def __init__(self, length, debug=False):
"""
Hierarchical block to detect Null symbols
@param length length of the Null symbol (in samples)
@param debug whether to write signals out to files
"""
gr.hier_block2.__init__(self, "detect_null",
gr.io_signature(1, 1, gr.sizeof_gr_complex), # input signature
gr.io_signature(1, 1, gr.sizeof_char)) # output signature
# get the magnitude squared
self.ns_c2magsquared = gr.complex_to_mag_squared()
self.ns_moving_sum = dabp.moving_sum_ff(length)
self.ns_invert = gr.multiply_const_ff(-1)
# peak detector on the inverted, summed up signal -> we get the nulls (i.e. the position of the start of a frame)
self.ns_peak_detect = gr.peak_detector_fb(0.6,0.7,10,0.0001) # mostly found by try and error -> remember that the values are negative!
# connect it all
self.connect(self, self.ns_c2magsquared, self.ns_moving_sum, self.ns_invert, self.ns_peak_detect, self)
if debug:
self.connect(self.ns_invert, gr.file_sink(gr.sizeof_float, "debug/ofdm_sync_dabp_ns_filter_inv_f.dat"))
self.connect(self.ns_peak_detect,gr.file_sink(gr.sizeof_char, "debug/ofdm_sync_dabp_peak_detect_b.dat"))
def __init__(self,options,Freq): gr.top_block.__init__(self) if options.input_file == "": self.IS_USRP2 = True else: self.IS_USRP2 = False #self.min_freq = options.start #self.max_freq = options.stop self.min_freq = Freq.value-(3*10**6) # same as that of the transmitter bandwidth ie 6MHZ approx for a given value of decimation line option any more self.max_freq = Freq.value+(3*10**6) if self.min_freq > self.max_freq: self.min_freq, self.max_freq = self.max_freq, self.min_freq # swap them print "Start and stop frequencies order swapped!" self.fft_size = options.fft_size self.ofdm_bins = options.sense_bins # build graph s2v = gr.stream_to_vector(gr.sizeof_gr_complex, self.fft_size) mywindow = window.blackmanharris(self.fft_size) fft = gr.fft_vcc(self.fft_size, True, mywindow) power = 0 for tap in mywindow: power += tap*tap c2mag = gr.complex_to_mag_squared(self.fft_size) #log = gr.nlog10_ff(10, self.fft_size, -20*math.log10(self.fft_size)-10*math.log10(power/self.fft_size)) # modifications for USRP2 if self.IS_USRP2: self.u = uhd.usrp_source(options.args,uhd.io_type.COMPLEX_FLOAT32,num_channels=1) # Modified Line # self.u.set_decim(options.decim) # samp_rate = self.u.adc_rate()/self.u.decim() samp_rate = 100e6/options.decim # modified sampling rate self.u.set_samp_rate(samp_rate) else: self.u = gr.file_source(gr.sizeof_gr_complex,options.input_file, True) samp_rate = 100e6 /options.decim # modified sampling rate self.freq_step =0 #0.75* samp_rate self.min_center_freq = (self.min_freq + self.max_freq)/2 global BW BW = self.max_freq - self.min_freq global size size=self.fft_size global ofdm_bins ofdm_bins = self.ofdm_bins global usr #global thrshold_inorder usr=samp_rate nsteps = 10 #math.ceil((self.max_freq - self.min_freq) / self.freq_step) self.max_center_freq = self.min_center_freq + (nsteps * self.freq_step) self.next_freq = self.min_center_freq tune_delay = max(0, int(round(options.tune_delay * samp_rate / self.fft_size))) # in fft_frames dwell_delay = max(1, int(round(options.dwell_delay * samp_rate / self.fft_size))) # in fft_frames self.msgq = gr.msg_queue(16) # thread-safe message queue self._tune_callback = tune(self) # hang on to this to keep it from being GC'd stats = gr.bin_statistics_f(self.fft_size, self.msgq, self._tune_callback, tune_delay, dwell_delay) # control scanning and record frequency domain statistics self.connect(self.u, s2v, fft,c2mag,stats) if options.gain is None: g = self.u.get_gain_range() options.gain = float(g.start()+g.stop())/2 # if no gain was specified, use the mid-point in dB
def __init__(self, uhd_address, options):
gr.top_block.__init__(self)
self.uhd_addr = uhd_address
self.freq = options.freq
self.samp_rate = options.samp_rate
self.gain = options.gain
self.threshold = options.threshold
self.trigger = options.trigger
self.uhd_src = uhd.single_usrp_source(
device_addr=self.uhd_addr, stream_args=uhd.stream_args('fc32'))
self.uhd_src.set_samp_rate(self.samp_rate)
self.uhd_src.set_center_freq(self.freq, 0)
self.uhd_src.set_gain(self.gain, 0)
taps = firdes.low_pass_2(1, 1, 0.4, 0.1, 60)
self.chanfilt = gr.fir_filter_ccc(10, taps)
self.tagger = gr.burst_tagger(gr.sizeof_gr_complex)
# Dummy signaler to collect a burst on known periods
data = 1000 * [
0,
] + 1000 * [
1,
]
self.signal = gr.vector_source_s(data, True)
# Energy detector to get signal burst
## use squelch to detect energy
self.det = gr.simple_squelch_cc(self.threshold, 0.01)
## convert to mag squared (float)
self.c2m = gr.complex_to_mag_squared()
## average to debounce
self.avg = gr.single_pole_iir_filter_ff(0.01)
## rescale signal for conversion to short
self.scale = gr.multiply_const_ff(2**16)
## signal input uses shorts
self.f2s = gr.float_to_short()
# Use file sink burst tagger to capture bursts
self.fsnk = gr.tagged_file_sink(gr.sizeof_gr_complex, self.samp_rate)
##################################################
# Connections
##################################################
self.connect((self.uhd_src, 0), (self.tagger, 0))
self.connect((self.tagger, 0), (self.fsnk, 0))
if self.trigger:
# Connect a dummy signaler to the burst tagger
self.connect((self.signal, 0), (self.tagger, 1))
else:
# Connect an energy detector signaler to the burst tagger
self.connect(self.uhd_src, self.det)
self.connect(self.det, self.c2m, self.avg, self.scale, self.f2s)
self.connect(self.f2s, (self.tagger, 1))
def setup_normal(self, setimode): self.setup_radiometer_common(1) self.head = self.u if (self.use_notches == True): self.shead = self.notch_filt else: self.shead = self.u if setimode == False: self.detector = gr.complex_to_mag_squared() self.connect(self.shead, self.scope) if (self.use_notches == False): self.connect(self.head, self.detector, self.mute, self.reference_level, self.integrator, self.keepn, self.cal_mult, self.cal_offs, self.chart) else: self.connect(self.head, self.notch_filt, self.detector, self.mute, self.reference_level, self.integrator, self.keepn, self.cal_mult, self.cal_offs, self.chart) self.connect(self.cal_offs, self.probe) # # Add a side-chain detector chain, with a different integrator, for sampling # The reference channel data # This is used to derive the offset value for self.reference_level, used above # if (self.switch_mode == True): self.connect(self.detector, self.cmute, self.cintegrator, self.swkeep, self.cprobe) return
def __init__(self, length, debug=False): """ Hierarchical block to detect Null symbols @param length length of the Null symbol (in samples) @param debug whether to write signals out to files """ gr.hier_block2.__init__(self,"detect_null", gr.io_signature(1, 1, gr.sizeof_gr_complex), # input signature gr.io_signature(1, 1, gr.sizeof_char)) # output signature # get the magnitude squared self.ns_c2magsquared = gr.complex_to_mag_squared() # this wastes cpu cycles: # ns_detect_taps = [1]*length # self.ns_moving_sum = gr.fir_filter_fff(1,ns_detect_taps) # this isn't better: #self.ns_filter = gr.iir_filter_ffd([1]+[0]*(length-1)+[-1],[0,1]) # this does the same again, but is actually faster (outsourced to an independent block ..): self.ns_moving_sum = dab_swig.moving_sum_ff(length) self.ns_invert = gr.multiply_const_ff(-1) # peak detector on the inverted, summed up signal -> we get the nulls (i.e. the position of the start of a frame) self.ns_peak_detect = gr.peak_detector_fb(0.6,0.7,10,0.0001) # mostly found by try and error -> remember that the values are negative! # connect it all self.connect(self, self.ns_c2magsquared, self.ns_moving_sum, self.ns_invert, self.ns_peak_detect, self) if debug: self.connect(self.ns_invert, gr.file_sink(gr.sizeof_float, "debug/ofdm_sync_dab_ns_filter_inv_f.dat")) self.connect(self.ns_peak_detect,gr.file_sink(gr.sizeof_char, "debug/ofdm_sync_dab_peak_detect_b.dat"))
def __init__(self, uhd_address, options):
gr.top_block.__init__(self)
self.uhd_addr = uhd_address
self.freq = options.freq
self.samp_rate = options.samp_rate
self.gain = options.gain
self.threshold = options.threshold
self.trigger = options.trigger
self.uhd_src = uhd.single_usrp_source(
device_addr=self.uhd_addr,
io_type=uhd.io_type_t.COMPLEX_FLOAT32,
num_channels=1,
)
self.uhd_src.set_samp_rate(self.samp_rate)
self.uhd_src.set_center_freq(self.freq, 0)
self.uhd_src.set_gain(self.gain, 0)
taps = firdes.low_pass_2(1, 1, 0.4, 0.1, 60)
self.chanfilt = gr.fir_filter_ccc(10, taps)
self.tagger = gr.burst_tagger(gr.sizeof_gr_complex)
# Dummy signaler to collect a burst on known periods
data = 1000*[0,] + 1000*[1,]
self.signal = gr.vector_source_s(data, True)
# Energy detector to get signal burst
## use squelch to detect energy
self.det = gr.simple_squelch_cc(self.threshold, 0.01)
## convert to mag squared (float)
self.c2m = gr.complex_to_mag_squared()
## average to debounce
self.avg = gr.single_pole_iir_filter_ff(0.01)
## rescale signal for conversion to short
self.scale = gr.multiply_const_ff(2**16)
## signal input uses shorts
self.f2s = gr.float_to_short()
# Use file sink burst tagger to capture bursts
self.fsnk = gr.tagged_file_sink(gr.sizeof_gr_complex, self.samp_rate)
##################################################
# Connections
##################################################
self.connect((self.uhd_src, 0), (self.tagger, 0))
self.connect((self.tagger, 0), (self.fsnk, 0))
if self.trigger:
# Connect a dummy signaler to the burst tagger
self.connect((self.signal, 0), (self.tagger, 1))
else:
# Connect an energy detector signaler to the burst tagger
self.connect(self.uhd_src, self.det)
self.connect(self.det, self.c2m, self.avg, self.scale, self.f2s)
self.connect(self.f2s, (self.tagger, 1))
def ms_to_file(hb, block, filename, N=4096, delay=0, fft=False, scale=1):
streamsize = determine_streamsize(block)
vlen = streamsize / gr.sizeof_gr_complex
blks = [block]
if fft and vlen > 1:
gr_fft = fft_blocks.fft_vcc(vlen, True, [], True)
blks.append(gr_fft)
mag_sqrd = gr.complex_to_mag_squared(vlen)
blks.append(mag_sqrd)
if vlen > 1:
v2s = blocks.vector_to_stream(gr.sizeof_float, vlen)
blks.append(v2s)
if delay != 0:
delayline = delayline_ff(delay)
blks.append(delayline)
gr_scale = gr.multiply_const_ff(scale)
blks.append(gr_scale)
filter = gr.fir_filter_fff(1, [1.0 / N] * N)
blks.append(filter)
for i in range(len(blks) - 1):
hb.connect(blks[i], blks[i + 1])
log_to_file(hb, filter, filename)
def __init__(self, queue, options, args): #grc_wxgui.top_block_gui.__init__(self, title="Top Block") gr.top_block.__init__(self) ################################################## # Variables ################################################## self.samp_rate = samp_rate = 100e6/512 ################################################## # Blocks ################################################## self.gr_complex_to_mag_squared_0 = gr.complex_to_mag_squared(1) self.gr_keep_one_in_n_0 = gr.keep_one_in_n(gr.sizeof_gr_complex*1, 10) self.keyfob_msg = keyfob.msg(queue, samp_rate/10.0, options.threshold) self.uhd_single_usrp_source_0 = uhd.single_usrp_source( device_addr="", io_type=uhd.io_type_t.COMPLEX_FLOAT32, num_channels=1, ) _clk_cfg = uhd.clock_config_t() _clk_cfg.ref_source = uhd.clock_config_t.REF_SMA _clk_cfg.pps_source = uhd.clock_config_t.PPS_SMA _clk_cfg.pps_polarity = uhd.clock_config_t.PPS_POS #self.uhd_single_usrp_source_0.set_clock_config(_clk_cfg); self.uhd_single_usrp_source_0.set_samp_rate(samp_rate) self.uhd_single_usrp_source_0.set_center_freq(options.freq, 0) self.uhd_single_usrp_source_0.set_gain(40, 0) ################################################## # Connections ################################################## self.connect((self.uhd_single_usrp_source_0, 0), (self.gr_keep_one_in_n_0, 0)) self.connect((self.gr_keep_one_in_n_0, 0), (self.gr_complex_to_mag_squared_0, 0)) self.connect((self.gr_complex_to_mag_squared_0, 0), self.keyfob_msg)
