr.conjugate_cc()
self.corr = gr.multiply_cc()
# Create a moving sum filter for the corr output
if 1:
moving_sum_taps = [1.0 for i in range(fft_length // 2)]
self.moving_sum_filter = gr.fir_filter_ccf(1, moving_sum_taps)
else:
moving_sum_taps = [
complex(1.0, 0.0) for i in range(fft_length // 2)
]
self.moving_sum_filter = gr.fft_filter_ccc(1, moving_sum_taps)
# Create a moving sum filter for the input
self.inputmag2 = gr.complex_to_mag_squared()
movingsum2_taps = [1.0 for i in range(fft_length // 2)]
#movingsum2_taps = [0.5 for i in range(fft_length*4)] #apurv - implementing Veljo's suggestion, when pause b/w packets
if 1:
self.inputmovingsum = gr.fir_filter_fff(1, movingsum2_taps)
else:
self.inputmovingsum = gr.fft_filter_fff(1, movingsum2_taps)
self.square = gr.multiply_ff()
self.normalize = gr.divide_ff()
# Get magnitude (peaks) and angle (phase/freq error)
self.c2mag = gr.complex_to_mag_squared()
self.angle = gr.complex_to_arg()
self.sample_and_hold = gr.sample_and_hold_ff()
#ML measurements input to sampler block and detect
self.sub1 = gr.add_const_ff(-1)
self.pk_detect = gr.peak_detector_fb(
0.20, 0.20, 30, 0.001
) #apurv - implementing Veljo's suggestion, when pause b/w packets
self.connect(self, self.input)
# Calculate the frequency offset from the correlation of the preamble
self.connect(self.input, self.delay)
self.connect(self.input, (self.corr, 0))
self.connect(self.delay, self.conjg)
self.connect(self.conjg, (self.corr, 1))
self.connect(self.corr, self.moving_sum_filter)
#self.connect(self.moving_sum_filter, self.c2mag)
self.connect(self.moving_sum_filter, self.angle)
self.connect(self.angle, (self.sample_and_hold, 0)) # apurv--
#self.connect(self.angle, gr.delay(gr.sizeof_float, offset), (self.sample_and_hold, 0)) #apurv++
cross_correlate = 1
if cross_correlate == 1:
# cross-correlate with the known symbol
kstime = [k.conjugate() for k in kstime]
kstime.reverse()
self.crosscorr_filter = gr.fir_filter_ccc(1, kstime)
# get the magnitude #
self.corrmag = gr.complex_to_mag_squared()
self.f2b = gr.float_to_char()
self.threshold_factor = threshold #0.0012 #0.012 #0.0015
if 0:
self.slice = gr.threshold_ff(self.threshold_factor,
self.threshold_factor, 0,
fft_length)
else:
#thresholds = [self.threshold_factor, 9e-5]
self.slice = gr.threshold_ff(threshold, threshold, 0,
fft_length, threshold_type,
threshold_gap)
self.connect(self.input, self.crosscorr_filter, self.corrmag,
self.slice, self.f2b)
# some debug dump #
self.connect(self.corrmag,
gr.file_sink(gr.sizeof_float, "ofdm_corrmag.dat"))
#self.connect(self.f2b, gr.file_sink(gr.sizeof_char, "ofdm_f2b.dat"))
self.connect(self.f2b, (self.sample_and_hold, 1))
# Set output signals
# Output 0: fine frequency correction value
# Output 1: timing signal
self.connect(self.sample_and_hold, (self, 0))
#self.connect(self.pk_detect, (self,1)) #removed
#self.connect(self.f2b, gr.delay(gr.sizeof_char, 1), (self, 1))
self.connect(self.f2b, (self, 1))
if logging:
self.connect(
self.matched_filter,
gr.file_sink(gr.sizeof_float, "ofdm_sync_pn-mf_f.dat"))
self.connect(
self.normalize,
gr.file_sink(gr.sizeof_float, "ofdm_sync_pn-theta_f.dat"))
self.connect(
self.angle,
gr.file_sink(gr.sizeof_float, "ofdm_sync_pn-epsilon_f.dat"))
self.connect(
self.pk_detect,
gr.file_sink(gr.sizeof_char, "ofdm_sync_pn-peaks_b.dat"))
self.connect(
self.sample_and_hold,
gr.file_sink(gr.sizeof_float,
"ofdm_sync_pn-sample_and_hold_f.dat"))
self.connect(
self.input,
gr.file_sink(gr.sizeof_gr_complex, "ofdm_sync_pn-input_c.dat"))
def __init__(self, mode, debug=False):
"""
OFDM time and coarse frequency synchronisation for DAB
@param mode DAB mode (1-4)
@param debug if True: write data streams out to files
"""
if mode < 1 or mode > 4:
raise ValueError, "Invalid DAB mode: " + str(
mode) + " (modes 1-4 exist)"
# get the correct DAB parameters
dp = parameters.dab_parameters(mode)
rp = parameters.receiver_parameters(mode)
gr.hier_block2.__init__(
self,
"ofdm_sync_dab",
gr.io_signature(1, 1, gr.sizeof_gr_complex), # input signature
gr.io_signature2(2, 2, gr.sizeof_gr_complex,
gr.sizeof_char)) # output signature
# workaround for a problem that prevents connecting more than one block directly (see trac ticket #161)
self.input = gr.kludge_copy(gr.sizeof_gr_complex)
self.connect(self, self.input)
#
# null-symbol detection
#
# (outsourced to detect_zero.py)
self.ns_detect = detect_null.detect_null(dp.ns_length, debug)
self.connect(self.input, self.ns_detect)
#
# fine frequency synchronisation
#
# the code for fine frequency synchronisation is adapted from
# ofdm_sync_ml.py; it abuses the cyclic prefix to find the fine
# frequency error, as suggested in "ML Estimation of Timing and
# Frequency Offset in OFDM Systems", by Jan-Jaap van de Beek,
# Magnus Sandell, Per Ola Börjesson, see
# http://www.sm.luth.se/csee/sp/research/report/bsb96r.html
self.ffs_delay = gr.delay(gr.sizeof_gr_complex, dp.fft_length)
self.ffs_conj = gr.conjugate_cc()
self.ffs_mult = gr.multiply_cc()
# self.ffs_moving_sum = gr.fir_filter_ccf(1, [1]*dp.cp_length)
self.ffs_moving_sum = dab_swig.moving_sum_cc(dp.cp_length)
self.ffs_angle = gr.complex_to_arg()
self.ffs_angle_scale = gr.multiply_const_ff(1. / dp.fft_length)
self.ffs_delay_sample_and_hold = gr.delay(
gr.sizeof_char,
dp.symbol_length) # sample the value at the end of the symbol ..
self.ffs_sample_and_hold = gr.sample_and_hold_ff()
self.ffs_delay_input_for_correction = gr.delay(
gr.sizeof_gr_complex, dp.symbol_length
) # by delaying the input, we can use the ff offset estimation from the first symbol to correct the first symbol itself
self.ffs_nco = gr.frequency_modulator_fc(
1) # ffs_sample_and_hold directly outputs phase error per sample
self.ffs_mixer = gr.multiply_cc()
# calculate fine frequency error
self.connect(self.input, self.ffs_conj, self.ffs_mult)
self.connect(self.input, self.ffs_delay, (self.ffs_mult, 1))
self.connect(self.ffs_mult, self.ffs_moving_sum, self.ffs_angle)
# only use the value from the first half of the first symbol
self.connect(self.ffs_angle, self.ffs_angle_scale,
(self.ffs_sample_and_hold, 0))
self.connect(self.ns_detect, self.ffs_delay_sample_and_hold,
(self.ffs_sample_and_hold, 1))
# do the correction
self.connect(self.ffs_sample_and_hold, self.ffs_nco,
(self.ffs_mixer, 0))
self.connect(self.input, self.ffs_delay_input_for_correction,
(self.ffs_mixer, 1))
# output - corrected signal and start of DAB frames
self.connect(self.ffs_mixer, (self, 0))
self.connect(self.ffs_delay_sample_and_hold, (self, 1))
if debug:
self.connect(
self.ffs_angle,
gr.file_sink(gr.sizeof_float,
"debug/ofdm_sync_dab_ffs_angle.dat"))
self.connect(
self.ffs_sample_and_hold,
gr.multiply_const_ff(1. / (dp.T * 2 * pi)),
gr.file_sink(gr.sizeof_float,
"debug/ofdm_sync_dab_fine_freq_err_f.dat"))
self.connect(
self.ffs_mixer,
gr.file_sink(gr.sizeof_gr_complex,
"debug/ofdm_sync_dab_fine_freq_corrected_c.dat"))
def __init__(self, vlen):
gr.hier_block2.__init__(self, "snr_estimator",
gr.io_signature2(2,2,gr.sizeof_gr_complex,gr.sizeof_char),
gr.io_signature (1,1,gr.sizeof_float))
data_in = (self,0)
trig_in = (self,1)
snr_out = (self,0)
## Preamble Extraction
sampler = vector_sampler(gr.sizeof_gr_complex,vlen)
self.connect(data_in,sampler)
self.connect(trig_in,(sampler,1))
## Algorithm implementation
estim = sc_snr_estimator(vlen)
self.connect(sampler,estim)
self.connect(estim,snr_out)
return
## Split block into two parts
splitter = gr.vector_to_streams(gr.sizeof_gr_complex*vlen/2,2)
self.connect(sampler,splitter)
## Conjugate first half block
conj = gr.conjugate_cc(vlen/2)
self.connect(splitter,conj)
## Vector multiplication of both half blocks
vmult = gr.multiply_vcc(vlen/2)
self.connect(conj,vmult)
self.connect((splitter,1),(vmult,1))
## Sum of Products
psum = vector_sum_vcc(vlen/2)
self.connect(vmult,psum)
## Magnitude of P(d)
p_mag = gr.complex_to_mag()
self.connect(psum,p_mag)
## Squared Magnitude of block
r_magsqrd = gr.complex_to_mag_squared(vlen)
self.connect(sampler,r_magsqrd)
## Sum of squared second half block
r_sum = vector_sum_vff(vlen)
self.connect(r_magsqrd,r_sum)
## Square Root of Metric
m_sqrt = gr.divide_ff()
self.connect(p_mag,(m_sqrt,0))
self.connect(r_sum,gr.multiply_const_ff(0.5),(m_sqrt,1))
## Denominator of SNR estimate
denom = gr.add_const_ff(1)
neg_m_sqrt = gr.multiply_const_ff(-1.0)
self.connect(m_sqrt,limit_vff(1,1-2e-5,-1000),neg_m_sqrt,denom)
## SNR estimate
snr_est = gr.divide_ff()
self.connect(m_sqrt,(snr_est,0))
self.connect(denom,(snr_est,1))
## Setup Output Connections
self.connect(snr_est,self)
def __init__(self, fft_length, cp_length, kstime, logging=False):
"""
OFDM synchronization using PN Correlation and initial cross-correlation:
F. Tufvesson, O. Edfors, and M. Faulkner, "Time and Frequency Synchronization for OFDM using
PN-Sequency Preambles," IEEE Proc. VTC, 1999, pp. 2203-2207.
This implementation is meant to be a more robust version of the Schmidl and Cox receiver design.
By correlating against the preamble and using that as the input to the time-delayed correlation,
this circuit produces a very clean timing signal at the end of the preamble. The timing is
more accurate and does not have the problem associated with determining the timing from the
plateau structure in the Schmidl and Cox.
This implementation appears to require that the signal is received with a normalized power or signal
scalling factor to reduce ambiguities intorduced from partial correlation of the cyclic prefix and
the peak detection. A better peak detection block might fix this.
Also, the cross-correlation falls apart as the frequency offset gets larger and completely fails
when an integer offset is introduced. Another thing to look at.
