def __init__(self, options):
gr.hier_block2.__init__(self, "ais_demod",
gr.io_signature(1, 1, gr.sizeof_gr_complex), # Input signature
gr.io_signature(1, 1, gr.sizeof_char)) # Output signature
self._samples_per_symbol = options.samples_per_symbol
self._bits_per_sec = options.bits_per_sec
self._samplerate = self._samples_per_symbol * self._bits_per_sec
self._gain_mu = options.gain_mu
self._mu = options.mu
self._omega_relative_limit = options.omega_relative_limit
self.fftlen = options.fftlen
#right now we are going to hardcode the different options for VA mode here. later on we can use configurable options
samples_per_symbol_viterbi = 2
bits_per_symbol = 2
samples_per_symbol = 6
samples_per_symbol_clockrec = samples_per_symbol / bits_per_symbol
BT = 0.4
data_rate = 9600.0
samp_rate = options.samp_rate
self.gmsk_sync = gmsk_sync.square_and_fft_sync(self._samplerate, self._bits_per_sec, self.fftlen)
if(options.viterbi is True):
#calculate the required decimation and interpolation to achieve the desired samples per symbol
denom = gcd(data_rate*samples_per_symbol, samp_rate)
cr_interp = int(data_rate*samples_per_symbol/denom)
cr_decim = int(samp_rate/denom)
self.resample = blks2.rational_resampler_ccc(cr_interp, cr_decim)
#here we take a different tack and use A.A.'s CPM decomposition technique
self.clockrec = gr.clock_recovery_mm_cc(samples_per_symbol_clockrec, 0.005*0.005*0.25, 0.5, 0.005, 0.0005) #might have to futz with the max. deviation
(fsm, constellation, MF, N, f0T) = make_gmsk(samples_per_symbol_viterbi, BT) #calculate the decomposition required for demodulation
self.costas = gr.costas_loop_cc(0.015, 0.015*0.015*0.25, 100e-6, -100e-6, 4) #does fine freq/phase synchronization. should probably calc the coeffs instead of hardcode them.
self.streams2stream = gr.streams_to_stream(int(gr.sizeof_gr_complex*1), int(N))
self.mf0 = gr.fir_filter_ccc(samples_per_symbol_viterbi, MF[0].conjugate()) #two matched filters for decomposition
self.mf1 = gr.fir_filter_ccc(samples_per_symbol_viterbi, MF[1].conjugate())
self.fo = gr.sig_source_c(samples_per_symbol_viterbi, gr.GR_COS_WAVE, -f0T, 1, 0) #the memoryless modulation component of the decomposition
self.fomult = gr.multiply_cc(1)
self.trellis = trellis.viterbi_combined_cb(fsm, int(data_rate), -1, -1, int(N), constellation, trellis.TRELLIS_EUCLIDEAN) #the actual Viterbi decoder
else:
#this is probably not optimal and someone who knows what they're doing should correct me
self.datafiltertaps = gr.firdes.root_raised_cosine(10, #gain
self._samplerate*32, #sample rate
self._bits_per_sec, #symbol rate
0.4, #alpha, same as BT?
50*32) #no. of taps
self.datafilter = gr.fir_filter_fff(1, self.datafiltertaps)
sensitivity = (math.pi / 2) / self._samples_per_symbol
self.demod = gr.quadrature_demod_cf(sensitivity) #param is gain
#self.clockrec = digital.clock_recovery_mm_ff(self._samples_per_symbol,0.25*self._gain_mu*self._gain_mu,self._mu,self._gain_mu,self._omega_relative_limit)
self.clockrec = gr.pfb_clock_sync_ccf(self._samples_per_symbol, 0.04, self.datafiltertaps, 32, 0, 1.15)
self.tcslicer = digital.digital.binary_slicer_fb()
# self.dfe = digital.digital.lms_dd_equalizer_cc(
# 32,
# 0.005,
# 1,
# digital.digital.constellation_bpsk()
# )
# self.delay = gr.delay(gr.sizeof_float, 64 + 16) #the correlator delays 64 bits, and the LMS delays some as well.
self.slicer = digital.digital.binary_slicer_fb()
# self.training_correlator = digital.correlate_access_code_bb("1100110011001100", 0)
# self.cma = digital.cma_equalizer_cc
#just a note here: a complex combined quad demod/slicer could be based on if's rather than an actual quad demod, right?