def __init__(self,options,Freq): gr.top_block.__init__(self) if options.input_file == "": self.IS_USRP2 = True else: self.IS_USRP2 = False #self.min_freq = options.start #self.max_freq = options.stop self.min_freq = Freq.value-(3*10**6) # same as that of the transmitter bandwidth ie 6MHZ approx for a given value of decimation line option any more self.max_freq = Freq.value+(3*10**6) if self.min_freq > self.max_freq: self.min_freq, self.max_freq = self.max_freq, self.min_freq # swap them print "Start and stop frequencies order swapped!" self.fft_size = options.fft_size self.ofdm_bins = options.sense_bins # build graph s2v = gr.stream_to_vector(gr.sizeof_gr_complex, self.fft_size) mywindow = window.blackmanharris(self.fft_size) fft = gr.fft_vcc(self.fft_size, True, mywindow) power = 0 for tap in mywindow: power += tap*tap c2mag = gr.complex_to_mag_squared(self.fft_size) #log = gr.nlog10_ff(10, self.fft_size, -20*math.log10(self.fft_size)-10*math.log10(power/self.fft_size)) # modifications for USRP2 if self.IS_USRP2: self.u = uhd.usrp_source(options.args,uhd.io_type.COMPLEX_FLOAT32,num_channels=1) # Modified Line # self.u.set_decim(options.decim) # samp_rate = self.u.adc_rate()/self.u.decim() samp_rate = 100e6/options.decim # modified sampling rate self.u.set_samp_rate(samp_rate) else: self.u = gr.file_source(gr.sizeof_gr_complex,options.input_file, True) samp_rate = 100e6 /options.decim # modified sampling rate self.freq_step =0 #0.75* samp_rate self.min_center_freq = (self.min_freq + self.max_freq)/2 global BW BW = self.max_freq - self.min_freq global size size=self.fft_size global ofdm_bins ofdm_bins = self.ofdm_bins global usr #global thrshold_inorder usr=samp_rate nsteps = 10 self.max_center_freq = self.min_center_freq + (nsteps * self.freq_step) self.next_freq = self.min_center_freq tune_delay = max(0, int(round(options.tune_delay * samp_rate / self.fft_size))) # in fft_frames dwell_delay = max(1, int(round(options.dwell_delay * samp_rate / self.fft_size))) # in fft_frames self.msgq = gr.msg_queue(16) # thread-safe message queue self._tune_callback = tune(self) # hang on to this to keep it from being GC'd stats = gr.bin_statistics_f(self.fft_size, self.msgq, self._tune_callback, tune_delay, dwell_delay) # control scanning and record frequency domain statistics self.connect(self.u, s2v, fft,c2mag,stats) if options.gain is None: g = self.u.get_gain_range() options.gain = float(g.start()+g.stop())/2 # if no gain was specified, use the mid-point in dB
def __init__(self, fft_length):
gr.hier_block2.__init__(self, "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)
nominator = schmidl_nominator(fft_length)
# 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(self.input, 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)
# calculate epsilon from P(d), epsilon is normalized fractional frequency offset
#angle = gr.complex_to_arg()
#self.epsilon = gr.multiply_const_ff(1.0/math.pi)
#self.connect(nominator, angle, self.epsilon)
try:
gr.hier_block.update_var_names(self, "schmidl", vars())
gr.hier_block.update_var_names(self, "schmidl", vars(self))
except:
pass
def ms_to_file(hb,block,filename,N=4096,delay=0,fft=False,scale=1):
streamsize = determine_streamsize(block)
vlen = streamsize/gr.sizeof_gr_complex
blks = [block]
if fft and vlen > 1:
gr_fft = fft_blocks.fft_vcc(vlen,True,[],True)
blks.append(gr_fft)
mag_sqrd = gr.complex_to_mag_squared(vlen)
blks.append(mag_sqrd)
if vlen > 1:
v2s = blocks.vector_to_stream(gr.sizeof_float,vlen)
blks.append(v2s)
if delay != 0:
delayline = delayline_ff(delay)
blks.append(delayline)
gr_scale = gr.multiply_const_ff(scale)
blks.append(gr_scale)
filter = gr.fir_filter_fff(1,[1.0/N]*N)
blks.append(filter)
for i in range(len(blks)-1):
hb.connect(blks[i],blks[i+1])
log_to_file(hb,filter,filename)
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, samp_rate=1600000, samp_per_sym=16, verbose=0, msgq=0, freq_error=-0.0025000):
gr.hier_block2.__init__(
self, "Wmbus Phy1", gr.io_signature(1, 1, gr.sizeof_gr_complex * 1), gr.io_signature(0, 0, 0)
)
##################################################
# Parameters
##################################################
self.samp_rate = samp_rate
self.samp_per_sym = samp_per_sym
self.verbose = verbose
self.msgq = msgq
self.freq_error = freq_error
##################################################
# Blocks
##################################################
self.wmbus_demod_0 = wmbus_demod(samp_rate=1600000, samp_per_sym=16, freq_error=-0.0025000)
self.gr_nlog10_ff_0 = gr.nlog10_ff(10, 1, 0)
self.gr_complex_to_mag_squared_0 = gr.complex_to_mag_squared(1)
self.fir_filter_xxx_0 = filter.fir_filter_fff(samp_per_sym, (16 * [1.0 / 16]))
self.any_sink_0_1 = mbus.framer(msgq, verbose)
self.any_0 = mbus.correlate_preamble()
##################################################
# Connections
##################################################
self.connect((self.any_0, 0), (self.any_sink_0_1, 0))
self.connect((self.gr_complex_to_mag_squared_0, 0), (self.gr_nlog10_ff_0, 0))
self.connect((self.gr_nlog10_ff_0, 0), (self.fir_filter_xxx_0, 0))
self.connect((self.fir_filter_xxx_0, 0), (self.any_sink_0_1, 1))
self.connect((self, 0), (self.gr_complex_to_mag_squared_0, 0))
self.connect((self, 0), (self.wmbus_demod_0, 0))
self.connect((self.wmbus_demod_0, 0), (self.any_0, 0))
def __init__(self, dpss, fftshift=False):
gr.hier_block2.__init__(self, "eigenspectrum",
gr.io_signature(1, 1, gr.sizeof_gr_complex*len(dpss)),
gr.io_signature(1, 1, gr.sizeof_float*len(dpss)))
self.window = dpss
self.fft = gr.fft_vcc(len(dpss), True, self.window, fftshift)
self.c2mag = gr.complex_to_mag_squared(len(dpss))
self.connect(self, self.fft, self.c2mag, self)
def __init__(self, dpss, fftshift=False):
gr.hier_block2.__init__(self, "eigenspectrum",
gr.io_signature(1, 1, gr.sizeof_gr_complex*len(dpss)),
gr.io_signature(1, 1, gr.sizeof_float*len(dpss)))
self.window = dpss
self.fft = gr.fft_vcc(len(dpss), True, self.window, fftshift)
self.c2mag = gr.complex_to_mag_squared(len(dpss))
self.connect(self, self.fft, self.c2mag, 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 __init__(self):
sense_band_start=900*10**6
sense_band_stop=940*10**6
self.fft_size = options.fft_size
self.ofdm_bins = options.sense_bins
# build graph
s2v = gr.stream_to_vector(gr.sizeof_gr_complex, self.fft_size)
mywindow = window.blackmanharris(self.fft_size)
fft = gr.fft_vcc(self.fft_size, True, mywindow)
power = 0
for tap in mywindow:
power += tap*tap
c2mag = gr.complex_to_mag_squared(self.fft_size)
#log = gr.nlog10_ff(10, self.fft_size, -20*math.log10(self.fft_size)-10*math.log10(power/self.fft_size))
# modifications for USRP2
print "*******************in sensor init********************"
if self.IS_USRP2:
self.u = usrp2.source_32fc(options.interface, options.MAC_addr)
self.u.set_decim(options.decim)
samp_rate = self.u.adc_rate() / self.u.decim()
else:
self.u = gr.file_source(gr.sizeof_gr_complex,options.input_file, True)
samp_rate = 100e6 / options.decim
self.freq_step =0.75* samp_rate
#self.min_center_freq = (self.min_freq + self.max_freq)/2
global BW
BW = 0.75* samp_rate #self.max_freq - self.min_freq
global size
size=self.fft_size
global ofdm_bins
ofdm_bins = self.ofdm_bins
global usr
#global thrshold_inorder
usr=samp_rate
nsteps = 10 #math.ceil((self.max_freq - self.min_freq) / self.freq_step)
self.max_center_freq = self.min_center_freq + (nsteps * self.freq_step)
self.next_freq = self.min_center_freq
tune_delay = max(0, int(round(options.tune_delay * samp_rate / self.fft_size))) # in fft_frames
print tune_delay
dwell_delay = max(1, int(round(options.dwell_delay * samp_rate / self.fft_size))) # in fft_frames
print dwell_delay
self.msgq = gr.msg_queue(16)
self._tune_callback = tune(self)
# hang on to this to keep it from being GC'd
stats = gr.bin_statistics_f(self.fft_size, self.msgq, self._tune_callback, tune_delay,
dwell_delay)
self.connect(self.u, s2v, fft,c2mag,stats)
if options.gain is None:
# if no gain was specified, use the mid-point in dB
g = self.u.gain_range()
options.gain = float(g[0]+g[1])/2
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):
gr.hier_block2.__init__(self, "symbol_0_slicer",
gr.io_signature(2, 2, gr.sizeof_gr_complex),
gr.io_signature(1, 1, gr.sizeof_float))
adder = gr.add_cc()
to_mag = gr.complex_to_mag_squared()
self.connect((self, 0), (adder, 0))
self.connect((self, 1), (adder, 1))
self.connect(adder, to_mag, self)
def __init__(self):
gr.hier_block2.__init__(self, "symbol_1_slicer",
gr.io_signature(2, 2, gr.sizeof_gr_complex),
gr.io_signature(1, 1, gr.sizeof_float))
diff = gr.sub_cc()
to_mag = gr.complex_to_mag_squared()
self.connect((self, 0), (diff, 0))
self.connect((self, 1), (diff, 1))
self.connect(diff, to_mag, self)
def __init__(self,
N_id_2,
decim=16,
avg_halfframes=2 * 8,
freq_corr=0,
dump=None):
gr.hier_block2.__init__(
self,
"PSS correlator",
gr.io_signature(1, 1, gr.sizeof_gr_complex),
gr.io_signature(1, 1, gr.sizeof_float),
)
vec_half_frame = 30720 * 5 / decim
self.taps = []
for i in range(0, 3):
self.taps.append(
gen_pss_td(i, N_re=2048 / decim,
freq_corr=freq_corr).get_data_conj_rev())
self.corr = filter.fir_filter_ccc(1, self.taps[N_id_2])
self.mag = gr.complex_to_mag_squared()
self.vec = gr.stream_to_vector(gr.sizeof_float * 1, vec_half_frame)
self.deint = gr.deinterleave(gr.sizeof_float * vec_half_frame)
self.add = gr.add_vff(vec_half_frame)
self.argmax = gr.argmax_fs(vec_half_frame)
self.null = gr.null_sink(gr.sizeof_short * 1)
self.max = gr.max_ff(vec_half_frame)
self.to_float = gr.short_to_float(1, 1. / decim)
self.interleave = gr.interleave(gr.sizeof_float)
#self.framestart = gr.add_const_ii(-160-144*5-2048*6+30720*5)
self.connect(self, self.corr, self.mag, self.vec)
self.connect((self.argmax, 1), self.null)
#self.connect(self.argmax, self.to_float, self.to_int, self.framestart, self)
self.connect(self.argmax, self.to_float, self.interleave, self)
self.connect(self.max, (self.interleave, 1))
if avg_halfframes == 1:
self.connect(self.vec, self.argmax)
self.connect(self.vec, self.max)
else:
self.connect(self.vec, self.deint)
self.connect(self.add, self.argmax)
self.connect(self.add, self.max)
for i in range(0, avg_halfframes):
self.connect((self.deint, i), (self.add, i))
if dump != None:
self.connect(
self.mag,
gr.file_sink(gr.sizeof_float, dump + "_pss_corr_f.cfile"))
self.connect(
self.add,
gr.file_sink(gr.sizeof_float * vec_half_frame,
dump + "_pss_corr_add_f.cfile"))
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 test_complex_to_mag_squared(self):
src_data = (0, 1, -1, 3 + 4j, -3 - 4j, -3 + 4j)
expected_result = (0, 1, 1, 25, 25, 25)
src = gr.vector_source_c(src_data)
op = gr.complex_to_mag_squared()
dst = gr.vector_sink_f()
self.tb.connect(src, op)
self.tb.connect(op, dst)
self.tb.run()
actual_result = dst.data()
self.assertFloatTuplesAlmostEqual(expected_result, actual_result, 5)
def test_complex_to_mag_squared (self):
src_data = (0, 1, -1, 3+4j, -3-4j, -3+4j)
expected_result = (0, 1, 1, 25, 25, 25)
src = gr.vector_source_c (src_data)
op = gr.complex_to_mag_squared ()
dst = gr.vector_sink_f ()
self.tb.connect (src, op)
self.tb.connect (op, dst)
self.tb.run ()
actual_result = dst.data ()
self.assertFloatTuplesAlmostEqual (expected_result, actual_result,5)
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, data_tones, num_symbols, mode=0):
symbol_size = data_tones * gr.sizeof_gr_complex
gr.hier_block2.__init__(self, "SNR",
gr.io_signature2(2,2, symbol_size, symbol_size),
gr.io_signature(1,1, gr.sizeof_float))
sub = gr.sub_cc(data_tones)
self.connect((self,0), (sub,0))
self.connect((self,1), (sub,1))
err = gr.complex_to_mag_squared(data_tones);
self.connect(sub, err);
pow = gr.complex_to_mag_squared(data_tones);
self.connect((self,1), pow);
if mode == 0:
# one snr per symbol (num_symbols is ignored)
snr = gr.divide_ff()
self.connect(pow, gr.vector_to_stream(gr.sizeof_float, data_tones),
gr.integrate_ff(data_tones), (snr,0))
self.connect(err, gr.vector_to_stream(gr.sizeof_float, data_tones),
gr.integrate_ff(data_tones), (snr,1))
out = snr
elif mode == 1:
# one snr per packet
snr = gr.divide_ff()
self.connect(pow, gr.vector_to_stream(gr.sizeof_float, data_tones),
gr.integrate_ff(data_tones*num_symbols), (snr,0))
self.connect(err, gr.vector_to_stream(gr.sizeof_float, data_tones),
gr.integrate_ff(data_tones*num_symbols), (snr,1))
out = snr
elif mode == 2:
# one snr per frequency bin
snr = gr.divide_ff(data_tones)
self.connect(pow, raw.symbol_avg(data_tones, num_symbols), (snr,0))
self.connect(err, raw.symbol_avg(data_tones, num_symbols), (snr,1))
out = gr.vector_to_stream(gr.sizeof_float, data_tones)
self.connect(snr, out)
self.connect(out, gr.nlog10_ff(10), self)
def __init__(self):
gr.hier_block2.__init__(self,
"symbol_1_slicer",
gr.io_signature(2, 2, gr.sizeof_gr_complex),
gr.io_signature(1, 1, gr.sizeof_float))
diff = gr.sub_cc()
to_mag = gr.complex_to_mag_squared()
self.connect((self, 0), (diff, 0))
self.connect((self, 1), (diff, 1))
self.connect(diff,
to_mag,
self)
def __init__(self):
gr.hier_block2.__init__(self,
"symbol_0_slicer",
gr.io_signature(2, 2, gr.sizeof_gr_complex),
gr.io_signature(1, 1, gr.sizeof_float))
adder = gr.add_cc()
to_mag = gr.complex_to_mag_squared()
self.connect((self, 0), (adder, 0))
self.connect((self, 1), (adder, 1))
self.connect(adder,
to_mag,
self)
def __init__(self,
sample_rate,
fft_size,
ref_scale,
frame_rate,
avg_alpha,
average,
win=None):
"""
Create an log10(abs(fft)) stream chain.