"""
gr.hier_block2.__init__(
self,
"ofdm_sync_pnac",
gr.io_signature(1, 1, gr.sizeof_gr_complex), # Input signature
gr.io_signature2(2, 2, gr.sizeof_float,
gr.sizeof_char)) # Output signature
self.input = gr.add_const_cc(0)
symbol_length = fft_length + cp_length
# PN Sync with cross-correlation input
# cross-correlate with the known symbol
kstime = [k.conjugate() for k in kstime[0:fft_length // 2]]
kstime.reverse()
self.crosscorr_filter = gr.fir_filter_ccc(1, kstime)
# Create a delay line
self.delay = gr.delay(gr.sizeof_gr_complex, fft_length / 2)
# Correlation from ML Sync
self.conjg = gr.conjugate_cc()
self.corr = gr.multiply_cc()
# Create a moving sum filter for the input
self.mag = gr.complex_to_mag_squared()
movingsum_taps = (fft_length // 1) * [
1.0,
]
self.power = gr.fir_filter_fff(1, movingsum_taps)
# Get magnitude (peaks) and angle (phase/freq error)
self.c2mag = gr.complex_to_mag_squared()
self.angle = gr.complex_to_arg()
self.compare = gr.sub_ff()
self.sample_and_hold = gr.sample_and_hold_ff()
#ML measurements input to sampler block and detect
self.threshold = gr.threshold_ff(
0, 0, 0) # threshold detection might need to be tweaked
self.peaks = gr.float_to_char()
self.connect(self, self.input)
# Cross-correlate input signal with known preamble
self.connect(self.input, self.crosscorr_filter)
# use the output of the cross-correlation as input time-shifted correlation
self.connect(self.crosscorr_filter, self.delay)
self.connect(self.crosscorr_filter, (self.corr, 0))
self.connect(self.delay, self.conjg)
self.connect(self.conjg, (self.corr, 1))
self.connect(self.corr, self.c2mag)
self.connect(self.corr, self.angle)
self.connect(self.angle, (self.sample_and_hold, 0))
# Get the power of the input signal to compare against the correlation
self.connect(self.crosscorr_filter, self.mag, self.power)
# Compare the power to the correlator output to determine timing peak
# When the peak occurs, it peaks above zero, so the thresholder detects this
self.connect(self.c2mag, (self.compare, 0))
self.connect(self.power, (self.compare, 1))
self.connect(self.compare, self.threshold)
self.connect(self.threshold, self.peaks, (self.sample_and_hold, 1))
# Set output signals
# Output 0: fine frequency correction value
# Output 1: timing signal
self.connect(self.sample_and_hold, (self, 0))
self.connect(self.peaks, (self, 1))
if logging:
self.connect(
self.compare,
gr.file_sink(gr.sizeof_float, "ofdm_sync_pnac-compare_f.dat"))
self.connect(
self.c2mag,
gr.file_sink(gr.sizeof_float, "ofdm_sync_pnac-theta_f.dat"))
self.connect(
self.power,
gr.file_sink(gr.sizeof_float,
"ofdm_sync_pnac-inputpower_f.dat"))
self.connect(
self.angle,
gr.file_sink(gr.sizeof_float, "ofdm_sync_pnac-epsilon_f.dat"))
self.connect(
self.threshold,
gr.file_sink(gr.sizeof_float,
"ofdm_sync_pnac-threshold_f.dat"))
self.connect(
self.peaks,
gr.file_sink(gr.sizeof_char, "ofdm_sync_pnac-peaks_b.dat"))
self.connect(
self.sample_and_hold,
gr.file_sink(gr.sizeof_float,
"ofdm_sync_pnac-sample_and_hold_f.dat"))
self.connect(
self.input,
gr.file_sink(gr.sizeof_gr_complex,
"ofdm_sync_pnac-input_c.dat"))
def __init__(self, freq_corr=0, N_id_1=134, avg_frames=1, N_id_2=0, decim=16): grc_wxgui.top_block_gui.__init__(self, title="Sss Corr2 Gui") _icon_path = "/usr/share/icons/hicolor/32x32/apps/gnuradio-grc.png" self.SetIcon(wx.Icon(_icon_path, wx.BITMAP_TYPE_ANY)) ################################################## # Parameters ################################################## self.freq_corr = freq_corr self.N_id_1 = N_id_1 self.avg_frames = avg_frames self.N_id_2 = N_id_2 self.decim = decim ################################################## # Variables ################################################## self.vec_half_frame = vec_half_frame = 30720*5/decim self.samp_rate = samp_rate = 30720e3/decim self.rot = rot = 0 self.noise_level = noise_level = 0 self.fft_size = fft_size = 2048/decim ################################################## # Blocks ################################################## _rot_sizer = wx.BoxSizer(wx.VERTICAL) self._rot_text_box = forms.text_box( parent=self.GetWin(), sizer=_rot_sizer, value=self.rot, callback=self.set_rot, label='rot', converter=forms.float_converter(), proportion=0, ) self._rot_slider = forms.slider( parent=self.GetWin(), sizer=_rot_sizer, value=self.rot, callback=self.set_rot, minimum=0, maximum=1, num_steps=100, style=wx.SL_HORIZONTAL, cast=float, proportion=1, ) self.Add(_rot_sizer) _noise_level_sizer = wx.BoxSizer(wx.VERTICAL) self._noise_level_text_box = forms.text_box( parent=self.GetWin(), sizer=_noise_level_sizer, value=self.noise_level, callback=self.set_noise_level, label='noise_level', converter=forms.float_converter(), proportion=0, ) self._noise_level_slider = forms.slider( parent=self.GetWin(), sizer=_noise_level_sizer, value=self.noise_level, callback=self.set_noise_level, minimum=0, maximum=10, num_steps=100, style=wx.SL_HORIZONTAL, cast=float, proportion=1, ) self.Add(_noise_level_sizer) self.wxgui_scopesink2_0 = scopesink2.scope_sink_c( self.GetWin(), title="Scope Plot", sample_rate=samp_rate, v_scale=0, v_offset=0, t_scale=0, ac_couple=False, xy_mode=False, num_inputs=2, trig_mode=gr.gr_TRIG_MODE_AUTO, y_axis_label="Counts", ) self.Add(self.wxgui_scopesink2_0.win) self.gr_vector_to_stream_1 = gr.vector_to_stream(gr.sizeof_gr_complex*1, fft_size) self.gr_vector_to_stream_0_2 = gr.vector_to_stream(gr.sizeof_gr_complex*1, fft_size) self.gr_vector_to_stream_0_1 = gr.vector_to_stream(gr.sizeof_gr_complex*1, fft_size) self.gr_vector_to_stream_0_0 = gr.vector_to_stream(gr.sizeof_gr_complex*1, fft_size) self.gr_vector_to_stream_0 = gr.vector_to_stream(gr.sizeof_float*1, fft_size) self.gr_vector_source_x_0_0_0 = gr.vector_source_c((gen_pss_fd(N_id_2, fft_size, False).get_data()), True, fft_size) self.gr_vector_source_x_0_0 = gr.vector_source_c((gen_pss_fd(N_id_2, fft_size, False).get_data()), True, fft_size) self.gr_vector_source_x_0 = gr.vector_source_c((gen_sss_fd( N_id_1, N_id_2, fft_size).get_sss(True)), True, fft_size) self.gr_stream_to_vector_0_0 = gr.stream_to_vector(gr.sizeof_gr_complex*1, fft_size) self.gr_stream_to_vector_0 = gr.stream_to_vector(gr.sizeof_gr_complex*1, fft_size) self.gr_repeat_0 = gr.repeat(gr.sizeof_float*1, fft_size) self.gr_null_sink_0_0 = gr.null_sink(gr.sizeof_gr_complex*1) self.gr_null_sink_0 = gr.null_sink(gr.sizeof_gr_complex*1) self.gr_noise_source_x_0 = gr.noise_source_c(gr.GR_GAUSSIAN, noise_level, 0) self.gr_multiply_xx_1 = gr.multiply_vcc(1) self.gr_multiply_xx_0 = gr.multiply_vcc(fft_size) self.gr_multiply_const_vxx_1 = gr.multiply_const_vcc((1/1500., )) self.gr_multiply_const_vxx_0 = gr.multiply_const_vcc((exp(rot*2*numpy.pi*1j), )) self.gr_interleave_0 = gr.interleave(gr.sizeof_gr_complex*fft_size) self.gr_integrate_xx_0 = gr.integrate_ff(fft_size) self.gr_float_to_complex_0_0 = gr.float_to_complex(1) self.gr_float_to_complex_0 = gr.float_to_complex(1) self.gr_file_source_0 = gr.file_source(gr.sizeof_gr_complex*1, "/home/user/git/gr-lte/gr-lte/test/foo_pss0_sss_in.cfile", True) self.gr_fft_vxx_1 = gr.fft_vcc(fft_size, False, (window.blackmanharris(1024)), True, 1) self.gr_fft_vxx_0 = gr.fft_vcc(fft_size, True, (window.blackmanharris(1024)), True, 1) self.gr_divide_xx_0_1 = gr.divide_cc(1) self.gr_divide_xx_0_0 = gr.divide_ff(1) self.gr_divide_xx_0 = gr.divide_cc(1) self.gr_deinterleave_0 = gr.deinterleave(gr.sizeof_gr_complex*fft_size) self.gr_conjugate_cc_1 = gr.conjugate_cc() self.gr_conjugate_cc_0 = gr.conjugate_cc() self.gr_complex_to_mag_squared_0_0 = gr.complex_to_mag_squared(1) self.gr_complex_to_mag_squared_0 = gr.complex_to_mag_squared(fft_size) self.gr_add_xx_0 = gr.add_vcc(1) self.gr_add_const_vxx_0 = gr.add_const_vff((1, )) self.const_source_x_0_0 = gr.sig_source_f(0, gr.GR_CONST_WAVE, 0, 0, 0) self.const_source_x_0 = gr.sig_source_f(0, gr.GR_CONST_WAVE, 0, 0, 0) ################################################## # Connections ################################################## self.connect((self.gr_file_source_0, 0), (self.gr_stream_to_vector_0, 0)) self.connect((self.gr_conjugate_cc_0, 0), (self.gr_stream_to_vector_0_0, 0)) self.connect((self.gr_stream_to_vector_0_0, 0), (self.gr_multiply_xx_0, 1)) self.connect((self.gr_deinterleave_0, 0), (self.gr_multiply_xx_0, 0)) self.connect((self.gr_multiply_xx_0, 0), (self.gr_complex_to_mag_squared_0, 0)) self.connect((self.gr_complex_to_mag_squared_0, 0), (self.gr_vector_to_stream_0, 0)) self.connect((self.gr_vector_to_stream_0, 0), (self.gr_integrate_xx_0, 0)) self.connect((self.gr_integrate_xx_0, 0), (self.gr_repeat_0, 0)) self.connect((self.gr_multiply_xx_0, 0), (self.gr_vector_to_stream_0_0, 0)) self.connect((self.gr_vector_to_stream_0_1, 0), (self.gr_multiply_xx_1, 1)) self.connect((self.gr_divide_xx_0, 0), (self.gr_multiply_xx_1, 0)) self.connect((self.gr_float_to_complex_0, 0), (self.gr_divide_xx_0, 1)) self.connect((self.gr_conjugate_cc_1, 0), (self.gr_divide_xx_0, 0)) self.connect((self.gr_deinterleave_0, 1), (self.gr_vector_to_stream_0_1, 0)) self.connect((self.gr_repeat_0, 0), (self.gr_float_to_complex_0, 0)) self.connect((self.const_source_x_0, 0), (self.gr_float_to_complex_0, 1)) self.connect((self.gr_vector_to_stream_0_0, 0), (self.gr_conjugate_cc_1, 0)) self.connect((self.gr_vector_to_stream_0_0, 0), (self.gr_complex_to_mag_squared_0_0, 0)) self.connect((self.gr_complex_to_mag_squared_0_0, 0), (self.gr_divide_xx_0_0, 0)) self.connect((self.gr_repeat_0, 0), (self.gr_divide_xx_0_0, 1)) self.connect((self.gr_divide_xx_0_0, 0), (self.gr_add_const_vxx_0, 0)) self.connect((self.gr_add_const_vxx_0, 0), (self.gr_float_to_complex_0_0, 0)) self.connect((self.const_source_x_0_0, 0), (self.gr_float_to_complex_0_0, 1)) self.connect((self.gr_float_to_complex_0_0, 0), (self.gr_divide_xx_0_1, 1)) self.connect((self.gr_multiply_xx_1, 0), (self.gr_divide_xx_0_1, 0)) self.connect((self.gr_stream_to_vector_0, 0), (self.gr_fft_vxx_0, 0)) self.connect((self.gr_fft_vxx_0, 0), (self.gr_deinterleave_0, 0)) self.connect((self.gr_vector_to_stream_0_2, 0), (self.gr_conjugate_cc_0, 0)) self.connect((self.gr_divide_xx_0_1, 0), (self.gr_null_sink_0, 0)) self.connect((self.gr_vector_source_x_0, 0), (self.gr_interleave_0, 1)) self.connect((self.gr_interleave_0, 0), (self.gr_fft_vxx_1, 0)) self.connect((self.gr_fft_vxx_1, 0), (self.gr_vector_to_stream_1, 0)) self.connect((self.gr_vector_source_x_0_0, 0), (self.gr_interleave_0, 0)) self.connect((self.gr_vector_source_x_0_0_0, 0), (self.gr_vector_to_stream_0_2, 0)) self.connect((self.gr_noise_source_x_0, 0), (self.gr_add_xx_0, 1)) self.connect((self.gr_vector_to_stream_1, 0), (self.gr_add_xx_0, 0)) self.connect((self.gr_add_xx_0, 0), (self.gr_multiply_const_vxx_0, 0)) self.connect((self.gr_vector_to_stream_0_1, 0), (self.gr_multiply_const_vxx_1, 0)) self.connect((self.gr_multiply_const_vxx_0, 0), (self.gr_null_sink_0_0, 0)) self.connect((self.gr_multiply_const_vxx_1, 0), (self.wxgui_scopesink2_0, 1)) self.connect((self.gr_divide_xx_0_1, 0), (self.wxgui_scopesink2_0, 0))
def __init__(self, fft_length, cp_length, kstime, logging=False):
"""
OFDM synchronization using PN Correlation and initial cross-correlation:
F. Tufvesson, O. Edfors, and M. Faulkner, "Time and Frequency Synchronization for OFDM using
PN-Sequency Preambles," IEEE Proc. VTC, 1999, pp. 2203-2207.
