def __init__(self,
samples_per_symbol=_def_samples_per_symbol,
bt=_def_bt,
verbose=_def_verbose,
log=_def_log):
gr.hier_block2.__init__(
self,
"gmsk_mod",
gr.io_signature(1, 1, gr.sizeof_char), # Input signature
gr.io_signature(1, 1, gr.sizeof_gr_complex)) # Output signature
samples_per_symbol = int(samples_per_symbol)
self._samples_per_symbol = samples_per_symbol
self._bt = bt
self._differential = False
if not isinstance(samples_per_symbol, int) or samples_per_symbol < 2:
raise TypeError, (
"samples_per_symbol must be an integer >= 2, is %r" %
(samples_per_symbol, ))
ntaps = 4 * samples_per_symbol # up to 3 bits in filter at once
sensitivity = (pi /
2) / samples_per_symbol # phase change per bit = pi / 2
# Turn it into NRZ data.
#self.nrz = digital.bytes_to_syms()
self.unpack = blocks.packed_to_unpacked_bb(1, gr.GR_MSB_FIRST)
self.nrz = digital.chunks_to_symbols_bf([-1, 1], 1)
# Form Gaussian filter
# Generate Gaussian response (Needs to be convolved with window below).
self.gaussian_taps = filter.firdes.gaussian(
1, # gain
samples_per_symbol, # symbol_rate
bt, # bandwidth * symbol time
ntaps # number of taps
)
self.sqwave = (1, ) * samples_per_symbol # rectangular window
self.taps = numpy.convolve(numpy.array(self.gaussian_taps),
numpy.array(self.sqwave))
self.gaussian_filter = filter.interp_fir_filter_fff(
samples_per_symbol, self.taps)
# FM modulation
self.fmmod = analog.frequency_modulator_fc(sensitivity)
if verbose:
self._print_verbage()
if log:
self._setup_logging()
# Connect & Initialize base class
self.connect(self, self.unpack, self.nrz, self.gaussian_filter,
self.fmmod, self)
def __init__(self,
samples_per_symbol=_def_samples_per_symbol,
bt=_def_bt,
verbose=_def_verbose,
log=_def_log):
gr.hier_block2.__init__(self, "gmsk_mod",
gr.io_signature(1, 1, gr.sizeof_char), # Input signature
gr.io_signature(1, 1, gr.sizeof_gr_complex)) # Output signature
samples_per_symbol = int(samples_per_symbol)
self._samples_per_symbol = samples_per_symbol
self._bt = bt
self._differential = False
if not isinstance(samples_per_symbol, int) or samples_per_symbol < 2:
raise TypeError, ("samples_per_symbol must be an integer >= 2, is %r" % (samples_per_symbol,))
ntaps = 4 * samples_per_symbol # up to 3 bits in filter at once
sensitivity = (pi / 2) / samples_per_symbol # phase change per bit = pi / 2
# Turn it into NRZ data.
#self.nrz = digital.bytes_to_syms()
self.unpack = blocks.packed_to_unpacked_bb(1, gr.GR_MSB_FIRST)
self.nrz = digital.chunks_to_symbols_bf([-1, 1], 1)