Provide access to the setting the filter and sample rate.
@param sample_rate Incoming stream sample rate
@param fft_size Number of FFT bins
@param ref_scale Sets 0 dB value input amplitude
@param frame_rate Output frame rate
@param avg_alpha FFT averaging (over time) constant [0.0-1.0]
@param average Whether to average [True, False]
@param win the window taps generation function
"""
gr.hier_block2.__init__(
self,
self._name,
gr.io_signature(1, 1, self._item_size), # Input signature
gr.io_signature(1, 1,
gr.sizeof_float * fft_size)) # Output signature
self._sd = stream_to_vector_decimator(item_size=self._item_size,
sample_rate=sample_rate,
vec_rate=frame_rate,
vec_len=fft_size)
if win is None: win = window.blackmanharris
fft_window = win(fft_size)
fft = self._fft_block[0](fft_size, True, fft_window)
window_power = sum(map(lambda x: x * x, fft_window))
c2magsq = gr.complex_to_mag_squared(fft_size)
self._avg = gr.single_pole_iir_filter_ff(1.0, fft_size)
self._log = gr.nlog10_ff(
10,
fft_size,
-20 * math.log10(fft_size) # Adjust for number of bins
- 10 *
math.log10(window_power / fft_size) # Adjust for windowing loss
- 20 * math.log10(ref_scale / 2)) # Adjust for reference scale
self.connect(self, self._sd, fft, c2magsq, self._avg, self._log, self)
self._average = average
self._avg_alpha = avg_alpha
self.set_avg_alpha(avg_alpha)
self.set_average(average)
def __init__( self, vlen, startup ):
gr.hier_block2.__init__( self,
"vector_acc_se",
gr.io_signature( 1, 1, gr.sizeof_gr_complex * vlen ),
gr.io_signature( 1, 1, gr.sizeof_float ) )
squared_error_subc = gr.complex_to_mag_squared( vlen )
squared_error_block = ofdm.vector_sum_vff( vlen )
accumulated_squared_error = ofdm.accumulator_ff()
if startup > 0:
startup_skip = gr.skiphead( gr.sizeof_gr_complex * vlen, startup )
self.connect( self, startup_skip, squared_error_subc, squared_error_block,
accumulated_squared_error, self )
else:
self.connect( self, squared_error_subc, squared_error_block,
accumulated_squared_error, self )
def __init__(self, fft_length):
gr.hier_block2.__init__(self, "schmidl_denominator",
gr.io_signature(1, 1, gr.sizeof_gr_complex),
gr.io_signature(1, 1, gr.sizeof_float))
# R(d) = sum(0 to L-1, |r_d|**2)
r_mag_sqrd = gr.complex_to_mag_squared()
r_mov_avg = gr.fir_filter_fff(1, [0.5 for i in range(fft_length)])
#r_mov_avg_half = gr.multiply_const_ff(0.5)
#r_mov_avg = moving_sum_cc(fft_length)
self.connect(self, r_mag_sqrd, r_mov_avg, self)
# r_mov_avg = R(d)
try:
gr.hier_block.update_var_names(self, "schmidl_denom", vars())
gr.hier_block.update_var_names(self, "schmidl_denom", vars(self))
except:
pass
def __init__(self, fft_length):
gr.hier_block2.__init__(self, "schmidl_denominator",
gr.io_signature(1,1,gr.sizeof_gr_complex),
gr.io_signature(1,1,gr.sizeof_float))
# R(d) = sum(0 to L-1, |r_d|**2)
r_mag_sqrd = gr.complex_to_mag_squared()
r_mov_avg = gr.fir_filter_fff(1,[0.5 for i in range(fft_length)])
#r_mov_avg_half = gr.multiply_const_ff(0.5)
#r_mov_avg = moving_sum_cc(fft_length)
self.connect(self, r_mag_sqrd, r_mov_avg, self)
# r_mov_avg = R(d)
try:
gr.hier_block.update_var_names(self, "schmidl_denom", vars())
gr.hier_block.update_var_names(self, "schmidl_denom", vars(self))
except:
pass
def __init__(self, N_id_2, decim=16, avg_halfframes=2*8, freq_corr=0, dump=None):
gr.hier_block2.__init__(
self, "PSS correlator",
gr.io_signature(1, 1, gr.sizeof_gr_complex),
gr.io_signature(1, 1, gr.sizeof_float),
)
vec_half_frame = 30720*5/decim
self.taps = []
for i in range(0,3):
self.taps.append(gen_pss_td(i, N_re=2048/decim, freq_corr=freq_corr).get_data_conj_rev())
self.corr = filter.fir_filter_ccc(1, self.taps[N_id_2])
self.mag = gr.complex_to_mag_squared()
self.vec = gr.stream_to_vector(gr.sizeof_float*1, vec_half_frame)
self.deint = gr.deinterleave(gr.sizeof_float*vec_half_frame)
self.add = gr.add_vff(vec_half_frame)
self.argmax = gr.argmax_fs(vec_half_frame)
self.null = gr.null_sink(gr.sizeof_short*1)
self.max = gr.max_ff(vec_half_frame)
self.to_float = gr.short_to_float(1, 1./decim)
self.interleave = gr.interleave(gr.sizeof_float)
#self.framestart = gr.add_const_ii(-160-144*5-2048*6+30720*5)
self.connect(self, self.corr, self.mag, self.vec)
self.connect((self.argmax,1), self.null)
#self.connect(self.argmax, self.to_float, self.to_int, self.framestart, self)
self.connect(self.argmax, self.to_float, self.interleave, self)
self.connect(self.max, (self.interleave,1))
if avg_halfframes == 1:
self.connect(self.vec, self.argmax)
self.connect(self.vec, self.max)
else:
self.connect(self.vec, self.deint)
self.connect(self.add, self.argmax)
self.connect(self.add, self.max)
for i in range(0, avg_halfframes):
self.connect((self.deint, i), (self.add, i))
if dump != None:
self.connect(self.mag, gr.file_sink(gr.sizeof_float, dump + "_pss_corr_f.cfile"))
self.connect(self.add, gr.file_sink(gr.sizeof_float*vec_half_frame, dump + "_pss_corr_add_f.cfile"))
def __init__(self, fs, fd, svn, alpha, dump_bins=False):
fft_size = int( 1e-3*fs)
gr.hier_block2.__init__(self,
"single_channel_correlator",
gr.io_signature(1,1, gr.sizeof_gr_complex*fft_size),
gr.io_signature(1,1, gr.sizeof_float*fft_size))
lc = local_code(svn=svn, fs=fs, fd=fd)
mult = gr.multiply_vcc(fft_size)
ifft = gr.fft_vcc(fft_size, False, [])
mag = gr.complex_to_mag_squared(fft_size)
self.iir = gr.single_pole_iir_filter_ff( alpha, fft_size)
self.connect( self, (mult, 0))
self.connect( lc, (mult, 1))
self.connect( mult, ifft, mag, self.iir, self)
if dump_bins == True:
self.connect_debug_sink(self.iir,fft_size,'/home/trondd/opengnss_output', fd)
def __init__(self, fs, fd, svn, alpha, dump_bins=False):
fft_size = int(1e-3 * fs)
gr.hier_block2.__init__(
self, "single_channel_correlator",
gr.io_signature(1, 1, gr.sizeof_gr_complex * fft_size),
gr.io_signature(1, 1, gr.sizeof_float * fft_size))
lc = local_code(svn=svn, fs=fs, fd=fd)
mult = gr.multiply_vcc(fft_size)
ifft = gr.fft_vcc(fft_size, False, [])
mag = gr.complex_to_mag_squared(fft_size)
self.iir = gr.single_pole_iir_filter_ff(alpha, fft_size)
self.connect(self, (mult, 0))
self.connect(lc, (mult, 1))
self.connect(mult, ifft, mag, self.iir, self)
if dump_bins == True:
self.connect_debug_sink(self.iir, fft_size,
'/home/trondd/opengnss_output', fd)
def __init__(self, samp_rate=1600000, samp_per_sym=16, verbose=0, msgq=0, freq_error=-0.0025000): gr.hier_block2.__init__( self, "Wmbus Phy1", gr.io_signature(1, 1, gr.sizeof_gr_complex*1), gr.io_signature(0, 0, 0), ) ################################################## # Parameters ################################################## self.samp_rate = samp_rate self.samp_per_sym = samp_per_sym self.verbose = verbose self.msgq = msgq self.freq_error = freq_error ################################################## # Blocks ################################################## self.wmbus_demod_0 = wmbus_demod( samp_rate=1600000, samp_per_sym=16, freq_error=-0.0025000, ) self.gr_nlog10_ff_0 = gr.nlog10_ff(10, 1, 0) self.gr_complex_to_mag_squared_0 = gr.complex_to_mag_squared(1) self.fir_filter_xxx_0 = filter.fir_filter_fff(samp_per_sym, (16*[1./16])) self.any_sink_0_1 = mbus.framer(msgq, verbose) self.any_0 = mbus.correlate_preamble() ################################################## # Connections ################################################## self.connect((self.any_0, 0), (self.any_sink_0_1, 0)) self.connect((self.gr_complex_to_mag_squared_0, 0), (self.gr_nlog10_ff_0, 0)) self.connect((self.gr_nlog10_ff_0, 0), (self.fir_filter_xxx_0, 0)) self.connect((self.fir_filter_xxx_0, 0), (self.any_sink_0_1, 1)) self.connect((self, 0), (self.gr_complex_to_mag_squared_0, 0)) self.connect((self, 0), (self.wmbus_demod_0, 0)) self.connect((self.wmbus_demod_0, 0), (self.any_0, 0))
def __init__(self, sample_rate, fft_size, ref_scale, frame_rate, avg_alpha, average, win=None):
"""
Create an log10(abs(fft)) stream chain.
Provide access to the setting the filter and sample rate.
Args:
sample_rate: Incoming stream sample rate
fft_size: Number of FFT bins
ref_scale: Sets 0 dB value input amplitude
frame_rate: Output frame rate
avg_alpha: FFT averaging (over time) constant [0.0-1.0]
average: Whether to average [True, False]
win: the window taps generation function
"""
gr.hier_block2.__init__(self, self._name,
gr.io_signature(1, 1, self._item_size), # Input signature
gr.io_signature(1, 1, gr.sizeof_float*fft_size)) # Output signature
self._sd = stream_to_vector_decimator(item_size=self._item_size,
sample_rate=sample_rate,
vec_rate=frame_rate,
vec_len=fft_size)
if win is None: win = window.blackmanharris
fft_window = win(fft_size)
fft = self._fft_block[0](fft_size, True, fft_window)
window_power = sum(map(lambda x: x*x, fft_window))
c2magsq = gr.complex_to_mag_squared(fft_size)
self._avg = filter.single_pole_iir_filter_ff(1.0, fft_size)
self._log = gr.nlog10_ff(10, fft_size,
-20*math.log10(fft_size) # Adjust for number of bins
-10*math.log10(window_power/fft_size) # Adjust for windowing loss
-20*math.log10(ref_scale/2)) # Adjust for reference scale
self.connect(self, self._sd, fft, c2magsq, self._avg, self._log, self)
self._average = average
self._avg_alpha = avg_alpha
self.set_avg_alpha(avg_alpha)
self.set_average(average)
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, fft_length):
gr.hier_block2.__init__(self, "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)
nominator = schmidl_nominator(fft_length)
# 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(self.input, 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)
# calculate epsilon from P(d), epsilon is normalized fractional frequency offset
#angle = gr.complex_to_arg()
#self.epsilon = gr.multiply_const_ff(1.0/math.pi)
#self.connect(nominator, angle, self.epsilon)
try:
gr.hier_block.update_var_names(self, "schmidl", vars())
gr.hier_block.update_var_names(self, "schmidl", vars(self))
except:
pass
def __init__(self, length, debug=False):
"""
Hierarchical block to detect Null symbols
@param length length of the Null symbol (in samples)
@param debug whether to write signals out to files
"""
gr.hier_block2.__init__(
self,
"detect_null",
gr.io_signature(1, 1, gr.sizeof_gr_complex), # input signature
gr.io_signature(1, 1, gr.sizeof_char)) # output signature
# get the magnitude squared
self.ns_c2magsquared = gr.complex_to_mag_squared()
self.ns_moving_sum = dabp.moving_sum_ff(length)
self.ns_invert = gr.multiply_const_ff(-1)
# peak detector on the inverted, summed up signal -> we get the nulls (i.e. the position of the start of a frame)
self.ns_peak_detect = gr.peak_detector_fb(
0.6, 0.7, 10, 0.0001
) # mostly found by try and error -> remember that the values are negative!