This implementation is meant to be a more robust version of the Schmidl and Cox receiver design.
By correlating against the preamble and using that as the input to the time-delayed correlation,
this circuit produces a very clean timing signal at the end of the preamble. The timing is
more accurate and does not have the problem associated with determining the timing from the
plateau structure in the Schmidl and Cox.
This implementation appears to require that the signal is received with a normalized power or signal
scalling factor to reduce ambiguities intorduced from partial correlation of the cyclic prefix and
the peak detection. A better peak detection block might fix this.
Also, the cross-correlation falls apart as the frequency offset gets larger and completely fails
when an integer offset is introduced. Another thing to look at.
"""
gr.hier_block2.__init__(self, "ofdm_sync_pnac",
gr.io_signature(1, 1, gr.sizeof_gr_complex), # Input signature
gr.io_signature2(2, 2, gr.sizeof_float, gr.sizeof_char)) # Output signature
self.input = gr.add_const_cc(0)
symbol_length = fft_length + cp_length
# PN Sync with cross-correlation input
# cross-correlate with the known symbol
kstime = [k.conjugate() for k in kstime[0:fft_length//2]]
kstime.reverse()
self.crosscorr_filter = filter.fir_filter_ccc(1, kstime)
# Create a delay line
self.delay = gr.delay(gr.sizeof_gr_complex, fft_length/2)
# Correlation from ML Sync
self.conjg = gr.conjugate_cc();
self.corr = gr.multiply_cc();
# Create a moving sum filter for the input
self.mag = gr.complex_to_mag_squared()
movingsum_taps = (fft_length//1)*[1.0,]
self.power = filter.fir_filter_fff(1,movingsum_taps)
# Get magnitude (peaks) and angle (phase/freq error)
self.c2mag = gr.complex_to_mag_squared()
self.angle = gr.complex_to_arg()
self.compare = gr.sub_ff()
self.sample_and_hold = gr.sample_and_hold_ff()
#ML measurements input to sampler block and detect
self.threshold = gr.threshold_ff(0,0,0) # threshold detection might need to be tweaked
self.peaks = gr.float_to_char()
self.connect(self, self.input)
# Cross-correlate input signal with known preamble
self.connect(self.input, self.crosscorr_filter)
# use the output of the cross-correlation as input time-shifted correlation
self.connect(self.crosscorr_filter, self.delay)
self.connect(self.crosscorr_filter, (self.corr,0))
self.connect(self.delay, self.conjg)
self.connect(self.conjg, (self.corr,1))
self.connect(self.corr, self.c2mag)
self.connect(self.corr, self.angle)
self.connect(self.angle, (self.sample_and_hold,0))
# Get the power of the input signal to compare against the correlation
self.connect(self.crosscorr_filter, self.mag, self.power)
# Compare the power to the correlator output to determine timing peak
# When the peak occurs, it peaks above zero, so the thresholder detects this
self.connect(self.c2mag, (self.compare,0))
self.connect(self.power, (self.compare,1))
self.connect(self.compare, self.threshold)
self.connect(self.threshold, self.peaks, (self.sample_and_hold,1))
# Set output signals
# Output 0: fine frequency correction value
# Output 1: timing signal
self.connect(self.sample_and_hold, (self,0))
self.connect(self.peaks, (self,1))
if logging:
self.connect(self.compare, gr.file_sink(gr.sizeof_float, "ofdm_sync_pnac-compare_f.dat"))
self.connect(self.c2mag, gr.file_sink(gr.sizeof_float, "ofdm_sync_pnac-theta_f.dat"))
self.connect(self.power, gr.file_sink(gr.sizeof_float, "ofdm_sync_pnac-inputpower_f.dat"))
self.connect(self.angle, gr.file_sink(gr.sizeof_float, "ofdm_sync_pnac-epsilon_f.dat"))
self.connect(self.threshold, gr.file_sink(gr.sizeof_float, "ofdm_sync_pnac-threshold_f.dat"))
self.connect(self.peaks, gr.file_sink(gr.sizeof_char, "ofdm_sync_pnac-peaks_b.dat"))
self.connect(self.sample_and_hold, gr.file_sink(gr.sizeof_float, "ofdm_sync_pnac-sample_and_hold_f.dat"))
self.connect(self.input, gr.file_sink(gr.sizeof_gr_complex, "ofdm_sync_pnac-input_c.dat"))
def __init__(self, fft_length, cp_length, logging=False):
"""
OFDM synchronization using PN Correlation:
T. M. Schmidl and D. C. Cox, "Robust Frequency and Timing
Synchonization for OFDM," IEEE Trans. Communications, vol. 45,
no. 12, 1997.
"""
gr.hier_block2.__init__(self, "ofdm_sync_pn",
gr.io_signature(1, 1, gr.sizeof_gr_complex), # Input signature
gr.io_signature2(2, 2, gr.sizeof_float, gr.sizeof_char)) # Output signature
self.input = gr.add_const_cc(0)
# PN Sync
# Create a delay line
self.delay = gr.delay(gr.sizeof_gr_complex, fft_length/2)
# Correlation from ML Sync
self.conjg = gr.conjugate_cc();
self.corr = gr.multiply_cc();
# Create a moving sum filter for the corr output
if 1:
moving_sum_taps = [1.0 for i in range(fft_length//2)]
self.moving_sum_filter = gr.fir_filter_ccf(1,moving_sum_taps)
else:
moving_sum_taps = [complex(1.0,0.0) for i in range(fft_length//2)]
self.moving_sum_filter = gr.fft_filter_ccc(1,moving_sum_taps)
# Create a moving sum filter for the input
self.inputmag2 = gr.complex_to_mag_squared()
movingsum2_taps = [1.0 for i in range(fft_length//2)]
if 1:
self.inputmovingsum = gr.fir_filter_fff(1,movingsum2_taps)
else:
self.inputmovingsum = gr.fft_filter_fff(1,movingsum2_taps)
self.square = gr.multiply_ff()
self.normalize = gr.divide_ff()
# Get magnitude (peaks) and angle (phase/freq error)
self.c2mag = gr.complex_to_mag_squared()
self.angle = gr.complex_to_arg()
self.sample_and_hold = gr.sample_and_hold_ff()
#ML measurements input to sampler block and detect
#self.sub1 = gr.add_const_ff(-1)
self.sub1 = gr.add_const_ff(0)
self.pk_detect = gr.peak_detector_fb(0.20, 0.20, 30, 0.001)
#self.pk_detect = gr.peak_detector2_fb(9)
self.connect(self, self.input)
# Calculate the frequency offset from the correlation of the preamble
self.connect(self.input, self.delay)
self.connect(self.input, (self.corr,0))
self.connect(self.delay, self.conjg)
self.connect(self.conjg, (self.corr,1))
self.connect(self.corr, self.moving_sum_filter)
self.connect(self.moving_sum_filter, self.c2mag)
self.connect(self.moving_sum_filter, self.angle)
self.connect(self.angle, (self.sample_and_hold,0))
# Get the power of the input signal to normalize the output of the correlation
self.connect(self.input, self.inputmag2, self.inputmovingsum)
self.connect(self.inputmovingsum, (self.square,0))
self.connect(self.inputmovingsum, (self.square,1))
self.connect(self.square, (self.normalize,1))
self.connect(self.c2mag, (self.normalize,0))
# Create a moving sum filter for the corr output
matched_filter_taps = [1.0/cp_length for i in range(cp_length)]
self.matched_filter = gr.fir_filter_fff(1,matched_filter_taps)
self.connect(self.normalize, self.matched_filter)
self.connect(self.matched_filter, self.sub1, self.pk_detect)
#self.connect(self.matched_filter, self.pk_detect)
self.connect(self.pk_detect, (self.sample_and_hold,1))
# Set output signals
# Output 0: fine frequency correction value
# Output 1: timing signal
self.connect(self.sample_and_hold, (self,0))
self.connect(self.pk_detect, (self,1))
if logging:
self.connect(self.matched_filter, gr.file_sink(gr.sizeof_float, "ofdm_sync_pn-mf_f.dat"))
self.connect(self.normalize, gr.file_sink(gr.sizeof_float, "ofdm_sync_pn-theta_f.dat"))
self.connect(self.angle, gr.file_sink(gr.sizeof_float, "ofdm_sync_pn-epsilon_f.dat"))
self.connect(self.pk_detect, gr.file_sink(gr.sizeof_char, "ofdm_sync_pn-peaks_b.dat"))
self.connect(self.sample_and_hold, gr.file_sink(gr.sizeof_float, "ofdm_sync_pn-sample_and_hold_f.dat"))
self.connect(self.input, gr.file_sink(gr.sizeof_gr_complex, "ofdm_sync_pn-input_c.dat"))
def __init__(self, options, hostname):
gr.top_block.__init__(self)
##################################################
# Variables
##################################################
window_size = options.window_size
sync_length = options.sync_length
gain = options.gain
freq = options.freq
samp_rate = options.bandwidth
self.uhd_usrp_source_0 = uhd.usrp_source(device_addr="", stream_args=uhd.stream_args(cpu_format="fc32", channels=range(1)))
self.uhd_usrp_source_0.set_samp_rate(samp_rate)
self.uhd_usrp_source_0.set_center_freq(freq, 0)
self.uhd_usrp_source_0.set_gain(gain, 0)
self.ieee802_1_ofdm_sync_short_0 = gr_ieee802_11.ofdm_sync_short(0.8, 80 * 80, 2, False)
self.ieee802_1_ofdm_sync_long_0 = gr_ieee802_11.ofdm_sync_long(sync_length, 100, False)
self.ieee802_1_ofdm_equalize_symbols_0 = gr_ieee802_11.ofdm_equalize_symbols(False)
self.ieee802_1_ofdm_decode_signal_0 = gr_ieee802_11.ofdm_decode_signal(False)
self.ieee802_1_ofdm_decode_mac_0 = gr_ieee802_11.ofdm_decode_mac(False)
self.ieee802_11_ofdm_parse_mac_0 = gr_ieee802_11.ofdm_parse_mac(True)
self.gr_stream_to_vector_0 = gr.stream_to_vector(gr.sizeof_gr_complex*1, 64)
self.gr_socket_pdu_0 = gr.socket_pdu("TCP_SERVER", hostname, str(options.PHYRXport), 10000)
self.gr_skiphead_0 = gr.skiphead(gr.sizeof_gr_complex*1, 20000000)
self.gr_multiply_xx_0 = gr.multiply_vcc(1)
self.gr_divide_xx_0 = gr.divide_ff(1)
self.gr_delay_0_0 = gr.delay(gr.sizeof_gr_complex*1, sync_length)
self.gr_delay_0 = gr.delay(gr.sizeof_gr_complex*1, 16)
self.gr_conjugate_cc_0 = gr.conjugate_cc()
self.gr_complex_to_mag_squared_0 = gr.complex_to_mag_squared(1)
self.gr_complex_to_mag_0 = gr.complex_to_mag(1)
self.fir_filter_xxx_0_0 = filter.fir_filter_ccf(1, ([1]*window_size))
self.fir_filter_xxx_0 = filter.fir_filter_fff(1, ([1]*window_size))
self.fft_vxx_0 = fft.fft_vcc(64, True, (), True, 1)
self.message_debug = gr.message_debug()
##################################################
# Connections
##################################################
self.connect((self.uhd_usrp_source_0, 0), (self.gr_skiphead_0, 0))
self.connect((self.gr_skiphead_0, 0), (self.gr_complex_to_mag_squared_0, 0))
self.connect((self.fir_filter_xxx_0, 0), (self.gr_divide_xx_0, 1))
self.connect((self.gr_complex_to_mag_squared_0, 0), (self.fir_filter_xxx_0, 0))
self.connect((self.gr_skiphead_0, 0), (self.gr_multiply_xx_0, 0))
self.connect((self.gr_conjugate_cc_0, 0), (self.gr_multiply_xx_0, 1))
self.connect((self.gr_complex_to_mag_0, 0), (self.gr_divide_xx_0, 0))
self.connect((self.gr_multiply_xx_0, 0), (self.fir_filter_xxx_0_0, 0))
self.connect((self.fir_filter_xxx_0_0, 0), (self.gr_complex_to_mag_0, 0))
self.connect((self.gr_skiphead_0, 0), (self.gr_delay_0, 0))
self.connect((self.gr_delay_0, 0), (self.gr_conjugate_cc_0, 0))
self.connect((self.fft_vxx_0, 0), (self.ieee802_1_ofdm_equalize_symbols_0, 0))
self.connect((self.ieee802_1_ofdm_equalize_symbols_0, 0), (self.ieee802_1_ofdm_decode_signal_0, 0))
self.connect((self.ieee802_1_ofdm_decode_signal_0, 0), (self.ieee802_1_ofdm_decode_mac_0, 0))
self.connect((self.ieee802_1_ofdm_sync_short_0, 0), (self.gr_delay_0_0, 0))
self.connect((self.gr_delay_0, 0), (self.ieee802_1_ofdm_sync_short_0, 0))
self.connect((self.gr_divide_xx_0, 0), (self.ieee802_1_ofdm_sync_short_0, 1))
self.connect((self.gr_delay_0_0, 0), (self.ieee802_1_ofdm_sync_long_0, 1))
self.connect((self.ieee802_1_ofdm_sync_short_0, 0), (self.ieee802_1_ofdm_sync_long_0, 0))
self.connect((self.ieee802_1_ofdm_sync_long_0, 0), (self.gr_stream_to_vector_0, 0))
self.connect((self.gr_stream_to_vector_0, 0), (self.fft_vxx_0, 0))
##################################################
# Asynch Message Connections
##################################################
self.msg_connect(self.ieee802_1_ofdm_decode_mac_0, "out", self.ieee802_11_ofdm_parse_mac_0, "in")
self.msg_connect(self.ieee802_11_ofdm_parse_mac_0, "out", self.gr_socket_pdu_0, "pdus")
def __init__(self, options):
gr.hier_block2.__init__(self, "fbmc_receive_path",
gr.io_signature(1,1,gr.sizeof_gr_complex),
gr.io_signature(0,0,0))
print "This is FBMC receive path 1x1"
common_options.defaults(options)
config = self.config = station_configuration()
config.data_subcarriers = dsubc = options.subcarriers
config.cp_length = 0
config.frame_data_blocks = options.data_blocks
config._verbose = options.verbose #TODO: update
config.fft_length = options.fft_length
config.dc_null = options.dc_null
config.training_data = default_block_header(dsubc,
config.fft_length,config.dc_null,options)
config.coding = options.coding
config.ber_window = options.ber_window
config.periodic_parts = 8
config.frame_id_blocks = 1 # FIXME
self._options = copy.copy(options) #FIXME: do we need this?