# Form Gaussian filter
# Generate Gaussian response (Needs to be convolved with window below).
self.gaussian_taps = filter.firdes.gaussian(
1, # gain
samples_per_symbol, # symbol_rate
bt, # bandwidth * symbol time
ntaps # number of taps
)
self.sqwave = (1,) * samples_per_symbol # rectangular window
self.taps = numpy.convolve(numpy.array(self.gaussian_taps),numpy.array(self.sqwave))
self.gaussian_filter = filter.interp_fir_filter_fff(samples_per_symbol, self.taps)
# FM modulation
self.fmmod = analog.frequency_modulator_fc(sensitivity)
if verbose:
self._print_verbage()
if log:
self._setup_logging()
# Connect & Initialize base class
self.connect(self, self.unpack, self.nrz, self.gaussian_filter, self.fmmod, self)
def test_002_correlation_b(self):
for degree in range(1,11): # Higher degrees take too long to correlate
src = digital.glfsr_source_b(degree, False)
b2f = digital.chunks_to_symbols_bf((-1.0,1.0), 1)
dst = blocks.vector_sink_f()
del self.tb # Discard existing top block
self.tb = gr.top_block()
self.tb.connect(src, b2f, dst)
self.tb.run()
self.tb.disconnect_all()
actual_result = dst.data()
R = auto_correlate(actual_result)
self.assertEqual(R[0], float(len(R))) # Auto-correlation peak at origin
for i in range(len(R)-1):
self.assertEqual(R[i+1], -1.0) # Auto-correlation minimum everywhere else
def test_bf_002(self):
const = [-3, -1, 1, 3]
src_data = (0, 1, 2, 3, 3, 2, 1, 0)
expected_result = (-3, -1, 1, 3, 3, 1, -1, -3)
src = blocks.vector_source_b(src_data)
op = digital.chunks_to_symbols_bf(const)
dst = blocks.vector_sink_f()
self.tb.connect(src, op)
self.tb.connect(op, dst)
self.tb.run()
actual_result = dst.data()
self.assertEqual(expected_result, actual_result)
def test_002_correlation_b(self):
for degree in range(1,11): # Higher degrees take too long to correlate
src = digital.glfsr_source_b(degree, False)
b2f = digital.chunks_to_symbols_bf((-1.0,1.0), 1)
dst = gr.vector_sink_f()
del self.tb # Discard existing top block
self.tb = gr.top_block()
self.tb.connect(src, b2f, dst)
self.tb.run()
self.tb.disconnect_all()
actual_result = dst.data()
R = auto_correlate(actual_result)
self.assertEqual(R[0], float(len(R))) # Auto-correlation peak at origin
for i in range(len(R)-1):
self.assertEqual(R[i+1], -1.0) # Auto-correlation minimum everywhere else
def test_bf_002(self):
const = [-3, -1, 1, 3]
src_data = (0, 1, 2, 3, 3, 2, 1, 0)
expected_result = (-3, -1, 1, 3,
3, 1, -1, -3)
src = gr.vector_source_b(src_data)
op = digital.chunks_to_symbols_bf(const)
dst = gr.vector_sink_f()
self.tb.connect(src, op)
self.tb.connect(op, dst)
self.tb.run()
actual_result = dst.data()
self.assertEqual(expected_result, actual_result)
def __init__(self,
samples_per_symbol=_def_samples_per_symbol,
bits_per_symbol=_def_bits_per_symbol,
h_numerator=_def_h_numerator,
h_denominator=_def_h_denominator,
cpm_type=_def_cpm_type,
bt=_def_bt,
symbols_per_pulse=_def_symbols_per_pulse,
generic_taps=_def_generic_taps,
verbose=_def_verbose,
log=_def_log):
gr.hier_block2.__init__(self, "cpm_mod",
gr.io_signature(1, 1, gr.sizeof_char), # Input signature
gr.io_signature(1, 1, gr.sizeof_gr_complex)) # Output signature
self._samples_per_symbol = samples_per_symbol
self._bits_per_symbol = bits_per_symbol
self._h_numerator = h_numerator
self._h_denominator = h_denominator
self._cpm_type = cpm_type
self._bt=bt
if cpm_type == 0 or cpm_type == 2 or cpm_type == 3: # CPFSK, RC, Generic
self._symbols_per_pulse = symbols_per_pulse
elif cpm_type == 1: # GMSK
self._symbols_per_pulse = 4
else:
raise TypeError, ("cpm_type must be an integer in {0,1,2,3}, is %r" % (cpm_type,))
self._generic_taps=numpy.array(generic_taps)
if samples_per_symbol < 2:
raise TypeError, ("samples_per_symbol must be >= 2, is %r" % (samples_per_symbol,))
self.nsymbols = 2**bits_per_symbol
self.sym_alphabet = numpy.arange(-(self.nsymbols-1),self.nsymbols,2).tolist()
self.ntaps = int(self._symbols_per_pulse * samples_per_symbol)
sensitivity = 2 * pi * h_numerator / h_denominator / samples_per_symbol
# Unpack Bytes into bits_per_symbol groups
self.B2s = blocks.packed_to_unpacked_bb(bits_per_symbol,gr.GR_MSB_FIRST)