# connect it all
self.connect(self, self.ns_c2magsquared, self.ns_moving_sum,
self.ns_invert, self.ns_peak_detect, self)
if debug:
self.connect(
self.ns_invert,
gr.file_sink(gr.sizeof_float,
"debug/ofdm_sync_dabp_ns_filter_inv_f.dat"))
self.connect(
self.ns_peak_detect,
gr.file_sink(gr.sizeof_char,
"debug/ofdm_sync_dabp_peak_detect_b.dat"))
def __init__(self, subc, vlen, ss):
gr.hier_block2.__init__(
self,
"new_snr_estimator",
gr.io_signature(2, 2, gr.sizeof_gr_complex * vlen),
#gr.io_signature2(2,2,gr.sizeof_float*vlen,gr.sizeof_float*vlen/ss*(ss-1)))
gr.io_signature2(2, 2, gr.sizeof_float * vlen, gr.sizeof_float))
print "Created Milan's SINR estimator"
trigger = [0] * vlen
trigger[0] = 1
v = range(vlen / ss)
ones_ind = map(lambda z: z * ss, v)
skip2_pr0 = skip(gr.sizeof_gr_complex, vlen)
skip2_pr1 = skip(gr.sizeof_gr_complex, vlen)
for x in ones_ind:
skip2_pr0.skip(x)
skip2_pr1.skip(x)
#print "skipped ones",ones_ind
v2s_pr0 = gr.vector_to_stream(gr.sizeof_gr_complex, vlen)
v2s_pr1 = gr.vector_to_stream(gr.sizeof_gr_complex, vlen)
s2v2_pr0 = gr.stream_to_vector(gr.sizeof_gr_complex,
vlen / ss * (ss - 1))
trigger_src_2_pr0 = gr.vector_source_b(trigger, True)
s2v2_pr1 = gr.stream_to_vector(gr.sizeof_gr_complex,
vlen / ss * (ss - 1))
trigger_src_2_pr1 = gr.vector_source_b(trigger, True)
mag_sq_zeros_pr0 = gr.complex_to_mag_squared(vlen / ss * (ss - 1))
mag_sq_zeros_pr1 = gr.complex_to_mag_squared(vlen / ss * (ss - 1))
filt_zeros_pr0 = gr.single_pole_iir_filter_ff(0.01,
vlen / ss * (ss - 1))
filt_zeros_pr1 = gr.single_pole_iir_filter_ff(0.01,
vlen / ss * (ss - 1))
v1 = vlen / ss * (ss - 1)
vevc1 = [-1] * v1
neg_nomin_z = gr.multiply_const_vff(vevc1)
div_z = gr.divide_ff(vlen / ss * (ss - 1))
on_zeros = gr.add_const_vff(vevc1)
sum_zeros = add_vff(vlen / ss * (ss - 1))
# For average
sum_all = vector_sum_vff(vlen)
mult = gr.multiply_const_ff(1. / vlen)
scsnr_db_av = gr.nlog10_ff(10, 1, 0)
filt_end_av = gr.single_pole_iir_filter_ff(0.1)
self.connect((self, 0), v2s_pr0, skip2_pr0, s2v2_pr0, mag_sq_zeros_pr0,
filt_zeros_pr0)
self.connect(trigger_src_2_pr0, (skip2_pr0, 1))
self.connect((self, 1), v2s_pr1, skip2_pr1, s2v2_pr1, mag_sq_zeros_pr1,
filt_zeros_pr1)
self.connect(trigger_src_2_pr1, (skip2_pr1, 1))
# On zeros
self.connect(filt_zeros_pr1, (sum_zeros, 0))
self.connect(filt_zeros_pr0, neg_nomin_z, (sum_zeros, 1))
self.connect(sum_zeros, div_z)
self.connect(filt_zeros_pr0, (div_z, 1))
scsnr_db = gr.nlog10_ff(10, vlen, 0)
filt_end = gr.single_pole_iir_filter_ff(0.1, vlen)
dd = []
for i in range(vlen / ss):
dd.extend([i * ss])
#print dd
interpolator = sinr_interpolator(vlen, ss, dd)
self.connect(div_z, interpolator, filt_end, scsnr_db, self)
self.connect(interpolator, sum_all, mult, scsnr_db_av, filt_end_av,
(self, 1))
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, tuner_callback, options):
gr.hier_block2.__init__(
self,
"sense_path",
gr.io_signature(1, 1, gr.sizeof_gr_complex), # Input signature
gr.io_signature(0, 0, 0)) # Output signature
self.usrp_rate = options.channel_rate
self.usrp_tune = tuner_callback
self.threshold = options.threshold
#self.freq_step = options.chan_bandwidth
#self.min_freq = options.start_freq
#self.max_freq = options.end_freq
self.hold_freq = False
self.channels = [
600000000, 620000000, 625000000, 640000000, 645000000, 650000000
]
self.current_chan = 0
self.num_channels = len(
self.channels) #(self.max_freq - self.min_freq)/self.freq_step
#if self.min_freq > self.max_freq:
# self.min_freq, self.max_freq = self.max_freq, self.min_freq # swap them
self.fft_size = options.sense_fft_size
if not options.real_time:
realtime = False
else:
# Attempt to enable realtime scheduling
r = gr.enable_realtime_scheduling()
if r == gr.RT_OK:
realtime = True
else:
realtime = False
print "Note: failed to enable realtime scheduling"
# build graph
s2v = gr.stream_to_vector(gr.sizeof_gr_complex, self.fft_size)
mywindow = window.blackmanharris(self.fft_size)
fft = gr.fft_vcc(self.fft_size, True, mywindow)
power = 0
for tap in mywindow:
power += tap * tap
c2mag = gr.complex_to_mag_squared(self.fft_size)
# FIXME the log10 primitive is dog slow
log = gr.nlog10_ff(
10, self.fft_size, -20 * math.log10(self.fft_size) -
10 * math.log10(power / self.fft_size))
# Set the freq_step to 75% of the actual data throughput.
# This allows us to discard the bins on both ends of the spectrum.
#changed on 2011 May 31, MR -- maybe change back at some point
#self.min_center_freq = self.min_freq + self.freq_step/2
#nsteps = math.ceil((self.max_freq - self.min_freq) / self.freq_step)
#self.max_center_freq = self.min_center_freq + (nsteps * self.freq_step)
self.next_freq = self.channels[
self.current_chan] #self.min_center_freq
tune_delay = max(0,
int(
round(options.tune_delay * self.usrp_rate /
self.fft_size))) # in fft_frames
dwell_delay = max(1,
int(
round(options.dwell_delay * self.usrp_rate /
self.fft_size))) # in fft_frames
self.msgq = gr.msg_queue(16)
self._tune_callback = tune(
self) # hang on to this to keep it from being GC'd
self.stats = gr.bin_statistics_f(self.fft_size, self.msgq,
self._tune_callback, tune_delay,
dwell_delay)
# FIXME leave out the log10 until we speed it up
#self.connect(self, s2v, fft, c2mag, log, stats)
self.connect(self, s2v, fft, c2mag, self.stats)
def __init__(self, ahw="default", freq=150.0e6, ppm=0.0, vol=1.0, ftune=0.0, xftune=0.0, srate=1.0e6, upclo=0.0, devinfo="rtl=0", agc=0, arate=48.0e3, upce=0, mthresh=-10.0, offs=50.e3, flist="", dfifo="multimode_fifo", mbw=2.0e3, deemph=75.0e-6, dmode="NFM1"):
grc_wxgui.top_block_gui.__init__(self, title="Multimode Radio Receiver")
_icon_path = "/usr/share/icons/hicolor/32x32/apps/gnuradio-grc.png"
self.SetIcon(wx.Icon(_icon_path, wx.BITMAP_TYPE_ANY))
##################################################
# Parameters
##################################################
self.ahw = ahw
self.freq = freq
self.ppm = ppm
self.vol = vol
self.ftune = ftune
self.xftune = xftune
self.srate = srate
self.upclo = upclo
self.devinfo = devinfo
self.agc = agc
self.arate = arate
self.upce = upce
self.mthresh = mthresh
self.offs = offs
self.flist = flist
self.dfifo = dfifo
self.mbw = mbw
self.deemph = deemph
self.dmode = dmode
##################################################
# Variables
##################################################
self.sc_list_str = sc_list_str = flist
self.zoom = zoom = 1
self.thresh = thresh = mthresh
self.scan_rate = scan_rate = 15
self.scan_power = scan_power = 0
self.sc_low = sc_low = 150e6
self.sc_listm = sc_listm = False
self.sc_list = sc_list = eval("["+sc_list_str+"]")
self.sc_incr = sc_incr = 12.5e3
self.sc_high = sc_high = 300e6
self.sc_ena = sc_ena = False
self.samp_rate = samp_rate = int(mh.get_good_rate(devinfo,srate))
self.rf_power = rf_power = 0
self.ifreq = ifreq = freq
self.zoomed_lp = zoomed_lp = (samp_rate/2.1)/zoom
self.wbfm = wbfm = 200e3
self.rf_d_power = rf_d_power = 0
self.mode = mode = dmode
self.logpower = logpower = math.log10(rf_power+1.0e-14)*10.0
self.cur_freq = cur_freq = mh.scan_freq_out(sc_ena,sc_low,sc_high,freq,ifreq,scan_power+1.0e-14,thresh,sc_incr,scan_rate,sc_listm,sc_list)
self.bw = bw = mbw
self.audio_int_rate = audio_int_rate = 40e3
self.zoom_taps = zoom_taps = firdes.low_pass(1.0,samp_rate,zoomed_lp,zoomed_lp/3,firdes.WIN_HAMMING,6.76)
self.xfine = xfine = xftune
self.volume = volume = vol
self.variable_static_text_1 = variable_static_text_1 = cur_freq
self.variable_static_text_0_0 = variable_static_text_0_0 = samp_rate
self.variable_static_text_0 = variable_static_text_0 = float(int(math.log10(rf_d_power+1.0e-14)*100.0)/10.0)
self.upc_offset = upc_offset = upclo
self.upc = upc = upce
self.ssbo = ssbo = -bw/2 if mode == "LSB" else 0.0
self.sc_list_len = sc_list_len = len(sc_list)
self.rfgain = rfgain = 25
self.record_file = record_file = "recording.wav"
self.record = record = False
self.offset = offset = offs
self.muted = muted = 0.0 if logpower >= thresh else 1
self.main_taps = main_taps = firdes.low_pass(1.0,wbfm,mh.get_mode_deviation(mode,bw)*1.05,mh.get_mode_deviation(mode,bw)/2.0,firdes.WIN_HAMMING,6.76)
self.k = k = wbfm/(2*math.pi*mh.get_mode_deviation(mode,bw))
self.iagc = iagc = agc
self.freq_update = freq_update = 0
self.fine = fine = ftune
self.digi_rate = digi_rate = 50e3
self.aratio = aratio = int(wbfm/audio_int_rate)
##################################################
# Blocks
##################################################
self.rf_probe = gr.probe_avg_mag_sqrd_c(0, 0.015)
self.Main = self.Main = wx.Notebook(self.GetWin(), style=wx.NB_TOP)
self.Main.AddPage(grc_wxgui.Panel(self.Main), "Main Controls")
self.Main.AddPage(grc_wxgui.Panel(self.Main), "Scan/Upconv Controls")
self.Add(self.Main)
self._zoom_chooser = forms.drop_down(
parent=self.Main.GetPage(0).GetWin(),
value=self.zoom,
callback=self.set_zoom,
label="Spectral Zoom Ratio",
choices=[1, 2, 5, 10, 20, 50, 100],
labels=[],
)
self.Main.GetPage(0).GridAdd(self._zoom_chooser, 1, 4, 1, 1)
_xfine_sizer = wx.BoxSizer(wx.VERTICAL)
self._xfine_text_box = forms.text_box(
parent=self.Main.GetPage(0).GetWin(),
sizer=_xfine_sizer,
value=self.xfine,
callback=self.set_xfine,
label="Extra Fine Tuning",
converter=forms.float_converter(),
proportion=0,
)
self._xfine_slider = forms.slider(
parent=self.Main.GetPage(0).GetWin(),
sizer=_xfine_sizer,
value=self.xfine,
callback=self.set_xfine,
minimum=-1.0e3,
maximum=1.0e3,
num_steps=200,
style=wx.SL_HORIZONTAL,
cast=float,
proportion=1,
)
self.Main.GetPage(0).GridAdd(_xfine_sizer, 0, 3, 1, 1)
_volume_sizer = wx.BoxSizer(wx.VERTICAL)
self._volume_text_box = forms.text_box(
parent=self.Main.GetPage(0).GetWin(),
sizer=_volume_sizer,
value=self.volume,
callback=self.set_volume,
label="Volume",
converter=forms.float_converter(),
proportion=0,
)
self._volume_slider = forms.slider(
parent=self.Main.GetPage(0).GetWin(),
sizer=_volume_sizer,
value=self.volume,
callback=self.set_volume,
minimum=1.0,
maximum=10.0,
num_steps=100,
style=wx.SL_HORIZONTAL,
cast=float,
proportion=1,
)
self.Main.GetPage(0).GridAdd(_volume_sizer, 0, 0, 1, 1)
self._upc_offset_text_box = forms.text_box(
parent=self.Main.GetPage(1).GetWin(),
value=self.upc_offset,
callback=self.set_upc_offset,
label="Upconv. LO Freq",
converter=forms.float_converter(),
)
self.Main.GetPage(1).GridAdd(self._upc_offset_text_box, 3, 2, 1, 2)
self._upc_check_box = forms.check_box(
parent=self.Main.GetPage(1).GetWin(),
value=self.upc,
callback=self.set_upc,
label="Ext. Upconv.",
true=1,
false=0,
)
self.Main.GetPage(1).GridAdd(self._upc_check_box, 3, 0, 1, 1)
_rfgain_sizer = wx.BoxSizer(wx.VERTICAL)
self._rfgain_text_box = forms.text_box(
parent=self.Main.GetPage(0).GetWin(),
sizer=_rfgain_sizer,
value=self.rfgain,
callback=self.set_rfgain,
label="RF Gain",
converter=forms.float_converter(),
proportion=0,
)
self._rfgain_slider = forms.slider(
parent=self.Main.GetPage(0).GetWin(),
sizer=_rfgain_sizer,
value=self.rfgain,
callback=self.set_rfgain,
minimum=0,
maximum=50,
num_steps=200,
style=wx.SL_HORIZONTAL,
cast=float,
proportion=1,
)
self.Main.GetPage(0).GridAdd(_rfgain_sizer, 2, 1, 1, 1)
self._record_file_text_box = forms.text_box(
parent=self.Main.GetPage(0).GetWin(),
value=self.record_file,
callback=self.set_record_file,
label="Recording Filename",
converter=forms.str_converter(),
)
self.Main.GetPage(0).GridAdd(self._record_file_text_box, 2, 3, 1, 3)
self._record_check_box = forms.check_box(
parent=self.Main.GetPage(0).GetWin(),
value=self.record,
callback=self.set_record,
label="Record",
true=True,
false=False,
)
self.Main.GetPage(0).GridAdd(self._record_check_box, 2, 2, 1, 1)
_offset_sizer = wx.BoxSizer(wx.VERTICAL)
self._offset_text_box = forms.text_box(
parent=self.Main.GetPage(0).GetWin(),
sizer=_offset_sizer,
value=self.offset,
callback=self.set_offset,