config.fbmc = options.fbmc
config.block_length = config.fft_length + config.cp_length
config.frame_data_part = config.frame_data_blocks + config.frame_id_blocks
config.frame_length = config.training_data.fbmc_no_preambles + 2*config.frame_data_part
config.postpro_frame_length = config.frame_data_part + \
config.training_data.no_pilotsyms
config.subcarriers = dsubc + \
config.training_data.pilot_subcarriers
config.virtual_subcarriers = config.fft_length - config.subcarriers - config.dc_null
total_subc = config.subcarriers
# check some bounds
if config.fft_length < config.subcarriers:
raise SystemError, "Subcarrier number must be less than FFT length"
if config.fft_length < config.cp_length:
raise SystemError, "Cyclic prefix length must be less than FFT length"
#self.input = gr.kludge_copy(gr.sizeof_gr_complex)
#self.connect( self, self.input )
self.input = self
self.ideal = options.ideal
self.ideal2 = options.ideal2
## Inner receiver
## Timing & Frequency Synchronization
## Channel estimation + Equalization
## Phase Tracking for sampling clock frequency offset correction
inner_receiver = self.inner_receiver = fbmc_inner_receiver( options, options.log )
self.connect( self.input, inner_receiver )
ofdm_blocks = ( inner_receiver, 2 )
frame_start = ( inner_receiver, 1 )
disp_ctf = ( inner_receiver, 0 )
#self.snr_est_preamble = ( inner_receiver, 3 )
#terminate_stream(self,snr_est_preamble)
disp_cfo = ( inner_receiver, 3 )
if self.ideal is False and self.ideal2 is False:
self.zmq_probe_freqoff = zeromq.pub_sink(gr.sizeof_float, 1, "tcp://*:5557")
self.connect(disp_cfo, self.zmq_probe_freqoff)
else:
self.connect(disp_cfo, blocks.null_sink(gr.sizeof_float))
# for ID decoder
used_id_bits = config.used_id_bits = 8 #TODO: constant in source code!
rep_id_bits = config.rep_id_bits = dsubc/used_id_bits #BPSK
if options.log:
print "rep_id_bits %d" % (rep_id_bits)
if dsubc % used_id_bits <> 0:
raise SystemError,"Data subcarriers need to be multiple of 10"
## Workaround to avoid periodic structure
seed(1)
whitener_pn = [randint(0,1) for i in range(used_id_bits*rep_id_bits)]
## NOTE!!! BIG HACK!!!
## first preamble ain't equalized ....
## for Milan's SNR estimator
## Outer Receiver
## Make new inner receiver compatible with old outer receiver
## FIXME: renew outer receiver
self.ctf = disp_ctf
#frame_sampler = ofdm_frame_sampler(options)
frame_sampler = fbmc_frame_sampler(options)
self.connect( ofdm_blocks, frame_sampler)
self.connect( frame_start, (frame_sampler,1) )
#
# ft = [0] * config.frame_length
# ft[0] = 1
#
# # The next block ensures that only complete frames find their way into
# # the old outer receiver. The dynamic frame start trigger is hence
# # replaced with a static one, fixed to the frame length.
#
# frame_sampler = ofdm.vector_sampler( gr.sizeof_gr_complex * total_subc,
# config.frame_length )
# self.symbol_output = blocks.vector_to_stream( gr.sizeof_gr_complex * total_subc,
# config.frame_length )
# delayed_frame_start = blocks.delay( gr.sizeof_char, config.frame_length - 1 )
# damn_static_frame_trigger = blocks.vector_source_b( ft, True )
#
# if options.enable_erasure_decision:
# frame_gate = vector_sampler(
# gr.sizeof_gr_complex * total_subc * config.frame_length, 1 )
# self.connect( ofdm_blocks, frame_sampler, frame_gate,
# self.symbol_output )
# else:
# self.connect( ofdm_blocks, frame_sampler, self.symbol_output )
#
# self.connect( frame_start, delayed_frame_start, ( frame_sampler, 1 ) )
if options.enable_erasure_decision:
frame_gate = frame_sampler.frame_gate
self.symbol_output = frame_sampler
orig_frame_start = frame_start
frame_start = (frame_sampler,1)
self.frame_trigger = frame_start
#terminate_stream(self, self.frame_trigger)
## Pilot block filter
pb_filt = self._pilot_block_filter = fbmc_pilot_block_filter()
self.connect(self.symbol_output,pb_filt)
self.connect(self.frame_trigger,(pb_filt,1))
self.frame_data_trigger = (pb_filt,1)
#self.symbol_output = pb_filt
#if options.log:
#log_to_file(self, pb_filt, "data/pb_filt_out.compl")
if config.fbmc:
pda_in = pb_filt
else:
## Pilot subcarrier filter
ps_filt = self._pilot_subcarrier_filter = pilot_subcarrier_filter()
self.connect(self.symbol_output,ps_filt)
if options.log:
log_to_file(self, ps_filt, "data/ps_filt_out.compl")
pda_in = ps_filt
## Workaround to avoid periodic structure
# for ID decoder
seed(1)
whitener_pn = [randint(0,1) for i in range(used_id_bits*rep_id_bits)]
if not options.enable_erasure_decision:
## ID Block Filter
# Filter ID block, skip data blocks
id_bfilt = self._id_block_filter = vector_sampler(
gr.sizeof_gr_complex * dsubc, 1 )
if not config.frame_id_blocks == 1:
raise SystemExit, "# ID Blocks > 1 not supported"
self.connect( pda_in , id_bfilt )
self.connect( self.frame_data_trigger, ( id_bfilt, 1 ) ) # trigger
#log_to_file( self, id_bfilt, "data/id_bfilt.compl" )
## ID Demapper and Decoder, soft decision
self.id_dec = self._id_decoder = ofdm.coded_bpsk_soft_decoder( dsubc,
used_id_bits, whitener_pn )
self.connect( id_bfilt, self.id_dec )
print "Using coded BPSK soft decoder for ID detection"
else: # options.enable_erasure_decision:
id_bfilt = self._id_block_filter = vector_sampler(
gr.sizeof_gr_complex * total_subc, config.frame_id_blocks )
id_bfilt_trig_delay = 0
for x in range( config.frame_length ):
if x in config.training_data.pilotsym_pos:
id_bfilt_trig_delay += 1
else:
break
print "Position of ID block within complete frame: %d" %(id_bfilt_trig_delay)
assert( id_bfilt_trig_delay > 0 ) # else not supported
id_bfilt_trig = blocks.delay( gr.sizeof_char, id_bfilt_trig_delay )
self.connect( ofdm_blocks, id_bfilt )
self.connect( orig_frame_start, id_bfilt_trig, ( id_bfilt, 1 ) )
self.id_dec = self._id_decoder = ofdm.coded_bpsk_soft_decoder( total_subc,
used_id_bits, whitener_pn, config.training_data.shifted_pilot_tones )
self.connect( id_bfilt, self.id_dec )
print "Using coded BPSK soft decoder for ID detection"
# The threshold block either returns 1.0 if the llr-value from the
# id decoder is below the threshold, else 0.0. Hence we convert this
# into chars, 0 and 1, and use it as trigger for the sampler.
min_llr = ( self.id_dec, 1 )
erasure_threshold = gr.threshold_ff( 10.0, 10.0, 0 ) # FIXME is it the optimal threshold?
