def __init__(self, seq1, seq2, factor, alpha, samp_rate, freqs):
"""
Description:
This block is functionally equivalent to the frequency_timing_estimator block, except from the fact that each filter is matched to a sequence that can be written as the kronecker product of seq1 and seq2.
Args:
seq1: sequence1 of kronecker filter, which is the given training sequence.
seq2: sequence2 of kronecker filter, which is the pulse for each training symbol.
factor: the rise and fall factors in peak detector, which is the factor determining when a peak has started and ended. In the peak detector, an average of the signal is calculated. When the value of the signal goes over factor*average, we start looking for a peak. When the value of the signal goes below factor*average, we stop looking for a peak. factor takes values in (0,1).
alpha: the smoothing factor of a moving average filter used in the peak detector takeng values in (0,1).
samp_rate: the sample rate of the system, which is used in the kronecker_filter.
freqs: the vector of center frequencies for each matched filter. Note that for a training sequence of length Nt, each matched filter can recover a sequence with normalized frequency offset ~ 1/(2Nt).
"""
gr.hier_block2.__init__(self,
"freq_timing_estimator",
gr.io_signature(1, 1, gr.sizeof_gr_complex*1),
gr.io_signaturev(3, 3, [gr.sizeof_char*1, gr.sizeof_float*1, gr.sizeof_float*1]),
)
##################################################
# Parameters
##################################################
self.seq1 = seq1
self.seq2 = seq2
self.factor = factor
self.alpha = alpha
self.samp_rate = samp_rate
self.freqs = freqs
self.n = n = len(freqs)
##################################################
# Blocks
##################################################
self._filter=[0]*self.n
self._c2mag2=[0]*self.n
for i in range(self.n):
self._filter[i]= cdma.kronecker_filter(seq1,seq2,samp_rate,self.freqs[i])
#self._filter[i]= filter.freq_xlating_fir_filter_ccc(1, (numpy.conjugate(self.ts[::-1])), self.freqs[i], self.samp_rate)
self._c2mag2[i] = blocks.complex_to_mag_squared(1)
self.blocks_max = blocks.max_ff(1)
self.blocks_peak_detector = blocks.peak_detector_fb(self.factor, self.factor, 0, self.alpha)
self.blocks_argmax = blocks.argmax_fs(1)
self.blocks_null_sink = blocks.null_sink(gr.sizeof_short*1)
self.digital_chunks_to_symbols = digital.chunks_to_symbols_sf((freqs), 1)
self.blocks_sample_and_hold = blocks.sample_and_hold_ff()
##################################################
# Connections
##################################################
for i in range(self.n):
self.connect((self, 0), (self._filter[i], 0))
self.connect((self._filter[i], 0), (self._c2mag2[i], 0))
self.connect((self._c2mag2[i], 0), (self.blocks_max, i))
self.connect((self._c2mag2[i], 0), (self.blocks_argmax, i))
self.connect((self.blocks_max, 0), (self.blocks_peak_detector, 0))
self.connect((self.blocks_peak_detector, 0), (self, 0))
self.connect((self.blocks_argmax, 0), (self.blocks_null_sink, 0))
self.connect((self.blocks_argmax, 1), (self.digital_chunks_to_symbols, 0))
self.connect((self.digital_chunks_to_symbols, 0), (self.blocks_sample_and_hold, 0))
self.connect((self.blocks_peak_detector, 0), (self.blocks_sample_and_hold, 1))
self.connect((self.blocks_sample_and_hold, 0), (self, 1))
self.connect((self.blocks_max, 0), (self, 2))
def __init__(self, seq1, seq2, factor, alpha, freqs):
"""
Description:
frequency timing estimator class does frequency/timing acquisition from scratch.It uses a bank of parallel correlators at each specified frequency. It then takes the max abs value of all these and passes it through a peak detector to find timing.
Args:
seq1: sequence1 of kronecker filter, which is the given training sequence.
seq2: sequence2 of kronecker filter, which is the pulse for each training symbol.
factor: the rise and fall factors in peak detector, which is the factor determining when a peak has started and ended. In the peak detector, an average of the signal is calculated. When the value of the signal goes over factor*average, we start looking for a peak. When the value of the signal goes below factor*average, we stop looking for a peak.
alpha: the smoothing factor of a moving average filter used in the peak detector taking values in (0,1).
freqs: the vector of normalized center frequencies for each matched filter. Note that for a training sequence of length Nt, each matched filter can recover a sequence with normalized frequency offset ~ 1/(2Nt).
"""
gr.hier_block2.__init__(self,
"freq_timing_estimator",
gr.io_signature(1, 1, gr.sizeof_gr_complex*1),
gr.io_signaturev(3, 3, [gr.sizeof_char*1, gr.sizeof_float*1, gr.sizeof_float*1]),
)
##################################################
# Parameters
##################################################
self.seq1 = seq1
self.seq2 = seq2
self.factor = factor
self.alpha = alpha
self.freqs = freqs
self.n = n = len(freqs)
##################################################
# Blocks
##################################################
self._filter=[0]*self.n
self._c2mag2=[0]*self.n
for i in range(self.n):
self._filter[i]= cdma.kronecker_filter(seq1,seq2,1,self.freqs[i])
#self._filter[i]= filter.freq_xlating_fir_filter_ccc(1, numpy.kron(seq1,seq2), self.freqs[i], 1)
self._c2mag2[i] = blocks.complex_to_mag_squared(1)
self.blocks_max = blocks.max_ff(1)
self.blocks_peak_detector = blocks.peak_detector_fb(self.factor, self.factor, 0, self.alpha)
self.blocks_argmax = blocks.argmax_fs(1)
self.blocks_null_sink = blocks.null_sink(gr.sizeof_short*1)
self.digital_chunks_to_symbols = digital.chunks_to_symbols_sf((freqs), 1)
self.blocks_sample_and_hold = blocks.sample_and_hold_ff()
##################################################
# Connections
##################################################
for i in range(self.n):
self.connect((self, 0), (self._filter[i], 0))
self.connect((self._filter[i], 0), (self._c2mag2[i], 0))
self.connect((self._c2mag2[i], 0), (self.blocks_max, i))
self.connect((self._c2mag2[i], 0), (self.blocks_argmax, i))
self.connect((self.blocks_max, 0), (self.blocks_peak_detector, 0))
self.connect((self.blocks_peak_detector, 0), (self, 0))
self.connect((self.blocks_argmax, 0), (self.blocks_null_sink, 0))
self.connect((self.blocks_argmax, 1), (self.digital_chunks_to_symbols, 0))
self.connect((self.digital_chunks_to_symbols, 0), (self.blocks_sample_and_hold, 0))
self.connect((self.blocks_peak_detector, 0), (self.blocks_sample_and_hold, 1))
self.connect((self.blocks_sample_and_hold, 0), (self, 1))
self.connect((self.blocks_max, 0), (self, 2))