label="LO Offset",
converter=forms.float_converter(),
proportion=0,
)
self._offset_slider = forms.slider(
parent=self.Main.GetPage(0).GetWin(),
sizer=_offset_sizer,
value=self.offset,
callback=self.set_offset,
minimum=25e3,
maximum=500e3,
num_steps=200,
style=wx.SL_HORIZONTAL,
cast=float,
proportion=1,
)
self.Main.GetPage(0).GridAdd(_offset_sizer, 1, 3, 1, 1)
self._mode_chooser = forms.drop_down(
parent=self.Main.GetPage(0).GetWin(),
value=self.mode,
callback=self.set_mode,
label="Mode",
choices=mh.get_modes_values(),
labels=mh.get_modes_names(),
)
self.Main.GetPage(0).GridAdd(self._mode_chooser, 0, 4, 1, 1)
self._iagc_check_box = forms.check_box(
parent=self.Main.GetPage(0).GetWin(),
value=self.iagc,
callback=self.set_iagc,
label="AGC",
true=1,
false=0,
)
self.Main.GetPage(0).GridAdd(self._iagc_check_box, 2, 0, 1, 1)
def _freq_update_probe():
while True:
val = self.rf_probe.level()
try: self.set_freq_update(val)
except AttributeError, e: pass
time.sleep(1.0/(1.0/(2.5)))
_freq_update_thread = threading.Thread(target=_freq_update_probe)
_freq_update_thread.daemon = True
_freq_update_thread.start()
_fine_sizer = wx.BoxSizer(wx.VERTICAL)
self._fine_text_box = forms.text_box(
parent=self.Main.GetPage(0).GetWin(),
sizer=_fine_sizer,
value=self.fine,
callback=self.set_fine,
label="Fine Tuning",
converter=forms.float_converter(),
proportion=0,
)
self._fine_slider = forms.slider(
parent=self.Main.GetPage(0).GetWin(),
sizer=_fine_sizer,
value=self.fine,
callback=self.set_fine,
minimum=-35e3,
maximum=35e3,
num_steps=100,
style=wx.SL_HORIZONTAL,
cast=float,
proportion=1,
)
self.Main.GetPage(0).GridAdd(_fine_sizer, 0, 2, 1, 1)
self.display_probe = gr.probe_avg_mag_sqrd_c(0, 0.002)
_bw_sizer = wx.BoxSizer(wx.VERTICAL)
self._bw_text_box = forms.text_box(
parent=self.Main.GetPage(0).GetWin(),
sizer=_bw_sizer,
value=self.bw,
callback=self.set_bw,
label="AM/SSB Bandwidth",
converter=forms.float_converter(),
proportion=0,
)
self._bw_slider = forms.slider(
parent=self.Main.GetPage(0).GetWin(),
sizer=_bw_sizer,
value=self.bw,
callback=self.set_bw,
minimum=1.0e3,
maximum=audio_int_rate/2,
num_steps=100,
style=wx.SL_HORIZONTAL,
cast=float,
proportion=1,
)
self.Main.GetPage(0).GridAdd(_bw_sizer, 1, 2, 1, 1)
self.wxgui_waterfallsink2_0 = waterfallsink2.waterfall_sink_c(
self.Main.GetPage(0).GetWin(),
baseband_freq=mh.get_last_returned(freq_update),
dynamic_range=40,
ref_level=0,
ref_scale=2.0,
sample_rate=samp_rate/zoom,
fft_size=1024,
fft_rate=4,
average=True,
avg_alpha=None,
title="Spectrogram",
win=window.hamming,
)
self.Main.GetPage(0).Add(self.wxgui_waterfallsink2_0.win)
def wxgui_waterfallsink2_0_callback(x, y):
self.set_freq(x)
self.wxgui_waterfallsink2_0.set_callback(wxgui_waterfallsink2_0_callback)
self.wxgui_fftsink2_0 = fftsink2.fft_sink_c(
self.Main.GetPage(0).GetWin(),
baseband_freq=mh.get_last_returned(freq_update),
y_per_div=10,
y_divs=10,
ref_level=0,
ref_scale=2.0,
sample_rate=samp_rate/zoom,
fft_size=1024,
fft_rate=4,
average=True,
avg_alpha=0.1,
title="Panorama",
peak_hold=False,
win=window.hamming,
)
self.Main.GetPage(0).Add(self.wxgui_fftsink2_0.win)
def wxgui_fftsink2_0_callback(x, y):
self.set_freq(x)
self.wxgui_fftsink2_0.set_callback(wxgui_fftsink2_0_callback)
self._variable_static_text_1_static_text = forms.static_text(
parent=self.Main.GetPage(1).GetWin(),
value=self.variable_static_text_1,
callback=self.set_variable_static_text_1,
label="Current Scan Freq",
converter=forms.float_converter(),
)
self.Main.GetPage(1).GridAdd(self._variable_static_text_1_static_text, 0, 5, 1, 2)
self._variable_static_text_0_0_static_text = forms.static_text(
parent=self.Main.GetPage(0).GetWin(),
value=self.variable_static_text_0_0,
callback=self.set_variable_static_text_0_0,
label="Actual srate",
converter=forms.float_converter(),
)
self.Main.GetPage(0).GridAdd(self._variable_static_text_0_0_static_text, 1, 5, 1, 1)
self._variable_static_text_0_static_text = forms.static_text(
parent=self.Main.GetPage(0).GetWin(),
value=self.variable_static_text_0,
callback=self.set_variable_static_text_0,
label="RF Level",
converter=forms.float_converter(),
)
self.Main.GetPage(0).GridAdd(self._variable_static_text_0_static_text, 1, 0, 1, 1)
_thresh_sizer = wx.BoxSizer(wx.VERTICAL)
self._thresh_text_box = forms.text_box(
parent=self.Main.GetPage(0).GetWin(),
sizer=_thresh_sizer,
value=self.thresh,
callback=self.set_thresh,
label="Mute Threshold",
converter=forms.float_converter(),
proportion=0,
)
self._thresh_slider = forms.slider(
parent=self.Main.GetPage(0).GetWin(),
sizer=_thresh_sizer,
value=self.thresh,
callback=self.set_thresh,
minimum=-50,
maximum=10,
num_steps=100,
style=wx.SL_HORIZONTAL,
cast=float,
proportion=1,
)
self.Main.GetPage(0).GridAdd(_thresh_sizer, 1, 1, 1, 1)
def _scan_power_probe():
while True:
val = self.rf_probe.level()
try: self.set_scan_power(val)
except AttributeError, e: pass
time.sleep(1.0/(scan_rate))
_scan_power_thread = threading.Thread(target=_scan_power_probe)
_scan_power_thread.daemon = True
_scan_power_thread.start()
self._sc_low_text_box = forms.text_box(
parent=self.Main.GetPage(1).GetWin(),
value=self.sc_low,
callback=self.set_sc_low,
label="Scan Low",
converter=forms.float_converter(),
)
self.Main.GetPage(1).GridAdd(self._sc_low_text_box, 0, 1, 1, 1)
self._sc_listm_check_box = forms.check_box(
parent=self.Main.GetPage(1).GetWin(),
value=self.sc_listm,
callback=self.set_sc_listm,
label="Scan List Mode",
true=True,
false=False,
)
self.Main.GetPage(1).GridAdd(self._sc_listm_check_box, 2, 0, 1, 1)
self._sc_list_str_text_box = forms.text_box(
parent=self.Main.GetPage(1).GetWin(),
value=self.sc_list_str,
callback=self.set_sc_list_str,
label="Scan List",
converter=forms.str_converter(),
)
self.Main.GetPage(1).GridAdd(self._sc_list_str_text_box, 2, 1, 1, 5)
self._sc_incr_chooser = forms.drop_down(
parent=self.Main.GetPage(1).GetWin(),
value=self.sc_incr,
callback=self.set_sc_incr,
label="Scan Increment (Hz)",
choices=[5.0e3,6.25e3,10.0e3,12.5e3,15e3,25e3],
labels=[],
)
self.Main.GetPage(1).GridAdd(self._sc_incr_chooser, 0, 0, 1, 1)
self._sc_high_text_box = forms.text_box(
parent=self.Main.GetPage(1).GetWin(),
value=self.sc_high,
callback=self.set_sc_high,
label="Scan High",
converter=forms.float_converter(),
)
self.Main.GetPage(1).GridAdd(self._sc_high_text_box, 0, 2, 1, 1)
self._sc_ena_check_box = forms.check_box(
parent=self.Main.GetPage(1).GetWin(),
value=self.sc_ena,
callback=self.set_sc_ena,
label="Scan Enable",
true=True,
false=False,
)
self.Main.GetPage(1).GridAdd(self._sc_ena_check_box, 0, 3, 1, 1)
def _rf_power_probe():
while True:
val = self.rf_probe.level()
try: self.set_rf_power(val)
except AttributeError, e: pass
time.sleep(1.0/(10))
_rf_power_thread = threading.Thread(target=_rf_power_probe)
_rf_power_thread.daemon = True
_rf_power_thread.start()
def _rf_d_power_probe():
while True:
val = self.display_probe.level()
try: self.set_rf_d_power(val)
except AttributeError, e: pass
time.sleep(1.0/(5))
_rf_d_power_thread = threading.Thread(target=_rf_d_power_probe)
_rf_d_power_thread.daemon = True
_rf_d_power_thread.start()
self.osmosdr_source_c_0 = osmosdr.source_c( args="nchan=" + str(1) + " " + devinfo )
self.osmosdr_source_c_0.set_sample_rate(samp_rate)
self.osmosdr_source_c_0.set_center_freq(cur_freq+offset+(upc_offset*float(upc)), 0)
self.osmosdr_source_c_0.set_freq_corr(ppm, 0)
self.osmosdr_source_c_0.set_gain_mode(iagc, 0)
self.osmosdr_source_c_0.set_gain(25 if iagc == 1 else rfgain, 0)
self.osmosdr_source_c_0.set_if_gain(20, 0)
self._ifreq_text_box = forms.text_box(
parent=self.Main.GetPage(0).GetWin(),
value=self.ifreq,
callback=self.set_ifreq,
label="Frequency",
converter=forms.float_converter(),
)
self.Main.GetPage(0).GridAdd(self._ifreq_text_box, 0, 1, 1, 1)
self.gr_wavfile_sink_0 = gr.wavfile_sink("/dev/null" if record == False else record_file, 1, int(audio_int_rate), 8)
self.gr_quadrature_demod_cf_0 = gr.quadrature_demod_cf(k)
self.gr_multiply_const_vxx_2 = gr.multiply_const_vff((1.0 if mh.get_mode_type(mode) == "FM" else 0.0, ))
self.gr_multiply_const_vxx_1 = gr.multiply_const_vff((0.0 if muted else volume/4.5, ))
self.gr_multiply_const_vxx_0_0_0 = gr.multiply_const_vff((0.85 if mh.get_mode_type(mode) == "AM" else 0.0, ))
self.gr_multiply_const_vxx_0_0 = gr.multiply_const_vff((0.85 if mh.get_mode_type(mode) == "SSB" else 0.0, ))
self.gr_multiply_const_vxx_0 = gr.multiply_const_vcc(((1.0/math.sqrt(mh.get_mode_deviation(mode,bw))*250), ))
self.gr_keep_one_in_n_1 = gr.keep_one_in_n(gr.sizeof_gr_complex*1, aratio)
self.gr_keep_one_in_n_0_0 = gr.keep_one_in_n(gr.sizeof_gr_complex*1, zoom)
self.gr_keep_one_in_n_0 = gr.keep_one_in_n(gr.sizeof_gr_complex*1, int(wbfm/digi_rate))
self.gr_freq_xlating_fir_filter_xxx_0_1 = gr.freq_xlating_fir_filter_ccc(1, (1.0, ), (offset+fine+xfine)/(samp_rate/1.0e6), samp_rate)
self.gr_fractional_interpolator_xx_0 = gr.fractional_interpolator_ff(0, audio_int_rate/arate)
self.gr_file_sink_0 = gr.file_sink(gr.sizeof_gr_complex*1, "/dev/null" if mh.get_mode_type(mode) != "DIG" else dfifo)
self.gr_file_sink_0.set_unbuffered(True)
self.gr_fft_filter_xxx_3 = gr.fft_filter_ccc(1, (zoom_taps), 1)
self.gr_fft_filter_xxx_2_0 = gr.fft_filter_fff(5, (firdes.low_pass(1.0,wbfm,14.5e3,8.5e3,firdes.WIN_HAMMING,6.76)), 1)
self.gr_fft_filter_xxx_2 = gr.fft_filter_ccc(1, (main_taps), 1)
self.gr_fft_filter_xxx_0 = gr.fft_filter_ccc(int(samp_rate/wbfm), (firdes.low_pass(1.0,samp_rate,98.5e3,66e3,firdes.WIN_HAMMING,6.76)), 1)
self.gr_feedforward_agc_cc_0 = gr.feedforward_agc_cc(1024, 0.75)
self.gr_complex_to_real_0 = gr.complex_to_real(1)
self.gr_complex_to_mag_squared_0 = gr.complex_to_mag_squared(1)
self.gr_add_xx_0 = gr.add_vff(1)
self.blks2_fm_deemph_0 = blks2.fm_deemph(fs=audio_int_rate, tau=deemph)
self.audio_sink_0 = audio.sink(int(arate), ahw, True)
##################################################
# Connections
##################################################
self.connect((self.gr_multiply_const_vxx_0_0, 0), (self.gr_add_xx_0, 1))
self.connect((self.gr_fractional_interpolator_xx_0, 0), (self.gr_multiply_const_vxx_1, 0))
self.connect((self.gr_multiply_const_vxx_1, 0), (self.audio_sink_0, 0))
self.connect((self.gr_multiply_const_vxx_1, 0), (self.audio_sink_0, 1))
self.connect((self.gr_feedforward_agc_cc_0, 0), (self.gr_complex_to_mag_squared_0, 0))
self.connect((self.osmosdr_source_c_0, 0), (self.gr_freq_xlating_fir_filter_xxx_0_1, 0))
self.connect((self.gr_multiply_const_vxx_0_0_0, 0), (self.gr_add_xx_0, 2))
self.connect((self.gr_feedforward_agc_cc_0, 0), (self.gr_complex_to_real_0, 0))
self.connect((self.gr_complex_to_real_0, 0), (self.gr_multiply_const_vxx_0_0, 0))
self.connect((self.gr_multiply_const_vxx_2, 0), (self.gr_add_xx_0, 0))
self.connect((self.gr_complex_to_mag_squared_0, 0), (self.gr_multiply_const_vxx_0_0_0, 0))
self.connect((self.gr_multiply_const_vxx_0, 0), (self.display_probe, 0))
self.connect((self.gr_multiply_const_vxx_0, 0), (self.rf_probe, 0))
self.connect((self.gr_add_xx_0, 0), (self.gr_fractional_interpolator_xx_0, 0))
self.connect((self.gr_add_xx_0, 0), (self.gr_wavfile_sink_0, 0))
self.connect((self.gr_freq_xlating_fir_filter_xxx_0_1, 0), (self.gr_fft_filter_xxx_0, 0))
self.connect((self.gr_keep_one_in_n_0, 0), (self.gr_file_sink_0, 0))
self.connect((self.gr_freq_xlating_fir_filter_xxx_0_1, 0), (self.gr_fft_filter_xxx_3, 0))
self.connect((self.gr_fft_filter_xxx_3, 0), (self.gr_keep_one_in_n_0_0, 0))
self.connect((self.gr_keep_one_in_n_0_0, 0), (self.wxgui_fftsink2_0, 0))
self.connect((self.gr_keep_one_in_n_0_0, 0), (self.wxgui_waterfallsink2_0, 0))
self.connect((self.blks2_fm_deemph_0, 0), (self.gr_multiply_const_vxx_2, 0))
self.connect((self.gr_quadrature_demod_cf_0, 0), (self.gr_fft_filter_xxx_2_0, 0))