erasure_dec = gr.float_to_char()
id_gate = vector_sampler( gr.sizeof_short, 1 )
ctf_gate = vector_sampler( gr.sizeof_float * total_subc, 1 )
self.connect( self.id_dec , id_gate )
self.connect( self.ctf, ctf_gate )
self.connect( min_llr, erasure_threshold, erasure_dec )
self.connect( erasure_dec, ( frame_gate, 1 ) )
self.connect( erasure_dec, ( id_gate, 1 ) )
self.connect( erasure_dec, ( ctf_gate, 1 ) )
self.id_dec = self._id_decoder = id_gate
self.ctf = ctf_gate
print "Erasure decision for IDs is enabled"
if options.log:
id_dec_f = gr.short_to_float()
self.connect(self.id_dec,id_dec_f)
log_to_file(self, id_dec_f, "data/id_dec_out.float")
if options.log:
log_to_file(self, id_bfilt, "data/id_blockfilter_out.compl")
# TODO: refactor names
if options.log:
map_src_f = gr.char_to_float(dsubc)
self.connect(map_src,map_src_f)
log_to_file(self, map_src_f, "data/map_src_out.float")
## Allocation Control
if options.static_allocation: #DEBUG
if options.coding:
mode = 1 # Coding mode 1-9
bitspermode = [0.5,1,1.5,2,3,4,4.5,5,6] # Information bits per mode
bitcount_vec = [(int)(config.data_subcarriers*config.frame_data_blocks*bitspermode[mode-1])]
bitloading = mode
else:
bitloading = 1
bitcount_vec = [config.data_subcarriers*config.frame_data_blocks*bitloading]
#bitcount_vec = [config.data_subcarriers*config.frame_data_blocks]
self.bitcount_src = blocks.vector_source_i(bitcount_vec,True,1)
# 0s for ID block, then data
#bitloading_vec = [0]*dsubc+[0]*(dsubc/2)+[2]*(dsubc/2)
bitloading_vec = [0]*dsubc+[bitloading]*dsubc
bitloading_src = blocks.vector_source_b(bitloading_vec,True,dsubc)
power_vec = [1]*config.data_subcarriers
power_src = blocks.vector_source_f(power_vec,True,dsubc)
else:
self.allocation_buffer = ofdm.allocation_buffer(config.data_subcarriers, config.frame_data_blocks, "tcp://"+options.tx_hostname+":3333",config.coding)
self.bitcount_src = (self.allocation_buffer,0)
bitloading_src = (self.allocation_buffer,1)
power_src = (self.allocation_buffer,2)
self.connect(self.id_dec, self.allocation_buffer)
if options.benchmarking:
self.allocation_buffer.set_allocation([4]*config.data_subcarriers,[1]*config.data_subcarriers)
if options.log:
log_to_file(self, self.bitcount_src, "data/bitcount_src_rx.int")
log_to_file(self, bitloading_src, "data/bitloading_src_rx.char")
log_to_file(self, power_src, "data/power_src_rx.cmplx")
log_to_file(self, self.id_dec, "data/id_dec_rx.short")
## Power Deallocator
pda = self._power_deallocator = multiply_frame_fc(config.frame_data_part, dsubc)
self.connect(pda_in,(pda,0))
self.connect(power_src,(pda,1))
## Demodulator
# if 0:
# ac_vector = [0.0+0.0j]*208
# ac_vector[0] = (2*10**(-0.452))
# ac_vector[3] = (10**(-0.651))
# ac_vector[7] = (10**(-1.151))
# csi_vector_inv=abs(numpy.fft.fft(numpy.sqrt(ac_vector)))**2
# dm_csi = numpy.fft.fftshift(csi_vector_inv) # TODO
dm_csi = [1]*dsubc # TODO
dm_csi = blocks.vector_source_f(dm_csi,True)
## Depuncturer
dp_trig = [0]*(config.frame_data_blocks/2)
dp_trig[0] = 1
dp_trig = blocks.vector_source_b(dp_trig,True) # TODO
if(options.coding):
fo=ofdm.fsm(1,2,[91,121])
if options.interleave:
int_object=trellis.interleaver(2000,666)
deinterlv = trellis.permutation(int_object.K(),int_object.DEINTER(),1,gr.sizeof_float)
demod = self._data_demodulator = generic_softdemapper_vcf(dsubc, config.frame_data_part, config.coding)
#self.connect(dm_csi,blocks.stream_to_vector(gr.sizeof_float,dsubc),(demod,2))
if(options.ideal):
self.connect(dm_csi,blocks.stream_to_vector(gr.sizeof_float,dsubc),(demod,2))
else:
dm_csi_filter = self.dm_csi_filter = filter.single_pole_iir_filter_ff(0.01,dsubc)
self.connect(self.ctf, self.dm_csi_filter,(demod,2))
#log_to_file(self, dm_csi_filter, "data/softs_csi.float")
#self.connect(dm_trig,(demod,3))
else:
demod = self._data_demodulator = generic_demapper_vcb(dsubc, config.frame_data_part)
if options.benchmarking:
# Do receiver benchmarking until the number of frames x symbols are collected
self.connect(pda,blocks.head(gr.sizeof_gr_complex*dsubc, options.N*config.frame_data_blocks),demod)
else:
self.connect(pda,demod)
self.connect(bitloading_src,(demod,1))
if(options.coding):
## Depuncturing
if not options.nopunct:
depuncturing = depuncture_ff(dsubc,0)
frametrigger_bitmap_filter = blocks.vector_source_b([1,0],True)
self.connect(bitloading_src,(depuncturing,1))
self.connect(dp_trig,(depuncturing,2))
## Decoding
chunkdivisor = int(numpy.ceil(config.frame_data_blocks/5.0))
print "Number of chunks at Viterbi decoder: ", chunkdivisor
decoding = self._data_decoder = ofdm.viterbi_combined_fb(fo,dsubc,-1,-1,2,chunkdivisor,[-1,-1,-1,1,1,-1,1,1],ofdm.TRELLIS_EUCLIDEAN)
if options.log and options.coding:
log_to_file(self, decoding, "data/decoded.char")
if not options.nopunct:
log_to_file(self, depuncturing, "data/vit_in.float")
if not options.nopunct:
if options.interleave:
self.connect(demod,deinterlv,depuncturing,decoding)
else:
self.connect(demod,depuncturing,decoding)
else:
self.connect(demod,decoding)
self.connect(self.bitcount_src, multiply_const_ii(1./chunkdivisor), (decoding,1))
if options.scatterplot or options.scatter_plot_before_phase_tracking:
if self.ideal2 is False:
scatter_vec_elem = self._scatter_vec_elem = ofdm.vector_element(dsubc,40)
scatter_s2v = self._scatter_s2v = blocks.stream_to_vector(gr.sizeof_gr_complex,config.frame_data_blocks)
scatter_id_filt = skip(gr.sizeof_gr_complex*dsubc,config.frame_data_blocks)
scatter_id_filt.skip_call(0)
scatter_trig = [0]*config.frame_data_part
scatter_trig[0] = 1
scatter_trig = blocks.vector_source_b(scatter_trig,True)
self.connect(scatter_trig,(scatter_id_filt,1))
self.connect(scatter_vec_elem,scatter_s2v)
if not options.scatter_plot_before_phase_tracking:
print "Enabling Scatterplot for data subcarriers"
self.connect(pda,scatter_id_filt,scatter_vec_elem)
# Work on this
#scatter_sink = ofdm.scatterplot_sink(dsubc)
#self.connect(pda,scatter_sink)
#self.connect(map_src,(scatter_sink,1))
#self.connect(dm_trig,(scatter_sink,2))
#print "Enabled scatterplot gui interface"
self.zmq_probe_scatter = zeromq.pub_sink(gr.sizeof_gr_complex,config.frame_data_blocks, "tcp://*:5560")
self.connect(scatter_s2v, blocks.keep_one_in_n(gr.sizeof_gr_complex*config.frame_data_blocks,20), self.zmq_probe_scatter)
else:
print "Enabling Scatterplot for data before phase tracking"
inner_rx = inner_receiver.before_phase_tracking
#scatter_sink2 = ofdm.scatterplot_sink(dsubc,"phase_tracking")
op = copy.copy(options)
op.enable_erasure_decision = False
new_framesampler = ofdm_frame_sampler(op)
self.connect( inner_rx, new_framesampler )
self.connect( orig_frame_start, (new_framesampler,1) )
new_ps_filter = pilot_subcarrier_filter()
new_pb_filter = fbmc_pilot_block_filter()
self.connect( (new_framesampler,1), (new_pb_filter,1) )
self.connect( new_framesampler, new_pb_filter,
new_ps_filter, scatter_id_filt, scatter_vec_elem )
#self.connect( new_ps_filter, scatter_sink2 )
#self.connect( map_src, (scatter_sink2,1))
#self.connect( dm_trig, (scatter_sink2,2))
if options.log:
if(options.coding):
log_to_file(self, demod, "data/data_stream_out.float")
else:
data_f = gr.char_to_float()
self.connect(demod,data_f)
log_to_file(self, data_f, "data/data_stream_out.float")
if options.sfo_feedback:
used_id_bits = 8
rep_id_bits = config.data_subcarriers/used_id_bits
seed(1)
whitener_pn = [randint(0,1) for i in range(used_id_bits*rep_id_bits)]
id_enc = ofdm.repetition_encoder_sb(used_id_bits,rep_id_bits,whitener_pn)
self.connect( self.id_dec, id_enc )
id_mod = ofdm_bpsk_modulator(dsubc)
self.connect( id_enc, id_mod )
id_mod_conj = gr.conjugate_cc(dsubc)
self.connect( id_mod, id_mod_conj )
id_mult = blocks.multiply_vcc(dsubc)
self.connect( id_bfilt, ( id_mult,0) )
self.connect( id_mod_conj, ( id_mult,1) )
# id_mult_avg = filter.single_pole_iir_filter_cc(0.01,dsubc)
# self.connect( id_mult, id_mult_avg )
id_phase = gr.complex_to_arg(dsubc)
self.connect( id_mult, id_phase )
log_to_file( self, id_phase, "data/id_phase.float" )
est=ofdm.LS_estimator_straight_slope(dsubc)
self.connect(id_phase,est)
slope=blocks.multiply_const_ff(1e6/2/3.14159265)
self.connect( (est,0), slope )
log_to_file( self, slope, "data/slope.float" )
log_to_file( self, (est,1), "data/offset.float" )
# ------------------------------------------------------------------------ #
# Display some information about the setup
if config._verbose:
self._print_verbage()
## debug logging ##
if options.log:
# log_to_file(self,self.ofdm_symbols,"data/unequalized_rx_ofdm_symbols.compl")
# log_to_file(self,self.ofdm_symbols,"data/unequalized_rx_ofdm_symbols.float",mag=True)
fftlen = 256
my_window = window.hamming(fftlen) #.blackmanharris(fftlen)
rxs_sampler = vector_sampler(gr.sizeof_gr_complex,fftlen)
rxs_sampler_vect = concatenate([[1],[0]*49])
rxs_trigger = blocks.vector_source_b(rxs_sampler_vect.tolist(),True)
rxs_window = blocks.multiply_const_vcc(my_window)
rxs_spectrum = gr.fft_vcc(fftlen,True,[],True)
rxs_mag = gr.complex_to_mag(fftlen)
rxs_avg = filter.single_pole_iir_filter_ff(0.01,fftlen)
#rxs_logdb = blocks.nlog10_ff(20.0,fftlen,-20*log10(fftlen))
rxs_logdb = gr.kludge_copy( gr.sizeof_float * fftlen )
rxs_decimate_rate = gr.keep_one_in_n(gr.sizeof_float*fftlen,1)
self.connect(rxs_trigger,(rxs_sampler,1))
self.connect(self.input,rxs_sampler,rxs_window,
rxs_spectrum,rxs_mag,rxs_avg,rxs_logdb, rxs_decimate_rate)
log_to_file( self, rxs_decimate_rate, "data/psd_input.float" )
#output branches
self.publish_rx_performance_measure()
def conjugate_cc(N):
op = gr.conjugate_cc()
tb = helper(N, op, gr.sizeof_gr_complex, gr.sizeof_gr_complex, 1, 1)
return tb
def __init__(self, fft_length, cp_length, logging=False):
"""
OFDM synchronization using PN Correlation:
T. M. Schmidl and D. C. Cox, "Robust Frequency and Timing
Synchonization for OFDM," IEEE Trans. Communications, vol. 45,
no. 12, 1997.