# Turn it into symmetric PAM data.
self.pam = digital_swig.chunks_to_symbols_bf(self.sym_alphabet,1)
# Generate pulse (sum of taps = samples_per_symbol/2)
if cpm_type == 0: # CPFSK
self.taps= (1.0/self._symbols_per_pulse/2,) * self.ntaps
elif cpm_type == 1: # GMSK
gaussian_taps = filter.firdes.gaussian(
1.0/2, # gain
samples_per_symbol, # symbol_rate
bt, # bandwidth * symbol time
self.ntaps # number of taps
)
sqwave = (1,) * samples_per_symbol # rectangular window
self.taps = numpy.convolve(numpy.array(gaussian_taps),numpy.array(sqwave))
elif cpm_type == 2: # Raised Cosine
# generalize it for arbitrary roll-off factor
self.taps = (1-numpy.cos(2*pi*numpy.arange(0,self.ntaps)/samples_per_symbol/self._symbols_per_pulse))/(2*self._symbols_per_pulse)
elif cpm_type == 3: # Generic CPM
self.taps = generic_taps
else:
raise TypeError, ("cpm_type must be an integer in {0,1,2,3}, is %r" % (cpm_type,))
self.filter = filter.pfb.arb_resampler_fff(samples_per_symbol, self.taps)
# FM modulation
self.fmmod = analog.frequency_modulator_fc(sensitivity)
if verbose:
self._print_verbage()
if log:
self._setup_logging()
# Connect
self.connect(self, self.B2s, self.pam, self.filter, self.fmmod, self)
def __init__(self,
samples_per_symbol=_def_samples_per_symbol,
sensitivity=_def_sensitivity,
bt=_def_bt,
verbose=_def_verbose,
log=_def_log):
"""
Hierarchical block for Gaussian Frequency Shift Key (GFSK)
modulation.
The input is a byte stream (unsigned char) and the
output is the complex modulated signal at baseband.
Args:
samples_per_symbol: samples per baud >= 2 (integer)
bt: Gaussian filter bandwidth * symbol time (float)
verbose: Print information about modulator? (bool)
debug: Print modualtion data to files? (bool)
"""
gr.hier_block2.__init__(self, "gfsk_mod",
gr.io_signature(1, 1, gr.sizeof_char), # Input signature
gr.io_signature(1, 1, gr.sizeof_gr_complex)) # Output signature
samples_per_symbol = int(samples_per_symbol)
self._samples_per_symbol = samples_per_symbol
self._bt = bt
self._differential = False
if not isinstance(samples_per_symbol, int) or samples_per_symbol < 2:
raise TypeError, ("samples_per_symbol must be an integer >= 2, is %r" % (samples_per_symbol,))
ntaps = 4 * samples_per_symbol # up to 3 bits in filter at once
#sensitivity = (pi / 2) / samples_per_symbol # phase change per bit = pi / 2
# Turn it into NRZ data.
#self.nrz = digital.bytes_to_syms()
self.unpack = blocks.packed_to_unpacked_bb(1, gr.GR_MSB_FIRST)
self.nrz = digital.chunks_to_symbols_bf([-1, 1])