self.connect((self.gr_fft_filter_xxx_2, 0), (self.gr_keep_one_in_n_0, 0))
self.connect((self.gr_fft_filter_xxx_2, 0), (self.gr_multiply_const_vxx_0, 0))
self.connect((self.gr_fft_filter_xxx_0, 0), (self.gr_fft_filter_xxx_2, 0))
self.connect((self.gr_keep_one_in_n_1, 0), (self.gr_feedforward_agc_cc_0, 0))
self.connect((self.gr_fft_filter_xxx_2, 0), (self.gr_keep_one_in_n_1, 0))
self.connect((self.gr_fft_filter_xxx_2, 0), (self.gr_quadrature_demod_cf_0, 0))
self.connect((self.gr_fft_filter_xxx_2_0, 0), (self.blks2_fm_deemph_0, 0))
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):
gr.top_block.__init__(self)
# Build an options parser to bring in information from the user on usage
usage = "usage: %prog [options] host min_freq max_freq"
parser = OptionParser(option_class=eng_option, usage=usage)
parser.add_option("-g", "--gain", type="eng_float", default=32,
help="set gain in dB (default is midpoint)")
parser.add_option("", "--tune-delay", type="eng_float", default=5e-5, metavar="SECS",
help="time to delay (in seconds) after changing frequency [default=%default]")
parser.add_option("", "--dwell-delay", type="eng_float", default=50e-5, metavar="SECS",
help="time to dwell (in seconds) at a given frequncy [default=%default]")
parser.add_option("-F", "--fft-size", type="int", default=256,
help="specify number of FFT bins [default=%default]")
parser.add_option("-d", "--decim", type="intx", default=16,
help="set decimation to DECIM [default=%default]")
parser.add_option("", "--real-time", action="store_true", default=False,
help="Attempt to enable real-time scheduling")
(options, args) = parser.parse_args()
if len(args) != 3:
parser.print_help()
sys.exit(1)
# get user-provided info on address of MSDD and frequency to sweep
self.address = args[0]
self.min_freq = eng_notation.str_to_num(args[1])
self.max_freq = eng_notation.str_to_num(args[2])
self.decim = options.decim
self.gain = options.gain
if self.min_freq > self.max_freq:
self.min_freq, self.max_freq = self.max_freq, self.min_freq # swap them
self.fft_size = options.fft_size
if not options.real_time:
realtime = False
else:
# Attempt to enable realtime scheduling
r = gr.enable_realtime_scheduling()
if r == gr.RT_OK:
realtime = True
else:
realtime = False
print "Note: failed to enable realtime scheduling"
# Sampling rate is hardcoded and cannot be read off device
adc_rate = 102.4e6
self.int_rate = adc_rate / self.decim
print "Sampling rate: ", self.int_rate
# build graph
self.port = 10001 # required port for UDP packets
# which board, op mode, adx, port
# self.src = msdd.source_c(0, 1, self.address, self.port) # build source object
self.conv = gr.interleaved_short_to_complex();
self.src = msdd.source_simple(self.address,self.port);
self.src.set_decim_rate(self.decim) # set decimation rate
# self.src.set_desired_packet_size(0, 1460) # set packet size to collect
self.set_gain(self.gain) # set receiver's attenuation
self.set_freq(self.min_freq) # set receiver's rx frequency
# restructure into vector format for FFT input
s2v = gr.stream_to_vector(gr.sizeof_gr_complex, self.fft_size)
# set up FFT processing block
mywindow = window.blackmanharris(self.fft_size)
fft = gr.fft_vcc(self.fft_size, True, mywindow, True)
power = 0
for tap in mywindow:
power += tap*tap
# calculate magnitude squared of output of FFT
c2mag = gr.complex_to_mag_squared(self.fft_size)
# FIXME the log10 primitive is dog slow
log = gr.nlog10_ff(10, self.fft_size,
-20*math.log10(self.fft_size)-10*math.log10(power/self.fft_size))
# Set the freq_step to % of the actual data throughput.
# This allows us to discard the bins on both ends of the spectrum.
self.percent = 0.4
# Calculate the frequency steps to use in the collection over the whole bandwidth
self.freq_step = self.percent * self.int_rate
self.min_center_freq = self.min_freq + self.freq_step/2
nsteps = math.ceil((self.max_freq - self.min_freq) / self.freq_step)
self.max_center_freq = self.min_center_freq + (nsteps * self.freq_step)
self.next_freq = self.min_center_freq
# use these values to set receiver settling time between samples and sampling time
# the default values provided seem to work well with the MSDD over 100 Mbps ethernet
tune_delay = max(0, int(round(options.tune_delay * self.int_rate / self.fft_size))) # in fft_frames
dwell_delay = max(1, int(round(options.dwell_delay * self.int_rate / self.fft_size))) # in fft_frames
# set up message callback routine to get data from bin_statistics_f block
self.msgq = gr.msg_queue(16)
self._tune_callback = tune(self) # hang on to this to keep it from being GC'd
# FIXME this block doesn't like to work with negatives because of the "d_max[i]=0" on line
# 151 of gr_bin_statistics_f.cc file. Set this to -10000 or something to get it to work.
stats = gr.bin_statistics_f(self.fft_size, self.msgq,
self._tune_callback, tune_delay, dwell_delay)
# FIXME there's a concern over the speed of the log calculation
# We can probably calculate the log inside the stats block
self.connect(self.src, self.conv, s2v, fft, c2mag, log, stats)
def __init__(self):
gr.top_block.__init__(self)
usage = "usage: %prog [options] min_freq max_freq"
parser = OptionParser(option_class=eng_option, usage=usage)
parser.add_option("-a", "--args", type="string", default="",
help="UHD device device address args [default=%default]")
parser.add_option("", "--spec", type="string", default=None,
help="Subdevice of UHD device where appropriate")
parser.add_option("-A", "--antenna", type="string", default=None,
help="select Rx Antenna where appropriate")
parser.add_option("-s", "--samp-rate", type="eng_float", default=1e6,
help="set sample rate [default=%default]")
parser.add_option("-g", "--gain", type="eng_float", default=None,
help="set gain in dB (default is midpoint)")
parser.add_option("", "--tune-delay", type="eng_float",
default=1e-3, metavar="SECS",
help="time to delay (in seconds) after changing frequency [default=%default]")
parser.add_option("", "--dwell-delay", type="eng_float",
default=10e-3, metavar="SECS",
help="time to dwell (in seconds) at a given frequency [default=%default]")
parser.add_option("", "--channel-bandwidth", type="eng_float",
default=12.5e3, metavar="Hz",
help="channel bandwidth of fft bins in Hz [default=%default]")
parser.add_option("-F", "--fft-size", type="int", default=256,
help="specify number of FFT bins [default=%default]")
parser.add_option("", "--real-time", action="store_true", default=False,
help="Attempt to enable real-time scheduling")
(options, args) = parser.parse_args()
if len(args) != 2:
parser.print_help()
sys.exit(1)
self.min_freq = eng_notation.str_to_num(args[0])
self.max_freq = eng_notation.str_to_num(args[1])
if self.min_freq > self.max_freq:
# swap them
self.min_freq, self.max_freq = self.max_freq, self.min_freq
self.fft_size = options.fft_size
self.channel_bandwidth = options.channel_bandwidth
if not options.real_time:
realtime = False
else:
# Attempt to enable realtime scheduling
r = gr.enable_realtime_scheduling()
if r == gr.RT_OK:
realtime = True
else:
realtime = False
print "Note: failed to enable realtime scheduling"
# build graph
self.u = uhd.usrp_source(device_addr=options.args,
stream_args=uhd.stream_args('fc32'))
# Set the subdevice spec
if(options.spec):
self.u.set_subdev_spec(options.spec, 0)
# Set the antenna
if(options.antenna):
self.u.set_antenna(options.antenna, 0)
self.usrp_rate = usrp_rate = options.samp_rate
self.u.set_samp_rate(usrp_rate)
dev_rate = self.u.get_samp_rate()
s2v = gr.stream_to_vector(gr.sizeof_gr_complex, self.fft_size)
mywindow = window.blackmanharris(self.fft_size)
fft = gr.fft_vcc(self.fft_size, True, mywindow, True)
power = 0
for tap in mywindow:
power += tap*tap
c2mag = gr.complex_to_mag_squared(self.fft_size)
# FIXME the log10 primitive is dog slow
log = gr.nlog10_ff(10, self.fft_size,
-20*math.log10(self.fft_size)-10*math.log10(power/self.fft_size))
# Set the freq_step to 75% of the actual data throughput.
# This allows us to discard the bins on both ends of the spectrum.
self.freq_step = 0.75 * usrp_rate
self.min_center_freq = self.min_freq + self.freq_step/2
nsteps = math.ceil((self.max_freq - self.min_freq) / self.freq_step)
self.max_center_freq = self.min_center_freq + (nsteps * self.freq_step)
self.next_freq = self.min_center_freq
tune_delay = max(0, int(round(options.tune_delay * usrp_rate / self.fft_size))) # in fft_frames
dwell_delay = max(1, int(round(options.dwell_delay * usrp_rate / self.fft_size))) # in fft_frames
self.msgq = gr.msg_queue(16)
self._tune_callback = tune(self) # hang on to this to keep it from being GC'd
stats = gr.bin_statistics_f(self.fft_size, self.msgq,
self._tune_callback, tune_delay,
dwell_delay)
# FIXME leave out the log10 until we speed it up
#self.connect(self.u, s2v, fft, c2mag, log, stats)
self.connect(self.u, s2v, fft, c2mag, stats)
if options.gain is None:
# if no gain was specified, use the mid-point in dB
g = self.u.get_gain_range()
options.gain = float(g.start()+g.stop())/2.0
self.set_gain(options.gain)
print "gain =", options.gain
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, frame, panel, vbox, argv):
stdgui2.std_top_block.__init__(self, frame, panel, vbox, argv)
self.frame = frame
self.panel = panel
parser = OptionParser(option_class=eng_option)
parser.add_option("-R",
"--rx-subdev-spec",
type="subdev",
default=(0, 0),
help="select USRP Rx side A or B (default=A)")
parser.add_option(
"-d",
"--decim",
type="int",
default=16,
help="set fgpa decimation rate to DECIM [default=%default]")
parser.add_option("-f",
"--freq",
type="eng_float",
default=None,
help="set frequency to FREQ",
metavar="FREQ")
parser.add_option("-Q",
"--observing",
type="eng_float",
default=0.0,
help="set observing frequency to FREQ")
parser.add_option("-a",
"--avg",
type="eng_float",
default=1.0,
help="set spectral averaging alpha")
parser.add_option("-V",
"--favg",
type="eng_float",
default=2.0,
help="set folder averaging alpha")
parser.add_option("-g",
"--gain",
type="eng_float",
default=None,
help="set gain in dB (default is midpoint)")
parser.add_option("-l",
"--reflevel",
type="eng_float",
default=30.0,
help="Set pulse display reference level")
parser.add_option("-L",
"--lowest",
type="eng_float",
default=1.5,
help="Lowest valid frequency bin")
parser.add_option("-e",
"--longitude",
type="eng_float",
default=-76.02,
help="Set Observer Longitude")
parser.add_option("-c",
"--latitude",
type="eng_float",
default=44.85,
help="Set Observer Latitude")
parser.add_option("-F",
"--fft_size",
type="eng_float",
default=1024,
help="Size of FFT")
parser.add_option("-t",
"--threshold",
type="eng_float",
default=2.5,
help="pulsar threshold")
parser.add_option("-p",
"--lowpass",
type="eng_float",
default=100,
help="Pulse spectra cutoff freq")
parser.add_option("-P", "--prefix", default="./", help="File prefix")
parser.add_option("-u",
"--pulsefreq",
type="eng_float",
default=0.748,
help="Observation pulse rate")
parser.add_option("-D",
"--dm",
type="eng_float",
default=1.0e-5,
help="Dispersion Measure")
parser.add_option("-O",
"--doppler",
type="eng_float",
default=1.0,
help="Doppler ratio")
parser.add_option("-B",
"--divbase",
type="eng_float",
default=20,
help="Y/Div menu base")
parser.add_option("-I",
"--division",
type="eng_float",
default=100,
help="Y/Div")
parser.add_option("-A",
"--audio_source",
default="plughw:0,0",
help="Audio input device spec")
parser.add_option("-N",
"--num_pulses",
default=1,
type="eng_float",
help="Number of display pulses")
(options, args) = parser.parse_args()
if len(args) != 0:
parser.print_help()
sys.exit(1)
self.show_debug_info = True
self.reflevel = options.reflevel
self.divbase = options.divbase
self.division = options.division
self.audiodev = options.audio_source
self.mult = int(options.num_pulses)