"""
gr.hier_block2.__init__(self, "ofdm_sync_pn",
gr.io_signature(1, 1, gr.sizeof_gr_complex), # Input signature
gr.io_signature2(2, 2, gr.sizeof_float, gr.sizeof_char)) # Output signature
self.input = gr.add_const_cc(0)
# PN Sync
# Create a delay line
self.delay = gr.delay(gr.sizeof_gr_complex, fft_length/2)
# Correlation from ML Sync
self.conjg = gr.conjugate_cc();
self.corr = gr.multiply_cc();
# Create a moving sum filter for the corr output
if 1:
moving_sum_taps = [1.0 for i in range(fft_length//2)]
self.moving_sum_filter = gr.fir_filter_ccf(1,moving_sum_taps)
else:
moving_sum_taps = [complex(1.0,0.0) for i in range(fft_length//2)]
self.moving_sum_filter = gr.fft_filter_ccc(1,moving_sum_taps)
# Create a moving sum filter for the input
self.inputmag2 = gr.complex_to_mag_squared()
# Modified by Yong (12.06.27)
#movingsum2_taps = [1.0 for i in range(fft_length//2)]
movingsum2_taps = [0.5 for i in range(fft_length)]
if 1:
self.inputmovingsum = gr.fir_filter_fff(1,movingsum2_taps)
else:
self.inputmovingsum = gr.fft_filter_fff(1,movingsum2_taps)
self.square = gr.multiply_ff()
self.normalize = gr.divide_ff()
# Get magnitude (peaks) and angle (phase/freq error)
self.c2mag = gr.complex_to_mag_squared()
self.angle = gr.complex_to_arg()
self.sample_and_hold = gr.sample_and_hold_ff()
#ML measurements input to sampler block and detect
self.sub1 = gr.add_const_ff(-1)
self.pk_detect = gr.peak_detector_fb(0.20, 0.20, 30, 0.001)
#self.pk_detect = gr.peak_detector2_fb(9)
self.connect(self, self.input)
# Calculate the frequency offset from the correlation of the preamble
self.connect(self.input, self.delay)
self.connect(self.input, (self.corr,0))
self.connect(self.delay, self.conjg)
self.connect(self.conjg, (self.corr,1))
self.connect(self.corr, self.moving_sum_filter)
self.connect(self.moving_sum_filter, self.c2mag)
self.connect(self.moving_sum_filter, self.angle)
self.connect(self.angle, (self.sample_and_hold,0))
# Get the power of the input signal to normalize the output of the correlation
self.connect(self.input, self.inputmag2, self.inputmovingsum)
self.connect(self.inputmovingsum, (self.square,0))
self.connect(self.inputmovingsum, (self.square,1))
self.connect(self.square, (self.normalize,1))
self.connect(self.c2mag, (self.normalize,0))
# Create a moving sum filter for the corr output
matched_filter_taps = [1.0/cp_length for i in range(cp_length)]
self.matched_filter = gr.fir_filter_fff(1,matched_filter_taps)
self.connect(self.normalize, self.matched_filter)
self.connect(self.matched_filter, self.sub1, self.pk_detect)
#self.connect(self.matched_filter, self.pk_detect)
self.connect(self.pk_detect, (self.sample_and_hold,1))
# Set output signals
# Output 0: fine frequency correction value
# Output 1: timing signal
self.connect(self.sample_and_hold, (self,0))
self.connect(self.pk_detect, (self,1))
if logging:
self.connect(self.matched_filter, gr.file_sink(gr.sizeof_float, "ofdm_sync_pn-mf_f.dat"))
self.connect(self.c2mag, gr.file_sink(gr.sizeof_float, "ofdm_sync_pn-nominator_f.dat"))
self.connect(self.square, gr.file_sink(gr.sizeof_float, "ofdm_sync_pn-denominator_f.dat"))
self.connect(self.normalize, gr.file_sink(gr.sizeof_float, "ofdm_sync_pn-theta_f.dat"))
self.connect(self.angle, gr.file_sink(gr.sizeof_float, "ofdm_sync_pn-epsilon_f.dat"))
self.connect(self.pk_detect, gr.file_sink(gr.sizeof_char, "ofdm_sync_pn-peaks_b.dat"))
self.connect(self.sample_and_hold, gr.file_sink(gr.sizeof_float, "ofdm_sync_pn-sample_and_hold_f.dat"))
self.connect(self.input, gr.file_sink(gr.sizeof_gr_complex, "ofdm_sync_pn-input_c.dat"))
def __init__(self, mode, debug=False): """ OFDM time and coarse frequency synchronisation for DAB @param mode DAB mode (1-4) @param debug if True: write data streams out to files """ if mode<1 or mode>4: raise ValueError, "Invalid DAB mode: "+str(mode)+" (modes 1-4 exist)" # get the correct DAB parameters dp = parameters.dab_parameters(mode) rp = parameters.receiver_parameters(mode) gr.hier_block2.__init__(self,"ofdm_sync_dab", gr.io_signature(1, 1, gr.sizeof_gr_complex), # input signature gr.io_signature2(2, 2, gr.sizeof_gr_complex, gr.sizeof_char)) # output signature # workaround for a problem that prevents connecting more than one block directly (see trac ticket #161) self.input = gr.kludge_copy(gr.sizeof_gr_complex) self.connect(self, self.input) # # null-symbol detection # # (outsourced to detect_zero.py) self.ns_detect = detect_null.detect_null(dp.ns_length, debug) self.connect(self.input, self.ns_detect) # # fine frequency synchronisation # # the code for fine frequency synchronisation is adapted from # ofdm_sync_ml.py; it abuses the cyclic prefix to find the fine # frequency error, as suggested in "ML Estimation of Timing and # Frequency Offset in OFDM Systems", by Jan-Jaap van de Beek, # Magnus Sandell, Per Ola Börjesson, see # http://www.sm.luth.se/csee/sp/research/report/bsb96r.html self.ffs_delay = gr.delay(gr.sizeof_gr_complex, dp.fft_length) self.ffs_conj = gr.conjugate_cc() self.ffs_mult = gr.multiply_cc() # self.ffs_moving_sum = gr.fir_filter_ccf(1, [1]*dp.cp_length) self.ffs_moving_sum = dab_swig.moving_sum_cc(dp.cp_length) self.ffs_angle = gr.complex_to_arg() self.ffs_angle_scale = gr.multiply_const_ff(1./dp.fft_length) self.ffs_delay_sample_and_hold = gr.delay(gr.sizeof_char, dp.symbol_length) # sample the value at the end of the symbol .. self.ffs_sample_and_hold = gr.sample_and_hold_ff() self.ffs_delay_input_for_correction = gr.delay(gr.sizeof_gr_complex, dp.symbol_length) # by delaying the input, we can use the ff offset estimation from the first symbol to correct the first symbol itself self.ffs_nco = gr.frequency_modulator_fc(1) # ffs_sample_and_hold directly outputs phase error per sample self.ffs_mixer = gr.multiply_cc() # calculate fine frequency error self.connect(self.input, self.ffs_conj, self.ffs_mult) self.connect(self.input, self.ffs_delay, (self.ffs_mult, 1)) self.connect(self.ffs_mult, self.ffs_moving_sum, self.ffs_angle) # only use the value from the first half of the first symbol self.connect(self.ffs_angle, self.ffs_angle_scale, (self.ffs_sample_and_hold, 0)) self.connect(self.ns_detect, self.ffs_delay_sample_and_hold, (self.ffs_sample_and_hold, 1)) # do the correction self.connect(self.ffs_sample_and_hold, self.ffs_nco, (self.ffs_mixer, 0)) self.connect(self.input, self.ffs_delay_input_for_correction, (self.ffs_mixer, 1)) # output - corrected signal and start of DAB frames self.connect(self.ffs_mixer, (self, 0)) self.connect(self.ffs_delay_sample_and_hold, (self, 1)) if debug: self.connect(self.ffs_angle, gr.file_sink(gr.sizeof_float, "debug/ofdm_sync_dab_ffs_angle.dat")) self.connect(self.ffs_sample_and_hold, gr.multiply_const_ff(1./(dp.T*2*pi)), gr.file_sink(gr.sizeof_float, "debug/ofdm_sync_dab_fine_freq_err_f.dat")) self.connect(self.ffs_mixer, gr.file_sink(gr.sizeof_gr_complex, "debug/ofdm_sync_dab_fine_freq_corrected_c.dat"))
def __init__(self,
freq_corr=0,
N_id_1=134,
avg_frames=1,
N_id_2=0,
decim=16):
grc_wxgui.top_block_gui.__init__(self, title="Sss Corr2 Gui")
_icon_path = "/usr/share/icons/hicolor/32x32/apps/gnuradio-grc.png"
self.SetIcon(wx.Icon(_icon_path, wx.BITMAP_TYPE_ANY))
##################################################
# Parameters
##################################################
self.freq_corr = freq_corr
self.N_id_1 = N_id_1
self.avg_frames = avg_frames
self.N_id_2 = N_id_2
self.decim = decim
##################################################
# Variables
##################################################
self.vec_half_frame = vec_half_frame = 30720 * 5 / decim
self.samp_rate = samp_rate = 30720e3 / decim
self.rot = rot = 0
self.noise_level = noise_level = 0
self.fft_size = fft_size = 2048 / decim
##################################################
# Blocks
##################################################
_rot_sizer = wx.BoxSizer(wx.VERTICAL)
self._rot_text_box = forms.text_box(
parent=self.GetWin(),
sizer=_rot_sizer,
value=self.rot,
callback=self.set_rot,
label='rot',
converter=forms.float_converter(),
proportion=0,
)
self._rot_slider = forms.slider(
parent=self.GetWin(),
sizer=_rot_sizer,
value=self.rot,
callback=self.set_rot,
minimum=0,
maximum=1,
num_steps=100,
style=wx.SL_HORIZONTAL,
cast=float,
proportion=1,
)
self.Add(_rot_sizer)
_noise_level_sizer = wx.BoxSizer(wx.VERTICAL)
self._noise_level_text_box = forms.text_box(
parent=self.GetWin(),
sizer=_noise_level_sizer,
value=self.noise_level,
callback=self.set_noise_level,
label='noise_level',
converter=forms.float_converter(),
proportion=0,
)
self._noise_level_slider = forms.slider(
parent=self.GetWin(),
sizer=_noise_level_sizer,
value=self.noise_level,
callback=self.set_noise_level,
minimum=0,
maximum=10,
num_steps=100,
style=wx.SL_HORIZONTAL,
cast=float,
proportion=1,
)
self.Add(_noise_level_sizer)
self.wxgui_scopesink2_0 = scopesink2.scope_sink_c(
self.GetWin(),
title="Scope Plot",
sample_rate=samp_rate,
v_scale=0,
v_offset=0,
t_scale=0,
ac_couple=False,
xy_mode=False,
num_inputs=2,
trig_mode=gr.gr_TRIG_MODE_AUTO,
y_axis_label="Counts",
)
self.Add(self.wxgui_scopesink2_0.win)
self.gr_vector_to_stream_1 = gr.vector_to_stream(
gr.sizeof_gr_complex * 1, fft_size)
self.gr_vector_to_stream_0_2 = gr.vector_to_stream(
gr.sizeof_gr_complex * 1, fft_size)
self.gr_vector_to_stream_0_1 = gr.vector_to_stream(
gr.sizeof_gr_complex * 1, fft_size)
self.gr_vector_to_stream_0_0 = gr.vector_to_stream(
gr.sizeof_gr_complex * 1, fft_size)
self.gr_vector_to_stream_0 = gr.vector_to_stream(
gr.sizeof_float * 1, fft_size)
self.gr_vector_source_x_0_0_0 = gr.vector_source_c(
(gen_pss_fd(N_id_2, fft_size, False).get_data()), True, fft_size)
self.gr_vector_source_x_0_0 = gr.vector_source_c(
(gen_pss_fd(N_id_2, fft_size, False).get_data()), True, fft_size)
self.gr_vector_source_x_0 = gr.vector_source_c(
(gen_sss_fd(N_id_1, N_id_2, fft_size).get_sss(True)), True,
fft_size)
self.gr_stream_to_vector_0_0 = gr.stream_to_vector(
gr.sizeof_gr_complex * 1, fft_size)
self.gr_stream_to_vector_0 = gr.stream_to_vector(
gr.sizeof_gr_complex * 1, fft_size)
self.gr_repeat_0 = gr.repeat(gr.sizeof_float * 1, fft_size)
self.gr_null_sink_0_0 = gr.null_sink(gr.sizeof_gr_complex * 1)
self.gr_null_sink_0 = gr.null_sink(gr.sizeof_gr_complex * 1)
self.gr_noise_source_x_0 = gr.noise_source_c(gr.GR_GAUSSIAN,
noise_level, 0)
self.gr_multiply_xx_1 = gr.multiply_vcc(1)
self.gr_multiply_xx_0 = gr.multiply_vcc(fft_size)
self.gr_multiply_const_vxx_1 = gr.multiply_const_vcc((1 / 1500., ))
self.gr_multiply_const_vxx_0 = gr.multiply_const_vcc(
(exp(rot * 2 * numpy.pi * 1j), ))
self.gr_interleave_0 = gr.interleave(gr.sizeof_gr_complex * fft_size)
self.gr_integrate_xx_0 = gr.integrate_ff(fft_size)