# Form Gaussian filter
# Generate Gaussian response (Needs to be convolved with window below).
self.gaussian_taps = filter.firdes.gaussian(
1.0, # gain
samples_per_symbol, # symbol_rate
bt, # bandwidth * symbol time
ntaps # number of taps
)
self.sqwave = (1,) * samples_per_symbol # rectangular window
self.taps = numpy.convolve(numpy.array(self.gaussian_taps),numpy.array(self.sqwave))
self.gaussian_filter = filter.interp_fir_filter_fff(samples_per_symbol, self.taps)
# FM modulation
self.fmmod = analog.frequency_modulator_fc(sensitivity)
# small amount of output attenuation to prevent clipping USRP sink
self.amp = blocks.multiply_const_cc(0.999)
if verbose:
self._print_verbage()
if log:
self._setup_logging()
# Connect & Initialize base class
self.connect(self, self.unpack, self.nrz, self.gaussian_filter, self.fmmod, self.amp, self)
def __init__(self,
samples_per_symbol=_def_samples_per_symbol,
bits_per_symbol=_def_bits_per_symbol,
h_numerator=_def_h_numerator,
h_denominator=_def_h_denominator,
cpm_type=_def_cpm_type,
bt=_def_bt,
symbols_per_pulse=_def_symbols_per_pulse,
generic_taps=_def_generic_taps,
verbose=_def_verbose,
log=_def_log):
gr.hier_block2.__init__(
self,
"cpm_mod",
gr.io_signature(1, 1, gr.sizeof_char), # Input signature
gr.io_signature(1, 1, gr.sizeof_gr_complex)) # Output signature
self._samples_per_symbol = samples_per_symbol
self._bits_per_symbol = bits_per_symbol
self._h_numerator = h_numerator
self._h_denominator = h_denominator
self._cpm_type = cpm_type
self._bt = bt
if cpm_type == 0 or cpm_type == 2 or cpm_type == 3: # CPFSK, RC, Generic
self._symbols_per_pulse = symbols_per_pulse
elif cpm_type == 1: # GMSK
self._symbols_per_pulse = 4
else:
raise TypeError, (
"cpm_type must be an integer in {0,1,2,3}, is %r" %
(cpm_type, ))
self._generic_taps = numpy.array(generic_taps)
if samples_per_symbol < 2:
raise TypeError, ("samples_per_symbol must be >= 2, is %r" %
(samples_per_symbol, ))
self.nsymbols = 2**bits_per_symbol
self.sym_alphabet = numpy.arange(-(self.nsymbols - 1), self.nsymbols,
2).tolist()
self.ntaps = int(self._symbols_per_pulse * samples_per_symbol)
sensitivity = 2 * pi * h_numerator / h_denominator / samples_per_symbol
# Unpack Bytes into bits_per_symbol groups
self.B2s = blocks.packed_to_unpacked_bb(bits_per_symbol,
gr.GR_MSB_FIRST)
# Turn it into symmetric PAM data.
self.pam = digital_swig.chunks_to_symbols_bf(self.sym_alphabet, 1)
# Generate pulse (sum of taps = samples_per_symbol/2)
if cpm_type == 0: # CPFSK
self.taps = (1.0 / self._symbols_per_pulse / 2, ) * self.ntaps
elif cpm_type == 1: # GMSK
gaussian_taps = filter.firdes.gaussian(
1.0 / 2, # gain
samples_per_symbol, # symbol_rate
bt, # bandwidth * symbol time
self.ntaps # number of taps
)
sqwave = (1, ) * samples_per_symbol # rectangular window
self.taps = numpy.convolve(numpy.array(gaussian_taps),
numpy.array(sqwave))
elif cpm_type == 2: # Raised Cosine
# generalize it for arbitrary roll-off factor
self.taps = (1 - numpy.cos(
2 * pi * numpy.arange(0, self.ntaps) / samples_per_symbol /
self._symbols_per_pulse)) / (2 * self._symbols_per_pulse)
elif cpm_type == 3: # Generic CPM
self.taps = generic_taps
else:
raise TypeError, (
"cpm_type must be an integer in {0,1,2,3}, is %r" %
(cpm_type, ))
self.filter = filter.pfb.arb_resampler_fff(samples_per_symbol,
self.taps)
# FM modulation
self.fmmod = analog.frequency_modulator_fc(sensitivity)
if verbose:
self._print_verbage()
if log:
self._setup_logging()
# Connect
self.connect(self, self.B2s, self.pam, self.filter, self.fmmod, self)