# Low-pass cutoff for post-detector filter
# Set to 100Hz usually, since lots of pulsars fit in this
# range
self.lowpass = options.lowpass
# What is lowest valid frequency bin in post-detector FFT?
# There's some pollution very close to DC
self.lowest_freq = options.lowest
# What (dB) threshold to use in determining spectral candidates
self.threshold = options.threshold
# Filename prefix for recording file
self.prefix = options.prefix
# Dispersion Measure (DM)
self.dm = options.dm
# Doppler shift, as a ratio
# 1.0 == no doppler shift
# 1.005 == a little negative shift
# 0.995 == a little positive shift
self.doppler = options.doppler
#
# Input frequency and observing frequency--not necessarily the
# same thing, if we're looking at the IF of some downconverter
# that's ahead of the USRP and daughtercard. This distinction
# is important in computing the correct de-dispersion filter.
#
self.frequency = options.freq
if options.observing <= 0:
self.observing_freq = options.freq
else:
self.observing_freq = options.observing
# build the graph
self.u = usrp.source_c(decim_rate=options.decim)
self.u.set_mux(
usrp.determine_rx_mux_value(self.u, options.rx_subdev_spec))
#
# Recording file, in case we ever need to record baseband data
#
self.recording = gr.file_sink(gr.sizeof_char, "/dev/null")
self.recording_state = False
self.pulse_recording = gr.file_sink(gr.sizeof_short, "/dev/null")
self.pulse_recording_state = False
#
# We come up with recording turned off, but the user may
# request recording later on
self.recording.close()
self.pulse_recording.close()
#
# Need these two for converting 12-bit baseband signals to 8-bit
#
self.tofloat = gr.complex_to_float()
self.tochar = gr.float_to_char()
# Need this for recording pulses (post-detector)
self.toshort = gr.float_to_short()
#
# The spectral measurer sets this when it has a valid
# average spectral peak-to-peak distance
# We can then use this to program the parameters for the epoch folder
#
# We set a sentimental value here
self.pulse_freq = options.pulsefreq
# Folder runs at this raw sample rate
self.folder_input_rate = 20000
# Each pulse in the epoch folder is sampled at 128 times the nominal
# pulse rate
self.folding = 128
#
# Try to find candidate parameters for rational resampler
#
save_i = 0
candidates = []
for i in range(20, 300):
input_rate = self.folder_input_rate
output_rate = int(self.pulse_freq * i)
interp = gru.lcm(input_rate, output_rate) / input_rate
decim = gru.lcm(input_rate, output_rate) / output_rate
if (interp < 500 and decim < 250000):
candidates.append(i)
# We didn't find anything, bail!
if (len(candidates) < 1):
print "Couldn't converge on resampler parameters"
sys.exit(1)
#
# Now try to find candidate with the least sampling error
#
mindiff = 999.999
for i in candidates:
diff = self.pulse_freq * i
diff = diff - int(diff)
if (diff < mindiff):
mindiff = diff
save_i = i
# Recompute rates
input_rate = self.folder_input_rate
output_rate = int(self.pulse_freq * save_i)
# Compute new interp and decim, based on best candidate
interp = gru.lcm(input_rate, output_rate) / input_rate
decim = gru.lcm(input_rate, output_rate) / output_rate
# Save optimized folding parameters, used later
self.folding = save_i
self.interp = int(interp)
self.decim = int(decim)
# So that we can view N pulses in the pulse viewer window
FOLD_MULT = self.mult
# determine the daughterboard subdevice we're using
self.subdev = usrp.selected_subdev(self.u, options.rx_subdev_spec)
self.cardtype = self.u.daughterboard_id(0)
# Compute raw input rate
input_rate = self.u.adc_freq() / self.u.decim_rate()
# BW==input_rate for complex data
self.bw = input_rate
#
# Set baseband filter bandwidth if DBS_RX:
#
if self.cardtype == usrp_dbid.DBS_RX:
lbw = input_rate / 2
if lbw < 1.0e6:
lbw = 1.0e6
self.subdev.set_bw(lbw)
#
# We use this as a crude volume control for the audio output
#
#self.volume = gr.multiply_const_ff(10**(-1))
#
# Create location data for ephem package
#
self.locality = ephem.Observer()
self.locality.long = str(options.longitude)
self.locality.lat = str(options.latitude)
#
# What is the post-detector LPF cutoff for the FFT?
#
PULSAR_MAX_FREQ = int(options.lowpass)
# First low-pass filters down to input_rate/FIRST_FACTOR
# and decimates appropriately
FIRST_FACTOR = int(input_rate / (self.folder_input_rate / 2))
first_filter = gr.firdes.low_pass(1.0, input_rate,
input_rate / FIRST_FACTOR,
input_rate / (FIRST_FACTOR * 20),
gr.firdes.WIN_HAMMING)
# Second filter runs at the output rate of the first filter,
# And low-pass filters down to PULSAR_MAX_FREQ*10
#
second_input_rate = int(input_rate / (FIRST_FACTOR / 2))
second_filter = gr.firdes.band_pass(1.0, second_input_rate, 0.10,
PULSAR_MAX_FREQ * 10,
PULSAR_MAX_FREQ * 1.5,
gr.firdes.WIN_HAMMING)
# Third filter runs at PULSAR_MAX_FREQ*20
# and filters down to PULSAR_MAX_FREQ
#
third_input_rate = PULSAR_MAX_FREQ * 20
third_filter = gr.firdes_band_pass(1.0, third_input_rate, 0.10,
PULSAR_MAX_FREQ,
PULSAR_MAX_FREQ / 10.0,
gr.firdes.WIN_HAMMING)
#
# Create the appropriate FFT scope
#
self.scope = ra_fftsink.ra_fft_sink_f(panel,
fft_size=int(options.fft_size),
sample_rate=PULSAR_MAX_FREQ * 2,
title="Post-detector spectrum",
ofunc=self.pulsarfunc,
xydfunc=self.xydfunc,
fft_rate=200)
#
# Tell scope we're looking from DC to PULSAR_MAX_FREQ
#
self.scope.set_baseband_freq(0.0)
#
# Setup stripchart for showing pulse profiles
#
hz = "%5.3fHz " % self.pulse_freq
per = "(%5.3f sec)" % (1.0 / self.pulse_freq)
sr = "%d sps" % (int(self.pulse_freq * self.folding))
times = " %d Pulse Intervals" % self.mult
self.chart = ra_stripchartsink.stripchart_sink_f(
panel,
sample_rate=1,
stripsize=self.folding * FOLD_MULT,
parallel=True,
title="Pulse Profiles: " + hz + per + times,
xlabel="Seconds @ " + sr,
ylabel="Level",
autoscale=True,
divbase=self.divbase,
scaling=1.0 / (self.folding * self.pulse_freq))
self.chart.set_ref_level(self.reflevel)
self.chart.set_y_per_div(self.division)
# De-dispersion filter setup
#
# Do this here, just before creating the filter
# that will use the taps.
#
ntaps = self.compute_disp_ntaps(self.dm, self.bw, self.observing_freq)
# Taps for the de-dispersion filter
self.disp_taps = Numeric.zeros(ntaps, Numeric.Complex64)
# Compute the de-dispersion filter now
self.compute_dispfilter(self.dm, self.doppler, self.bw,
self.observing_freq)
#
# Call constructors for receive chains
#
#
# Now create the FFT filter using the computed taps
self.dispfilt = gr.fft_filter_ccc(1, self.disp_taps)
#
# Audio sink
#
#print "input_rate ", second_input_rate, "audiodev ", self.audiodev
#self.audio = audio.sink(second_input_rate, self.audiodev)
#
# The three post-detector filters
# Done this way to allow an audio path (up to 10Khz)
# ...and also because going from xMhz down to ~100Hz
# In a single filter doesn't seem to work.
#
self.first = gr.fir_filter_fff(FIRST_FACTOR / 2, first_filter)
p = second_input_rate / (PULSAR_MAX_FREQ * 20)
self.second = gr.fir_filter_fff(int(p), second_filter)
self.third = gr.fir_filter_fff(10, third_filter)
# Detector
self.detector = gr.complex_to_mag_squared()
self.enable_comb_filter = False
# Epoch folder comb filter
if self.enable_comb_filter == True:
bogtaps = Numeric.zeros(512, Numeric.Float64)
self.folder_comb = gr.fft_filter_ccc(1, bogtaps)
# Rational resampler
self.folder_rr = blks2.rational_resampler_fff(self.interp, self.decim)
# Epoch folder bandpass
bogtaps = Numeric.zeros(1, Numeric.Float64)
self.folder_bandpass = gr.fir_filter_fff(1, bogtaps)
# Epoch folder F2C/C2F
self.folder_f2c = gr.float_to_complex()
self.folder_c2f = gr.complex_to_float()
# Epoch folder S2P
self.folder_s2p = gr.serial_to_parallel(gr.sizeof_float,
self.folding * FOLD_MULT)
# Epoch folder IIR Filter (produces average pulse profiles)
self.folder_iir = gr.single_pole_iir_filter_ff(
1.0 / options.favg, self.folding * FOLD_MULT)
#
# Set all the epoch-folder goop up
#
self.set_folding_params()
#
# Start connecting configured modules in the receive chain
#
# Connect raw USRP to de-dispersion filter, detector
self.connect(self.u, self.dispfilt, self.detector)
# Connect detector output to FIR LPF
# in two stages, followed by the FFT scope
self.connect(self.detector, self.first, self.second, self.third,
self.scope)
# Connect audio output
#self.connect(self.first, self.volume)
#self.connect(self.volume, (self.audio, 0))
#self.connect(self.volume, (self.audio, 1))
# Connect epoch folder
if self.enable_comb_filter == True:
self.connect(self.first, self.folder_bandpass, self.folder_rr,
self.folder_f2c, self.folder_comb, self.folder_c2f,
self.folder_s2p, self.folder_iir, self.chart)
else:
self.connect(self.first, self.folder_bandpass, self.folder_rr,
self.folder_s2p, self.folder_iir, self.chart)
# Connect baseband recording file (initially /dev/null)
self.connect(self.u, self.tofloat, self.tochar, self.recording)
# Connect pulse recording file (initially /dev/null)
self.connect(self.first, self.toshort, self.pulse_recording)
#
# Build the GUI elements
#
self._build_gui(vbox)
# Make GUI agree with command-line
self.myform['average'].set_value(int(options.avg))
self.myform['foldavg'].set_value(int(options.favg))
# Make spectral averager agree with command line
if options.avg != 1.0:
self.scope.set_avg_alpha(float(1.0 / options.avg))
self.scope.set_average(True)
# set initial values
if options.gain is None:
# if no gain was specified, use the mid-point in dB
g = self.subdev.gain_range()
options.gain = float(g[0] + g[1]) / 2
if options.freq is None:
# if no freq was specified, use the mid-point
r = self.subdev.freq_range()
options.freq = float(r[0] + r[1]) / 2
self.set_gain(options.gain)
#self.set_volume(-10.0)
if not (self.set_freq(options.freq)):
self._set_status_msg("Failed to set initial frequency")
self.myform['decim'].set_value(self.u.decim_rate())
self.myform['fs@usb'].set_value(self.u.adc_freq() /
self.u.decim_rate())
self.myform['dbname'].set_value(self.subdev.name())
self.myform['DM'].set_value(self.dm)
self.myform['Doppler'].set_value(self.doppler)
#
# Start the timer that shows current LMST on the GUI
#
self.lmst_timer.Start(1000)
def __init__(self, usrp_rate, tuner_callback, options):
gr.hier_block2.__init__(self, "sense_path",
gr.io_signature(1, 1, gr.sizeof_gr_complex), # Input signature
gr.io_signature(0, 0, 0)) # Output signature
self.usrp_rate = usrp_rate
self.usrp_tune = tuner_callback
self.num_tests = options.num_tests
self.threshold = options.threshold
self.min_freq = options.start_freq
self.max_freq = options.end_freq
if self.min_freq > self.max_freq:
self.min_freq, self.max_freq = self.max_freq, self.min_freq # swap them
self.fft_size = options.fft_size
if not options.real_time:
realtime = False
else:
# Attempt to enable realtime scheduling
r = gr.enable_realtime_scheduling()
if r == gr.RT_OK:
realtime = True
else:
realtime = False
print "Note: failed to enable realtime scheduling"
# build graph
s2v = gr.stream_to_vector(gr.sizeof_gr_complex, self.fft_size)
mywindow = window.blackmanharris(self.fft_size)
fft = gr.fft_vcc(self.fft_size, True, mywindow)
power = 0
for tap in mywindow:
power += tap*tap
c2mag = gr.complex_to_mag_squared(self.fft_size)
# FIXME the log10 primitive is dog slow
log = gr.nlog10_ff(10, self.fft_size,
-20*math.log10(self.fft_size)-10*math.log10(power/self.fft_size))