self.gr_float_to_complex_0_0 = gr.float_to_complex(1)
self.gr_float_to_complex_0 = gr.float_to_complex(1)
self.gr_file_source_0 = gr.file_source(
gr.sizeof_gr_complex * 1,
"/home/user/git/gr-lte/gr-lte/test/foo_pss0_sss_in.cfile", True)
self.gr_fft_vxx_1 = gr.fft_vcc(fft_size, False,
(window.blackmanharris(1024)), True, 1)
self.gr_fft_vxx_0 = gr.fft_vcc(fft_size, True,
(window.blackmanharris(1024)), True, 1)
self.gr_divide_xx_0_1 = gr.divide_cc(1)
self.gr_divide_xx_0_0 = gr.divide_ff(1)
self.gr_divide_xx_0 = gr.divide_cc(1)
self.gr_deinterleave_0 = gr.deinterleave(gr.sizeof_gr_complex *
fft_size)
self.gr_conjugate_cc_1 = gr.conjugate_cc()
self.gr_conjugate_cc_0 = gr.conjugate_cc()
self.gr_complex_to_mag_squared_0_0 = gr.complex_to_mag_squared(1)
self.gr_complex_to_mag_squared_0 = gr.complex_to_mag_squared(fft_size)
self.gr_add_xx_0 = gr.add_vcc(1)
self.gr_add_const_vxx_0 = gr.add_const_vff((1, ))
self.const_source_x_0_0 = gr.sig_source_f(0, gr.GR_CONST_WAVE, 0, 0, 0)
self.const_source_x_0 = gr.sig_source_f(0, gr.GR_CONST_WAVE, 0, 0, 0)
##################################################
# Connections
##################################################
self.connect((self.gr_file_source_0, 0),
(self.gr_stream_to_vector_0, 0))
self.connect((self.gr_conjugate_cc_0, 0),
(self.gr_stream_to_vector_0_0, 0))
self.connect((self.gr_stream_to_vector_0_0, 0),
(self.gr_multiply_xx_0, 1))
self.connect((self.gr_deinterleave_0, 0), (self.gr_multiply_xx_0, 0))
self.connect((self.gr_multiply_xx_0, 0),
(self.gr_complex_to_mag_squared_0, 0))
self.connect((self.gr_complex_to_mag_squared_0, 0),
(self.gr_vector_to_stream_0, 0))
self.connect((self.gr_vector_to_stream_0, 0),
(self.gr_integrate_xx_0, 0))
self.connect((self.gr_integrate_xx_0, 0), (self.gr_repeat_0, 0))
self.connect((self.gr_multiply_xx_0, 0),
(self.gr_vector_to_stream_0_0, 0))
self.connect((self.gr_vector_to_stream_0_1, 0),
(self.gr_multiply_xx_1, 1))
self.connect((self.gr_divide_xx_0, 0), (self.gr_multiply_xx_1, 0))
self.connect((self.gr_float_to_complex_0, 0), (self.gr_divide_xx_0, 1))
self.connect((self.gr_conjugate_cc_1, 0), (self.gr_divide_xx_0, 0))
self.connect((self.gr_deinterleave_0, 1),
(self.gr_vector_to_stream_0_1, 0))
self.connect((self.gr_repeat_0, 0), (self.gr_float_to_complex_0, 0))
self.connect((self.const_source_x_0, 0),
(self.gr_float_to_complex_0, 1))
self.connect((self.gr_vector_to_stream_0_0, 0),
(self.gr_conjugate_cc_1, 0))
self.connect((self.gr_vector_to_stream_0_0, 0),
(self.gr_complex_to_mag_squared_0_0, 0))
self.connect((self.gr_complex_to_mag_squared_0_0, 0),
(self.gr_divide_xx_0_0, 0))
self.connect((self.gr_repeat_0, 0), (self.gr_divide_xx_0_0, 1))
self.connect((self.gr_divide_xx_0_0, 0), (self.gr_add_const_vxx_0, 0))
self.connect((self.gr_add_const_vxx_0, 0),
(self.gr_float_to_complex_0_0, 0))
self.connect((self.const_source_x_0_0, 0),
(self.gr_float_to_complex_0_0, 1))
self.connect((self.gr_float_to_complex_0_0, 0),
(self.gr_divide_xx_0_1, 1))
self.connect((self.gr_multiply_xx_1, 0), (self.gr_divide_xx_0_1, 0))
self.connect((self.gr_stream_to_vector_0, 0), (self.gr_fft_vxx_0, 0))
self.connect((self.gr_fft_vxx_0, 0), (self.gr_deinterleave_0, 0))
self.connect((self.gr_vector_to_stream_0_2, 0),
(self.gr_conjugate_cc_0, 0))
self.connect((self.gr_divide_xx_0_1, 0), (self.gr_null_sink_0, 0))
self.connect((self.gr_vector_source_x_0, 0), (self.gr_interleave_0, 1))
self.connect((self.gr_interleave_0, 0), (self.gr_fft_vxx_1, 0))
self.connect((self.gr_fft_vxx_1, 0), (self.gr_vector_to_stream_1, 0))
self.connect((self.gr_vector_source_x_0_0, 0),
(self.gr_interleave_0, 0))
self.connect((self.gr_vector_source_x_0_0_0, 0),
(self.gr_vector_to_stream_0_2, 0))
self.connect((self.gr_noise_source_x_0, 0), (self.gr_add_xx_0, 1))
self.connect((self.gr_vector_to_stream_1, 0), (self.gr_add_xx_0, 0))
self.connect((self.gr_add_xx_0, 0), (self.gr_multiply_const_vxx_0, 0))
self.connect((self.gr_vector_to_stream_0_1, 0),
(self.gr_multiply_const_vxx_1, 0))
self.connect((self.gr_multiply_const_vxx_0, 0),
(self.gr_null_sink_0_0, 0))
self.connect((self.gr_multiply_const_vxx_1, 0),
(self.wxgui_scopesink2_0, 1))
self.connect((self.gr_divide_xx_0_1, 0), (self.wxgui_scopesink2_0, 0))
def __init__(self, sample_rate, ber_threshold=0, # Above which to do search ber_smoothing=0, # Alpha of BER smoother (0.01) ber_duration=0, # Length before trying next combo ber_sample_decimation=1, settling_period=0, pre_lock_duration=0, #ber_sample_skip=0 **kwargs): use_throttle = False base_duration = 1024 if sample_rate > 0: use_throttle = True base_duration *= 4 # Has to be high enough for block-delay if ber_threshold == 0: ber_threshold = 512 * 4 if ber_smoothing == 0: ber_smoothing = 0.01 if ber_duration == 0: ber_duration = base_duration * 2 # 1000ms if settling_period == 0: settling_period = base_duration * 1 # 500ms if pre_lock_duration == 0: pre_lock_duration = base_duration * 2 #1000ms print "Creating Auto-FEC:" print "\tsample_rate:\t\t", sample_rate print "\tber_threshold:\t\t", ber_threshold print "\tber_smoothing:\t\t", ber_smoothing print "\tber_duration:\t\t", ber_duration print "\tber_sample_decimation:\t", ber_sample_decimation print "\tsettling_period:\t", settling_period print "\tpre_lock_duration:\t", pre_lock_duration print "" self.sample_rate = sample_rate self.ber_threshold = ber_threshold #self.ber_smoothing = ber_smoothing self.ber_duration = ber_duration self.settling_period = settling_period self.pre_lock_duration = pre_lock_duration #self.ber_sample_skip = ber_sample_skip self.data_lock = threading.Lock() gr.hier_block2.__init__(self, "auto_fec", gr.io_signature(1, 1, gr.sizeof_gr_complex), # Post MPSK-receiver complex input gr.io_signature3(3, 3, gr.sizeof_char, gr.sizeof_float, gr.sizeof_float)) # Decoded packed bytes, BER metric, lock self.input_watcher = auto_fec_input_watcher(self) default_xform = self.input_watcher.xform_lock self.gr_conjugate_cc_0 = gr.conjugate_cc() self.connect((self, 0), (self.gr_conjugate_cc_0, 0)) # Input self.blks2_selector_0 = grc_blks2.selector( item_size=gr.sizeof_gr_complex*1, num_inputs=2, num_outputs=1, input_index=default_xform.get_conjugation_index(), output_index=0, ) self.connect((self.gr_conjugate_cc_0, 0), (self.blks2_selector_0, 0)) self.connect((self, 0), (self.blks2_selector_0, 1)) # Input self.gr_multiply_const_vxx_3 = gr.multiply_const_vcc((0.707*(1+1j), )) self.connect((self.blks2_selector_0, 0), (self.gr_multiply_const_vxx_3, 0)) self.gr_multiply_const_vxx_2 = gr.multiply_const_vcc((default_xform.get_rotation(), )) # phase_mult self.connect((self.gr_multiply_const_vxx_3, 0), (self.gr_multiply_const_vxx_2, 0)) self.gr_complex_to_float_0_0 = gr.complex_to_float(1) self.connect((self.gr_multiply_const_vxx_2, 0), (self.gr_complex_to_float_0_0, 0)) self.gr_interleave_1 = gr.interleave(gr.sizeof_float*1) self.connect((self.gr_complex_to_float_0_0, 1), (self.gr_interleave_1, 1)) self.connect((self.gr_complex_to_float_0_0, 0), (self.gr_interleave_1, 0)) self.gr_multiply_const_vxx_0 = gr.multiply_const_vff((1, )) # invert self.connect((self.gr_interleave_1, 0), (self.gr_multiply_const_vxx_0, 0)) self.baz_delay_2 = baz.delay(gr.sizeof_float*1, default_xform.get_puncture_delay()) # delay_puncture self.connect((self.gr_multiply_const_vxx_0, 0), (self.baz_delay_2, 0)) self.depuncture_ff_0 = baz.depuncture_ff((_puncture_matrices[self.input_watcher.puncture_matrix][1])) # puncture_matrix self.connect((self.baz_delay_2, 0), (self.depuncture_ff_0, 0)) self.baz_delay_1 = baz.delay(gr.sizeof_float*1, default_xform.get_viterbi_delay()) # delay_viterbi self.connect((self.depuncture_ff_0, 0), (self.baz_delay_1, 0)) self.swap_ff_0 = baz.swap_ff(default_xform.get_viterbi_swap()) # swap_viterbi self.connect((self.baz_delay_1, 0), (self.swap_ff_0, 0)) self.gr_decode_ccsds_27_fb_0 = gr.decode_ccsds_27_fb() if use_throttle: print "==> Using throttle at sample rate:", self.sample_rate self.gr_throttle_0 = gr.throttle(gr.sizeof_float, self.sample_rate) self.connect((self.swap_ff_0, 0), (self.gr_throttle_0, 0)) self.connect((self.gr_throttle_0, 0), (self.gr_decode_ccsds_27_fb_0, 0)) else: self.connect((self.swap_ff_0, 0), (self.gr_decode_ccsds_27_fb_0, 0)) self.connect((self.gr_decode_ccsds_27_fb_0, 0), (self, 0)) # Output bytes self.gr_add_const_vxx_1 = gr.add_const_vff((-4096, )) self.connect((self.gr_decode_ccsds_27_fb_0, 1), (self.gr_add_const_vxx_1, 0)) self.gr_multiply_const_vxx_1 = gr.multiply_const_vff((-1, )) self.connect((self.gr_add_const_vxx_1, 0), (self.gr_multiply_const_vxx_1, 0)) self.connect((self.gr_multiply_const_vxx_1, 0), (self, 1)) # Output BER self.gr_single_pole_iir_filter_xx_0 = gr.single_pole_iir_filter_ff(ber_smoothing, 1) self.connect((self.gr_multiply_const_vxx_1, 0), (self.gr_single_pole_iir_filter_xx_0, 0)) self.gr_keep_one_in_n_0 = blocks.keep_one_in_n(gr.sizeof_float, ber_sample_decimation) self.connect((self.gr_single_pole_iir_filter_xx_0, 0), (self.gr_keep_one_in_n_0, 0)) self.const_source_x_0 = gr.sig_source_f(0, gr.GR_CONST_WAVE, 0, 0, 0) # Last param is const value if use_throttle: lock_throttle_rate = self.sample_rate // 16 print "==> Using lock throttle rate:", lock_throttle_rate self.gr_throttle_1 = gr.throttle(gr.sizeof_float, lock_throttle_rate) self.connect((self.const_source_x_0, 0), (self.gr_throttle_1, 0)) self.connect((self.gr_throttle_1, 0), (self, 2)) else: self.connect((self.const_source_x_0, 0), (self, 2)) self.msg_q = gr.msg_queue(2*256) # message queue that holds at most 2 messages, increase to speed up process self.msg_sink = gr.message_sink(gr.sizeof_float, self.msg_q, dont_block=0) # Block to speed up process self.connect((self.gr_keep_one_in_n_0, 0), self.msg_sink) self.input_watcher.start()
def __init__(self, fft_length, cp_length, half_sync, kstime, ks1time, threshold, logging=False):
gr.hier_block2.__init__(
self,
"ofdm_sync_pn",
gr.io_signature(1, 1, gr.sizeof_gr_complex), # Input signature
gr.io_signature3(
3,
3, # Output signature
gr.sizeof_gr_complex, # delayed input
gr.sizeof_float, # fine frequency offset
gr.sizeof_char, # timing indicator
),
)
if half_sync:
period = fft_length / 2
window = fft_length / 2
else: # full symbol
period = fft_length + cp_length
window = fft_length # makes the plateau cp_length long
# Calculate the frequency offset from the correlation of the preamble
x_corr = gr.multiply_cc()
self.connect(self, gr.conjugate_cc(), (x_corr, 0))
self.connect(self, gr.delay(gr.sizeof_gr_complex, period), (x_corr, 1))
P_d = gr.moving_average_cc(window, 1.0)
self.connect(x_corr, P_d)
# offset by -1
phi = gr.sample_and_hold_ff()
self.corrmag = gr.complex_to_mag_squared()
P_d_angle = gr.complex_to_arg()
self.connect(P_d, P_d_angle, (phi, 0))
cross_correlate = 1