#in fact all the constellation decoders up to QPSK could operate on complex data w/o doing the whole atan thing
self.diff = gr.diff_decoder_bb(2)
self.invert = ais.invert() #NRZI signal diff decoded and inverted should give original signal
self.connect(self, self.gmsk_sync)
if(options.viterbi is False):
self.connect(self.gmsk_sync, self.clockrec, self.demod, self.slicer, self.diff, self.invert, self)
#self.connect(self.gmsk_sync, self.demod, self.clockrec, self.tcslicer, self.training_correlator)
#self.connect(self.clockrec, self.delay, (self.dfe, 0))
#self.connect(self.training_correlator, (self.dfe, 1))
#self.connect(self.dfe, self.slicer, self.diff, self.invert, self)
else:
self.connect(self.gmsk_sync, self.costas, self.resample, self.clockrec)
self.connect(self.clockrec, (self.fomult, 0))
self.connect(self.fo, (self.fomult, 1))
self.connect(self.fomult, self.mf0)
self.connect(self.fomult, self.mf1)
self.connect(self.mf0, (self.streams2stream, 0))
self.connect(self.mf1, (self.streams2stream, 1))
self.connect(self.streams2stream, self.trellis, self.diff, self.invert, self)
def __init__(self, options):
gr.hier_block2.__init__(
self,
"ais_demod",
gr.io_signature(1, 1, gr.sizeof_gr_complex), # Input signature
gr.io_signature(1, 1, gr.sizeof_char),
) # Output signature
self._samples_per_symbol = options.samples_per_symbol
self._bits_per_sec = options.bits_per_sec
self._samplerate = self._samples_per_symbol * self._bits_per_sec
self._gain_mu = options.gain_mu
self._mu = options.mu
self._omega_relative_limit = options.omega_relative_limit
self.fftlen = options.fftlen
# right now we are going to hardcode the different options for VA mode here. later on we can use configurable options
samples_per_symbol_viterbi = 2
bits_per_symbol = 2
samples_per_symbol = 6
samples_per_symbol_clockrec = samples_per_symbol / bits_per_symbol
BT = 0.4
data_rate = 9600.0
samp_rate = options.samp_rate
self.gmsk_sync = gmsk_sync.square_and_fft_sync(self._samplerate, self._bits_per_sec, self.fftlen)
if options.viterbi is True:
# calculate the required decimation and interpolation to achieve the desired samples per symbol
denom = gcd(data_rate * samples_per_symbol, samp_rate)
cr_interp = int(data_rate * samples_per_symbol / denom)
cr_decim = int(samp_rate / denom)
self.resample = blks2.rational_resampler_ccc(cr_interp, cr_decim)
# here we take a different tack and use A.A.'s CPM decomposition technique
self.clockrec = gr.clock_recovery_mm_cc(
samples_per_symbol_clockrec, 0.005 * 0.005 * 0.25, 0.5, 0.005, 0.0005
) # might have to futz with the max. deviation
(fsm, constellation, MF, N, f0T) = make_gmsk(
samples_per_symbol_viterbi, BT
) # calculate the decomposition required for demodulation
self.costas = gr.costas_loop_cc(
0.015, 0.015 * 0.015 * 0.25, 100e-6, -100e-6, 4
) # does fine freq/phase synchronization. should probably calc the coeffs instead of hardcode them.