# Set the freq_step to 75% of the actual data throughput.
# This allows us to discard the bins on both ends of the spectrum.
#changed on 2011 May 31, MR -- maybe change back at some point
self.freq_step = self.usrp_rate
self.min_center_freq = self.min_freq + self.freq_step/2
nsteps = math.ceil((self.max_freq - self.min_freq) / self.freq_step)
self.max_center_freq = self.min_center_freq + (nsteps * self.freq_step)
self.next_freq = self.min_center_freq
tune_delay = max(0, int(round(options.tune_delay * self.usrp_rate / self.fft_size))) # in fft_frames
dwell_delay = max(1, int(round(options.dwell_delay * self.usrp_rate / self.fft_size))) # in fft_frames
self.msgq = gr.msg_queue(16)
self._tune_callback = tune(self) # hang on to this to keep it from being GC'd
stats = gr.bin_statistics_f(self.fft_size, self.msgq,
self._tune_callback, tune_delay, dwell_delay)
# FIXME leave out the log10 until we speed it up
#self.connect(self, s2v, fft, c2mag, log, stats)
self.connect(self, s2v, fft, c2mag, stats)
def __init__(self, subc, vlen, ss):
gr.hier_block2.__init__(
self, "new_snr_estimator",
gr.io_signature(1, 1, gr.sizeof_gr_complex * vlen),
gr.io_signature(1, 1, gr.sizeof_float))
print "Created Milan's SNR estimator"
trigger = [0] * vlen
trigger[0] = 1
u = range(vlen / ss * (ss - 1))
zeros_ind = map(lambda z: z + 1 + z / (ss - 1), u)
skip1 = skip(gr.sizeof_gr_complex, vlen)
for x in zeros_ind:
skip1.skip(x)
#print "skipped zeros",zeros_ind
v = range(vlen / ss)
ones_ind = map(lambda z: z * ss, v)
skip2 = skip(gr.sizeof_gr_complex, vlen)
for x in ones_ind:
skip2.skip(x)
#print "skipped ones",ones_ind
v2s = gr.vector_to_stream(gr.sizeof_gr_complex, vlen)
s2v1 = gr.stream_to_vector(gr.sizeof_gr_complex, vlen / ss)
trigger_src_1 = gr.vector_source_b(trigger, True)
s2v2 = gr.stream_to_vector(gr.sizeof_gr_complex, vlen / ss * (ss - 1))
trigger_src_2 = gr.vector_source_b(trigger, True)
mag_sq_ones = gr.complex_to_mag_squared(vlen / ss)
mag_sq_zeros = gr.complex_to_mag_squared(vlen / ss * (ss - 1))
filt_ones = gr.single_pole_iir_filter_ff(0.1, vlen / ss)
filt_zeros = gr.single_pole_iir_filter_ff(0.1, vlen / ss * (ss - 1))
sum_ones = vector_sum_vff(vlen / ss)
sum_zeros = vector_sum_vff(vlen / ss * (ss - 1))
D = gr.divide_ff()
P = gr.multiply_ff()
mult1 = gr.multiply_const_ff(ss - 1.0)
add1 = gr.add_const_ff(-1.0)
mult2 = gr.multiply_const_ff(1. / ss)
scsnrdb = gr.nlog10_ff(10, 1, 0)
filt_end = gr.single_pole_iir_filter_ff(0.1)
self.connect(self, v2s, skip1, s2v1, mag_sq_ones, filt_ones, sum_ones)
self.connect(trigger_src_1, (skip1, 1))
self.connect(v2s, skip2, s2v2, mag_sq_zeros, filt_zeros, sum_zeros)
self.connect(trigger_src_2, (skip2, 1))
self.connect(sum_ones, D)
self.connect(sum_zeros, (D, 1))
self.connect(D, mult1, add1, mult2)
self.connect(mult2, scsnrdb, filt_end, self)
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, 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):
gr.top_block.__init__(self)
usage = "usage: %prog [options] host min_freq max_freq"
parser = OptionParser(option_class=eng_option, usage=usage)
parser.add_option("-g", "--gain", type="eng_float", default=None,
help="set gain in dB (default is midpoint)")
parser.add_option("", "--tune-delay", type="eng_float", default=5e-5, metavar="SECS",
help="time to delay (in seconds) after changing frequency [default=%default]")
parser.add_option("", "--dwell-delay", type="eng_float", default=50e-5, metavar="SECS",
help="time to dwell (in seconds) at a given frequncy [default=%default]")
parser.add_option("-F", "--fft-size", type="int", default=256,
help="specify number of FFT bins [default=%default]")
parser.add_option("-d", "--decim", type="intx", default=16,
help="set decimation to DECIM [default=%default]")
parser.add_option("", "--real-time", action="store_true", default=False,
help="Attempt to enable real-time scheduling")
(options, args) = parser.parse_args()
if len(args) != 3:
parser.print_help()
sys.exit(1)
self.address = args[0]
self.min_freq = eng_notation.str_to_num(args[1])
self.max_freq = eng_notation.str_to_num(args[2])
self.decim = options.decim
self.gain = options.gain
if self.min_freq > self.max_freq:
self.min_freq, self.max_freq = self.max_freq, self.min_freq # swap them
self.fft_size = options.fft_size
if not options.real_time:
realtime = False
else:
# Attempt to enable realtime scheduling
r = gr.enable_realtime_scheduling()
if r == gr.RT_OK:
realtime = True
else:
realtime = False
print "Note: failed to enable realtime scheduling"
adc_rate = 102.4e6
self.int_rate = adc_rate / self.decim
print "Sampling rate: ", self.int_rate
# build graph
self.port = 10001
self.src = msdd.source_simple(self.address, self.port)
self.src.set_decim_rate(self.decim)
self.set_gain(self.gain)
self.set_freq(self.min_freq)
s2v = gr.stream_to_vector(gr.sizeof_gr_complex, self.fft_size)
mywindow = window.blackmanharris(self.fft_size)
fft = gr.fft_vcc(self.fft_size, True, mywindow, True)
power = 0
for tap in mywindow:
power += tap*tap
norm = gr.multiply_const_cc(1.0/self.fft_size)
c2mag = gr.complex_to_mag_squared(self.fft_size)
# FIXME the log10 primitive is dog slow
log = gr.nlog10_ff(10, self.fft_size,
-20*math.log10(self.fft_size)-10*math.log10(power/self.fft_size))
# Set the freq_step to % of the actual data throughput.
# This allows us to discard the bins on both ends of the spectrum.
self.percent = 0.4
self.freq_step = self.percent * self.int_rate
self.min_center_freq = self.min_freq + self.freq_step/2
nsteps = math.ceil((self.max_freq - self.min_freq) / self.freq_step)
self.max_center_freq = self.min_center_freq + (nsteps * self.freq_step)
self.next_freq = self.min_center_freq
tune_delay = max(0, int(round(options.tune_delay * self.int_rate / self.fft_size))) # in fft_frames
dwell_delay = max(1, int(round(options.dwell_delay * self.int_rate / self.fft_size))) # in fft_frames
self.msgq = gr.msg_queue(16)
self._tune_callback = tune(self) # hang on to this to keep it from being GC'd
stats = gr.bin_statistics_f(self.fft_size, self.msgq,
self._tune_callback, tune_delay, dwell_delay)
# FIXME leave out the log10 until we speed it up
self.connect(self.src, s2v, fft, c2mag, log, stats)
def __init__(self):
gr.top_block.__init__(self)
global parser
parser = OptionParser(option_class=eng_option)
parser.add_option("-a",
"--args",
type="string",
default="",
help="UHD device address [default=%default]")
#parser.add_option("-e", "--interface", type="string", default="eth0", help="Select ethernet interface. Default is eth0")
#parser.add_option("-m", "--MAC_addr", type="string", default="", help="Select USRP2 by its MAC address.Default is auto-select")
parser.add_option("-p",
"--start",
type="eng_float",
default=1e7,
help="Start ferquency [default = %default]")
parser.add_option("-q",
"--stop",
type="eng_float",
default=1e8,
help="Stop ferquency [default = %default]")
parser.add_option(
"",
"--tune-delay",
type="eng_float",
default=1e-3,
metavar="SECS",
help=
"time to delay (in seconds) after changing frequency[default=%default]"
)
parser.add_option(
"",
"--dwell-delay",
type="eng_float",
default=10e-3,
metavar="SECS",
help=
"time to dwell (in seconds) at a given frequncy[default=%default]")
parser.add_option("-g",
"--gain",
type="eng_float",
default=None,
help="set gain in dB (default is midpoint)")
parser.add_option("-s",
"--fft-size",
type="int",
default=256,
help="specify number of FFT bins [default=%default]")
parser.add_option("-d",
"--decim",
type="intx",
default=16,
help="set decimation to DECIM [default=%default]")
parser.add_option("-i",
"--input_file",
default="",
help="radio input file",
metavar="FILE")
parser.add_option(
"-S",
"--sense-bins",
type="int",
default=64,
help="set number of bins in the OFDM block [default=%default]")
(options, args) = parser.parse_args()
if options.input_file == "":
self.IS_USRP2 = True
else:
self.IS_USRP2 = False
self.min_freq = options.start
self.max_freq = options.stop
print "min_freq=", self.min_freq
print "max_freq=", self.max_freq
if self.min_freq > self.max_freq:
self.min_freq, self.max_freq = self.max_freq, self.min_freq # swap them
print "Start and stop frequencies order swapped!"
self.fft_size = options.fft_size
self.ofdm_bins = options.sense_bins
# build graph
s2v = gr.stream_to_vector(gr.sizeof_gr_complex, self.fft_size)
mywindow = window.blackmanharris(self.fft_size)
fft = gr.fft_vcc(self.fft_size, True, mywindow)
power = 0
for tap in mywindow:
power += tap * tap
c2mag = gr.complex_to_mag_squared(self.fft_size)
#log = gr.nlog10_ff(10, self.fft_size, -20*math.log10(self.fft_size)-10*math.log10(power/self.fft_size))
# modifications for USRP2
print "*******************in sensor init********************"
if self.IS_USRP2:
self.u = uhd.usrp_source(device_addr=options.args,
io_type=uhd.io_type.COMPLEX_FLOAT32,
num_channels=1)
samp_rate = 100**6 / options.decim
self.u.set_samp_rate(samp_rate)
else:
self.u = gr.file_source(gr.sizeof_gr_complex, options.input_file,
True)
samp_rate = 100e6 / options.decim
self.freq_step = 0 #0.75* samp_rate
self.min_center_freq = (self.min_freq + self.max_freq) / 2
global BW
BW = self.max_freq - self.min_freq
print "bandwidth=", BW
global size
size = self.fft_size
global ofdm_bins
ofdm_bins = self.ofdm_bins
global usr
#global thrshold_inorder
usr = samp_rate
nsteps = 10 #math.ceil((self.max_freq - self.min_freq) / self.freq_step)
self.max_center_freq = self.min_center_freq + (nsteps * self.freq_step)
self.next_freq = self.min_center_freq
tune_delay = max(0,
int(
round(options.tune_delay * samp_rate /
self.fft_size))) # in fft_frames
print tune_delay
dwell_delay = max(1,
int(
round(options.dwell_delay * samp_rate /
self.fft_size))) # in fft_frames
print dwell_delay
self.msgq = gr.msg_queue(16)
self._tune_callback = tune(self)
# hang on to this to keep it from being GC'd
stats = gr.bin_statistics_f(self.fft_size, self.msgq,
self._tune_callback, tune_delay,
dwell_delay)
self.connect(self.u, s2v, fft, c2mag, stats)
if options.gain is None:
# if no gain was specified, use the mid-point in dB
g = self.u.get_gain_range()
options.gain = float(g.start() + g.stop()) / 2
def complex_to_mag_squared(N):
op = gr.complex_to_mag_squared()
tb = helper(N, op, gr.sizeof_gr_complex, gr.sizeof_float, 1, 1)
return tb
def __init__(self, demod_class, rx_callback, options, source_block):
gr.hier_block2.__init__(self, "receive_path",
gr.io_signature(1, 1, gr.sizeof_gr_complex),
gr.io_signature(0, 0, 0))
options = copy.copy(
options) # make a copy so we can destructively modify
self._verbose = options.verbose
self._bitrate = options.bitrate # desired bit rate
self._rx_callback = rx_callback # this callback is fired when a packet arrives
self._demod_class = demod_class # the demodulator_class we're using
self._chbw_factor = options.chbw_factor # channel filter bandwidth factor
# Get demod_kwargs
demod_kwargs = self._demod_class.extract_kwargs_from_options(options)
#Give hooks of usrp to blocks downstream
self.source_block = source_block
#########################################
# Build Blocks
#########################################
# Build the demodulator
self.demodulator = self._demod_class(**demod_kwargs)
# Make sure the channel BW factor is between 1 and sps/2
# or the filter won't work.
if (self._chbw_factor < 1.0
or self._chbw_factor > self.samples_per_symbol() / 2):
sys.stderr.write(
"Channel bandwidth factor ({0}) must be within the range [1.0, {1}].\n"
.format(self._chbw_factor,
self.samples_per_symbol() / 2))
sys.exit(1)
# Design filter to get actual channel we want
sw_decim = 1
chan_coeffs = gr.firdes.low_pass(
1.0, # gain
sw_decim * self.samples_per_symbol(), # sampling rate
self._chbw_factor, # midpoint of trans. band
0.5, # width of trans. band
gr.firdes.WIN_HANN) # filter type
self.channel_filter = gr.fft_filter_ccc(sw_decim, chan_coeffs)
# receiver
self.packet_receiver = \
digital.demod_pkts(self.demodulator,
access_code=None,
callback=self._rx_callback,
threshold=-1)
# Carrier Sensing Blocks
alpha = 0.001
thresh = 30 # in dB, will have to adjust
self.probe = gr.probe_avg_mag_sqrd_c(thresh, alpha)
# Display some information about the setup
if self._verbose:
self._print_verbage()
# More Carrier Sensing with FFT
#self.gr_vector_sink = gr.vector_sink_c(1024)
#self.gr_stream_to_vector = gr.stream_to_vector(gr.sizeof_gr_complex*1, 1024)
#self.gr_head = gr.head(gr.sizeof_gr_complex*1024, 1024)
#self.fft = fft.fft_vcc(1024, True, (window.blackmanharris(1024)), True, 1)
# Parameters
usrp_rate = options.bitrate
self.fft_size = 1024
self.min_freq = 2.4e9 - 0.75e6
self.max_freq = 2.4e9 + 0.75e6
self.tune_delay = 0.001
self.dwell_delay = 0.01
s2v = gr.stream_to_vector(gr.sizeof_gr_complex, self.fft_size)
mywindow = window.blackmanharris(self.fft_size)
fft = gr.fft_vcc(self.fft_size, True, mywindow)
power = 0
for tap in mywindow:
power += tap * tap
c2mag = gr.complex_to_mag_squared(self.fft_size)
# FIXME the log10 primitive is dog slow
log = gr.nlog10_ff(
10, self.fft_size, -20 * math.log10(self.fft_size) -
10 * math.log10(power / self.fft_size))
# Set the freq_step to 75% of the actual data throughput.
# This allows us to discard the bins on both ends of the spectrum.
#self.freq_step = 0.75 * usrp_rate
#self.min_center_freq = self.min_freq + self.freq_step/2
#nsteps = math.ceil((self.max_freq - self.min_freq) / self.freq_step)
#self.max_center_freq = self.min_center_freq + (nsteps * self.freq_step)
self.freq_step = 1.5e6
self.min_center_freq = self.min_freq
nsteps = 1
self.max_center_freq = self.max_freq
self.next_freq = self.min_center_freq
tune_delay = max(0,
int(round(self.tune_delay * usrp_rate /
self.fft_size))) # in fft_frames
dwell_delay = max(1,
int(
round(self.dwell_delay * usrp_rate /
self.fft_size))) # in fft_frames
self.msgq = gr.msg_queue(16)
self._tune_callback = tune(
self) # hang on to this to keep it from being GC'd
stats = gr.bin_statistics_f(self.fft_size, self.msgq,
self._tune_callback, tune_delay,
dwell_delay)
######################################################
# Connect Blocks Together
######################################################
#channel-filter-->Probe_Avg_Mag_Sqrd
# -->Packet_Receiver (Demod Done Here!!)
#
# connect FFT sampler to system
#self.connect(self, self.gr_stream_to_vector, self.fft, self.gr_vector_sink)
# connect block input to channel filter
self.connect(self, self.channel_filter)
# connect the channel input filter to the carrier power detector
self.connect(self.channel_filter, self.probe)
# connect channel filter to the packet receiver
self.connect(self.channel_filter, self.packet_receiver)
# FIXME leave out the log10 until we speed it up
#self.connect(self.u, s2v, fft, c2mag, log, stats)
self.connect(self.channel_filter, s2v, fft, c2mag, stats)