if cross_correlate == 1:
# cross-correlate with the known symbol
kstime = [k.conjugate() for k in kstime]
kstime.reverse()
self.crosscorr_filter = gr.fir_filter_ccc(1, kstime)
"""
self.f2b = gr.float_to_char()
self.slice = gr.threshold_ff(threshold, threshold, 0, fft_length)
#self.connect(self, self.crosscorr_filter, self.corrmag, self.slice, self.f2b)
self.connect(self.f2b, (phi,1))
self.connect(self.f2b, (self,2))
self.connect(self.f2b, gr.file_sink(gr.sizeof_char, "ofdm_f2b.dat"))
"""
# new method starts here - only crosscorrelate and use peak_detect block #
peak_detect = gr.peak_detector_fb(100, 100, 30, 0.001)
self.corrmag1 = gr.complex_to_mag_squared()
self.connect(self, self.crosscorr_filter, self.corrmag, peak_detect)
self.connect(peak_detect, (phi, 1))
self.connect(peak_detect, (self, 2))
self.connect(peak_detect, gr.file_sink(gr.sizeof_char, "sync-peaks_b.dat"))
self.connect(self.corrmag, gr.file_sink(gr.sizeof_float, "ofdm_corrmag.dat"))
self.connect(self, gr.delay(gr.sizeof_gr_complex, (fft_length)), (self, 0))
else:
# Get the power of the input signal to normalize the output of the correlation
R_d = gr.moving_average_ff(window, 1.0)
self.connect(self, gr.complex_to_mag_squared(), R_d)
R_d_squared = gr.multiply_ff() # this is retarded
self.connect(R_d, (R_d_squared, 0))
self.connect(R_d, (R_d_squared, 1))
M_d = gr.divide_ff()
self.connect(P_d, gr.complex_to_mag_squared(), (M_d, 0))
self.connect(R_d_squared, (M_d, 1))
# Now we need to detect peak of M_d
matched_filter = gr.moving_average_ff(cp_length, 1.0 / cp_length)
peak_detect = gr.peak_detector_fb(0.25, 0.25, 30, 0.001)
self.connect(M_d, matched_filter, gr.add_const_ff(-1), peak_detect)
offset = cp_length / 2 # cp_length/2
self.connect(peak_detect, (phi, 1))
self.connect(peak_detect, (self, 2))
self.connect(P_d_angle, gr.delay(gr.sizeof_float, offset), (phi, 0))
self.connect(
self, gr.delay(gr.sizeof_gr_complex, (fft_length + offset)), (self, 0)
) # delay the input to follow the freq offset
self.connect(peak_detect, gr.delay(gr.sizeof_char, (fft_length + offset)), (self, 2))
self.connect(peak_detect, gr.file_sink(gr.sizeof_char, "sync-peaks_b.dat"))
self.connect(matched_filter, gr.file_sink(gr.sizeof_float, "sync-mf.dat"))
self.connect(phi, (self, 1))
if logging:
self.connect(matched_filter, gr.file_sink(gr.sizeof_float, "sync-mf.dat"))
self.connect(M_d, gr.file_sink(gr.sizeof_float, "sync-M.dat"))
self.connect(P_d_angle, gr.file_sink(gr.sizeof_float, "sync-angle.dat"))
self.connect(peak_detect, gr.file_sink(gr.sizeof_char, "sync-peaks.datb"))
self.connect(phi, gr.file_sink(gr.sizeof_float, "sync-phi.dat"))
def __init__(self, dab_params, rx_params, debug=False): """ OFDM time and coarse frequency synchronisation for DAB @param mode DAB mode (1-4) @param debug if True: write data streams out to files """ dp = dab_params rp = rx_params gr.hier_block2.__init__(self,"ofdm_sync_dab", gr.io_signature(1, 1, gr.sizeof_gr_complex), # input signature gr.io_signature2(2, 2, gr.sizeof_gr_complex, gr.sizeof_char)) # output signature # workaround for a problem that prevents connecting more than one block directly (see trac ticket #161) self.input = gr.kludge_copy(gr.sizeof_gr_complex) self.connect(self, self.input) # # null-symbol detection # # (outsourced to detect_zero.py) self.ns_detect = detect_null.detect_null(dp.ns_length, debug) self.connect(self.input, self.ns_detect) # # fine frequency synchronisation # # the code for fine frequency synchronisation is adapted from # ofdm_sync_ml.py; it abuses the cyclic prefix to find the fine # frequency error, as suggested in "ML Estimation of Timing and # Frequency Offset in OFDM Systems", by Jan-Jaap van de Beek, # Magnus Sandell, Per Ola Börjesson, see # http://www.sm.luth.se/csee/sp/research/report/bsb96r.html self.ffs_delay = gr.delay(gr.sizeof_gr_complex, dp.fft_length) self.ffs_conj = gr.conjugate_cc() self.ffs_mult = gr.multiply_cc() self.ffs_moving_sum = dab_swig.moving_sum_cc(dp.cp_length) self.ffs_arg = gr.complex_to_arg() self.ffs_sample_and_average = dab_swig.ofdm_ffs_sample(dp.symbol_length, dp.fft_length, rp.symbols_for_ffs_estimation, rp.ffs_alpha, dp.sample_rate) if rp.correct_ffe: self.ffs_delay_input_for_correction = gr.delay(gr.sizeof_gr_complex, dp.symbol_length*rp.symbols_for_ffs_estimation) # by delaying the input, we can use the ff offset estimation from the first symbol to correct the first symbol itself self.ffs_delay_frame_start = gr.delay(gr.sizeof_char, dp.symbol_length*rp.symbols_for_ffs_estimation) # sample the value at the end of the symbol .. self.ffs_nco = gr.frequency_modulator_fc(1) # ffs_sample_and_hold directly outputs phase error per sample self.ffs_mixer = gr.multiply_cc() # calculate fine frequency error self.connect(self.input, self.ffs_conj, self.ffs_mult) self.connect(self.input, self.ffs_delay, (self.ffs_mult, 1)) self.connect(self.ffs_mult, self.ffs_moving_sum, self.ffs_arg, (self.ffs_sample_and_average, 0)) self.connect(self.ns_detect, (self.ffs_sample_and_average, 1)) if rp.correct_ffe: # do the correction self.connect(self.ffs_sample_and_average, self.ffs_nco, (self.ffs_mixer, 0)) self.connect(self.input, self.ffs_delay_input_for_correction, (self.ffs_mixer, 1)) # output - corrected signal and start of DAB frames self.connect(self.ffs_mixer, (self, 0)) self.connect(self.ns_detect, self.ffs_delay_frame_start, (self, 1)) else: # just patch the signal through self.connect(self.ffs_sample_and_average, gr.null_sink(gr.sizeof_float)) self.connect(self.input, (self,0)) # frame start still needed .. self.connect(self.ns_detect, (self,1)) if debug: self.connect(self.ffs_sample_and_average, gr.multiply_const_ff(1./(dp.T*2*pi)), gr.file_sink(gr.sizeof_float, "debug/ofdm_sync_dab_fine_freq_err_f.dat")) self.connect(self.ffs_mixer, gr.file_sink(gr.sizeof_gr_complex, "debug/ofdm_sync_dab_fine_freq_corrected_c.dat"))
def __init__(self):
grc_wxgui.top_block_gui.__init__(self, title="Top Block")
##################################################
# Variables
##################################################
self.tranwidth = tranwidth = 10000
self.tau = tau = 50e-6
self.gain = gain = 20
self.freq = freq = 100.0e6
self.decim = decim = 80
self.cutoff = cutoff = 100000
##################################################
# Blocks
##################################################
self._tranwidth_text_box = forms.text_box(
parent=self.GetWin(),
value=self.tranwidth,
callback=self.set_tranwidth,
label="tranwidth",
converter=forms.float_converter(),
)
self.GridAdd(self._tranwidth_text_box, 1, 1, 1, 1)
_tau_sizer = wx.BoxSizer(wx.VERTICAL)
self._tau_text_box = forms.text_box(
parent=self.GetWin(),
sizer=_tau_sizer,
value=self.tau,
callback=self.set_tau,
label="Zeitkonstante (Tau)",
converter=forms.float_converter(),
proportion=0,
)
self._tau_slider = forms.slider(
parent=self.GetWin(),
sizer=_tau_sizer,
value=self.tau,
callback=self.set_tau,
minimum=0,
maximum=100e-6,
num_steps=100,
style=wx.SL_HORIZONTAL,
cast=float,
proportion=1,
)
self.Add(_tau_sizer)
_gain_sizer = wx.BoxSizer(wx.VERTICAL)
self._gain_text_box = forms.text_box(
parent=self.GetWin(),
sizer=_gain_sizer,
value=self.gain,
callback=self.set_gain,
label="Gain [dB]",
converter=forms.float_converter(),
proportion=0,
)
self._gain_slider = forms.slider(
parent=self.GetWin(),
sizer=_gain_sizer,
value=self.gain,
callback=self.set_gain,
minimum=0,
maximum=30,
num_steps=60,
style=wx.SL_HORIZONTAL,
cast=float,
proportion=1,
)
self.Add(_gain_sizer)
_freq_sizer = wx.BoxSizer(wx.VERTICAL)
self._freq_text_box = forms.text_box(
parent=self.GetWin(),
sizer=_freq_sizer,
value=self.freq,
callback=self.set_freq,
label="Frequenz (UKW)",
converter=forms.float_converter(),
proportion=0,
)
self._freq_slider = forms.slider(
parent=self.GetWin(),
sizer=_freq_sizer,
value=self.freq,
callback=self.set_freq,
minimum=87.5e6,
maximum=108e6,
num_steps=205,
style=wx.SL_HORIZONTAL,
cast=float,
proportion=1,
)
self.GridAdd(_freq_sizer, 0, 0, 1, 1)
self._decim_text_box = forms.text_box(
parent=self.GetWin(),
value=self.decim,
callback=self.set_decim,
label="Decimation",
converter=forms.float_converter(),
)
self.GridAdd(self._decim_text_box, 1, 0, 1, 1)
self._cutoff_text_box = forms.text_box(
parent=self.GetWin(),
value=self.cutoff,
callback=self.set_cutoff,
label="Cutoff",
converter=forms.float_converter(),
)
self.GridAdd(self._cutoff_text_box, 0, 1, 1, 1)
self.wxgui_fftsink2_0 = fftsink2.fft_sink_f(
self.GetWin(),
baseband_freq=0,
y_per_div=10,
y_divs=10,
ref_level=0,
ref_scale=2.0,
sample_rate=64000000/decim,
fft_size=1024,
fft_rate=15,
average=False,
avg_alpha=None,
title="FFT Plot",
peak_hold=False,
)
self.Add(self.wxgui_fftsink2_0.win)
self.uhd_usrp_source_0 = uhd.usrp_source(
device_addr="type=usrp1",
stream_args=uhd.stream_args(
cpu_format="fc32",
channels=range(1),
),
)
self.uhd_usrp_source_0.set_subdev_spec("B:0", 0)
self.uhd_usrp_source_0.set_samp_rate(64000000/decim)
self.uhd_usrp_source_0.set_center_freq(freq, 0)
self.uhd_usrp_source_0.set_gain(gain, 0)
self.low_pass_filter_0_0 = gr.fir_filter_fff(1, firdes.low_pass(
1, 44100, 15000, tranwidth, firdes.WIN_HAMMING, 6.76))
self.low_pass_filter_0 = gr.fir_filter_ccf(1, firdes.low_pass(
1, 64000000/decim, cutoff, tranwidth, firdes.WIN_HAMMING, 6.76))
self.gr_multiply_xx_0 = gr.multiply_vcc(1)
self.gr_iir_filter_ffd_1 = gr.iir_filter_ffd(((1.0/(1+tau*2*800000), 1.0/(1+tau*2*800000))), ((1, -(1-tau*2*800000)/(1+tau*2*800000))))
self.gr_delay_0 = gr.delay(gr.sizeof_gr_complex*1, 1)
self.gr_conjugate_cc_0 = gr.conjugate_cc()
self.gr_complex_to_arg_0 = gr.complex_to_arg(1)
self.blks2_rational_resampler_xxx_0 = blks2.rational_resampler_fff(
interpolation=44100,
decimation=64000000/decim,
taps=None,
fractional_bw=None,
)
self.audio_sink_0 = audio.sink(44100, "", True)
##################################################
# Connections
##################################################
self.connect((self.uhd_usrp_source_0, 0), (self.low_pass_filter_0, 0))
self.connect((self.low_pass_filter_0, 0), (self.gr_delay_0, 0))
self.connect((self.low_pass_filter_0, 0), (self.gr_multiply_xx_0, 0))
self.connect((self.gr_delay_0, 0), (self.gr_conjugate_cc_0, 0))
self.connect((self.gr_conjugate_cc_0, 0), (self.gr_multiply_xx_0, 1))
self.connect((self.gr_multiply_xx_0, 0), (self.gr_complex_to_arg_0, 0))
self.connect((self.gr_complex_to_arg_0, 0), (self.blks2_rational_resampler_xxx_0, 0))
self.connect((self.blks2_rational_resampler_xxx_0, 0), (self.low_pass_filter_0_0, 0))
self.connect((self.low_pass_filter_0_0, 0), (self.gr_iir_filter_ffd_1, 0))
self.connect((self.gr_iir_filter_ffd_1, 0), (self.audio_sink_0, 0))
self.connect((self.gr_complex_to_arg_0, 0), (self.wxgui_fftsink2_0, 0))