self.streams2stream = gr.streams_to_stream(int(gr.sizeof_gr_complex * 1), int(N))
self.mf0 = gr.fir_filter_ccc(
samples_per_symbol_viterbi, MF[0].conjugate()
) # two matched filters for decomposition
self.mf1 = gr.fir_filter_ccc(samples_per_symbol_viterbi, MF[1].conjugate())
self.fo = gr.sig_source_c(
samples_per_symbol_viterbi, gr.GR_COS_WAVE, -f0T, 1, 0
) # the memoryless modulation component of the decomposition
self.fomult = gr.multiply_cc(1)
self.trellis = trellis.viterbi_combined_cb(
fsm, int(data_rate), -1, -1, int(N), constellation, trellis.TRELLIS_EUCLIDEAN
) # the actual Viterbi decoder
else:
# this is probably not optimal and someone who knows what they're doing should correct me
self.datafiltertaps = gr.firdes.root_raised_cosine(
10, # gain
self._samplerate * 32, # sample rate
self._bits_per_sec, # symbol rate
0.4, # alpha, same as BT?
50 * 32,
) # no. of taps
self.datafilter = gr.fir_filter_fff(1, self.datafiltertaps)
sensitivity = (math.pi / 2) / self._samples_per_symbol
self.demod = gr.quadrature_demod_cf(sensitivity) # param is gain
# self.clockrec = digital.clock_recovery_mm_ff(self._samples_per_symbol,0.25*self._gain_mu*self._gain_mu,self._mu,self._gain_mu,self._omega_relative_limit)
self.clockrec = gr.pfb_clock_sync_ccf(self._samples_per_symbol, 0.04, self.datafiltertaps, 32, 0, 1.15)
self.tcslicer = digital.digital.binary_slicer_fb()
# self.dfe = digital.digital.lms_dd_equalizer_cc(
# 32,
# 0.005,
# 1,
# digital.digital.constellation_bpsk()
# )
# self.delay = gr.delay(gr.sizeof_float, 64 + 16) #the correlator delays 64 bits, and the LMS delays some as well.
self.slicer = digital.digital.binary_slicer_fb()
# self.training_correlator = digital.correlate_access_code_bb("1100110011001100", 0)
# self.cma = digital.cma_equalizer_cc
# just a note here: a complex combined quad demod/slicer could be based on if's rather than an actual quad demod, right?
# in fact all the constellation decoders up to QPSK could operate on complex data w/o doing the whole atan thing
self.diff = gr.diff_decoder_bb(2)
self.invert = ais.invert() # NRZI signal diff decoded and inverted should give original signal
self.connect(self, self.gmsk_sync)
if options.viterbi is False:
self.connect(self.gmsk_sync, self.clockrec, self.demod, self.slicer, self.diff, self.invert, self)
# self.connect(self.gmsk_sync, self.demod, self.clockrec, self.tcslicer, self.training_correlator)
# self.connect(self.clockrec, self.delay, (self.dfe, 0))
# self.connect(self.training_correlator, (self.dfe, 1))
# self.connect(self.dfe, self.slicer, self.diff, self.invert, self)
else:
self.connect(self.gmsk_sync, self.costas, self.resample, self.clockrec)
self.connect(self.clockrec, (self.fomult, 0))
self.connect(self.fo, (self.fomult, 1))
self.connect(self.fomult, self.mf0)
self.connect(self.fomult, self.mf1)
self.connect(self.mf0, (self.streams2stream, 0))
self.connect(self.mf1, (self.streams2stream, 1))
self.connect(self.streams2stream, self.trellis, self.diff, self.invert, self)
def __init__(self, options):
gr.hier_block2.__init__(self, "ais_demod",
gr.io_signature(1, 1, gr.sizeof_gr_complex), # Input signature
gr.io_signature(1, 1, gr.sizeof_char)) # Output signature
self._samples_per_symbol = options[ "samples_per_symbol" ]
self._bits_per_sec = options[ "bits_per_sec" ]
self._samplerate = self._samples_per_symbol * self._bits_per_sec
self._gain_mu = options[ "gain_mu" ]
self._mu = options[ "mu" ]
self._omega_relative_limit = options[ "omega_relative_limit" ]
self.fftlen = options[ "fftlen" ]
#right now we are going to hardcode the different options for VA mode here. later on we can use configurable options
samples_per_symbol_viterbi = 2
bits_per_symbol = 2
samples_per_symbol = 6
samples_per_symbol_clockrec = samples_per_symbol / bits_per_symbol
BT = 0.4
data_rate = 9600.0
samp_rate = options[ "samp_rate" ]
self.gmsk_sync = gmsk_sync.square_and_fft_sync(self._samplerate, self._bits_per_sec, self.fftlen)
if(options[ "viterbi" ] is True):
#calculate the required decimation and interpolation to achieve the desired samples per symbol
denom = gcd(data_rate*samples_per_symbol, samp_rate)
cr_interp = int(data_rate*samples_per_symbol/denom)
cr_decim = int(samp_rate/denom)
self.resample = filter.rational_resampler_ccc(cr_interp, cr_decim)
#here we take a different tack and use A.A.'s CPM decomposition technique
self.clockrec = digital.clock_recovery_mm_cc(samples_per_symbol_clockrec, 0.005*0.005*0.25, 0.5, 0.005, 0.0005) #might have to futz with the max. deviation
(fsm, constellation, MF, N, f0T) = make_gmsk(samples_per_symbol_viterbi, BT) #calculate the decomposition required for demodulation
self.costas = digital.costas_loop_cc(0.015, 0.015*0.015*0.25, 100e-6, -100e-6, 4) #does fine freq/phase synchronization. should probably calc the coeffs instead of hardcode them.
self.streams2stream = blocks.streams_to_stream(int(gr.sizeof_gr_complex*1), int(N))
self.mf0 = filter.fir_filter_ccc(samples_per_symbol_viterbi, MF[0].conjugate()) #two matched filters for decomposition
self.mf1 = filter.fir_filter_ccc(samples_per_symbol_viterbi, MF[1].conjugate())
self.fo = analog.sig_source_c(samples_per_symbol_viterbi, gr.GR_COS_WAVE, -f0T, 1, 0) #the memoryless modulation component of the decomposition
self.fomult = blocks.multiply_cc(1)
self.trellis = trellis.viterbi_combined_cb(fsm, int(data_rate), -1, -1, int(N), constellation, trellis.TRELLIS_EUCLIDEAN) #the actual Viterbi decoder
else:
#this is probably not optimal and someone who knows what they're doing should correct me
self.datafiltertaps = filter.firdes.root_raised_cosine(10, #gain
self._samplerate*16, #sample rate
self._bits_per_sec, #symbol rate
0.4, #alpha, same as BT?
50*16) #no. of taps
sensitivity = (math.pi / 2) / self._samples_per_symbol
self.demod = analog.quadrature_demod_cf(sensitivity) #param is gain
self.clockrec = digital.pfb_clock_sync_ccf(self._samples_per_symbol,
0.04,
self.datafiltertaps, 16, 0, 1.15)
self.tcslicer = digital.digital.binary_slicer_fb()
self.slicer = digital.binary_slicer_fb()
self.diff = digital.diff_decoder_bb(2)
self.invert = ais.invert() #NRZI signal diff decoded and inverted should give original signal
self.connect(self, self.gmsk_sync)
if(options[ "viterbi" ] is False):
self.connect(self.gmsk_sync, self.clockrec, self.demod, self.slicer, self.diff, self.invert, self)
else:
self.connect(self.gmsk_sync, self.costas, self.resample, self.clockrec)
self.connect(self.clockrec, (self.fomult, 0))
self.connect(self.fo, (self.fomult, 1))
self.connect(self.fomult, self.mf0)
self.connect(self.fomult, self.mf1)
self.connect(self.mf0, (self.streams2stream, 0))
self.connect(self.mf1, (self.streams2stream, 1))
self.connect(self.streams2stream, self.trellis, self.diff, self.invert, self)
