def run_test (f,Kb,bitspersymbol,K,dimensionality,tot_constellation,N0,seed):
tb = gr.top_block ()
# TX
src = gr.lfsr_32k_source_s()
src_head = gr.head (gr.sizeof_short,Kb/16) # packet size in shorts
s2fsmi = gr.packed_to_unpacked_ss(bitspersymbol,gr.GR_MSB_FIRST) # unpack shorts to symbols compatible with the FSM input cardinality
enc = trellis.encoder_ss(f,0) # initial state = 0
# essentially here we implement the combination of modulation and channel as a memoryless modulation (the memory induced by the channel is hidden in the FSM)
mod = gr.chunks_to_symbols_sf(tot_constellation,dimensionality)
# CHANNEL
add = gr.add_ff()
noise = gr.noise_source_f(gr.GR_GAUSSIAN,math.sqrt(N0/2),seed)
# RX
metrics = trellis.metrics_f(f.O(),dimensionality,tot_constellation,digital.TRELLIS_EUCLIDEAN) # data preprocessing to generate metrics for Viterbi
va = trellis.viterbi_s(f,K,0,-1) # Put -1 if the Initial/Final states are not set.
fsmi2s = gr.unpacked_to_packed_ss(bitspersymbol,gr.GR_MSB_FIRST) # pack FSM input symbols to shorts
dst = gr.check_lfsr_32k_s();
tb.connect (src,src_head,s2fsmi,enc,mod)
tb.connect (mod,(add,0))
tb.connect (noise,(add,1))
tb.connect (add,metrics)
tb.connect (metrics,va,fsmi2s,dst)
tb.run()
ntotal = dst.ntotal ()
nright = dst.nright ()
runlength = dst.runlength ()
#print ntotal,nright,runlength
return (ntotal,ntotal-nright)
def run_test (fo,fi,interleaver,Kb,bitspersymbol,K,dimensionality,constellation,Es,N0,IT,seed):
tb = gr.top_block ()
# TX
src = gr.lfsr_32k_source_s()
src_head = gr.head (gr.sizeof_short,Kb/16) # packet size in shorts
s2fsmi = gr.packed_to_unpacked_ss(bitspersymbol,gr.GR_MSB_FIRST) # unpack shorts to symbols compatible with the outer FSM input cardinality
enc = trellis.sccc_encoder_ss(fo,0,fi,0,interleaver,K)
mod = gr.chunks_to_symbols_sf(constellation,dimensionality)
# CHANNEL
add = gr.add_ff()
noise = gr.noise_source_f(gr.GR_GAUSSIAN,math.sqrt(N0/2),seed)
# RX
dec = trellis.sccc_decoder_combined_fs(fo,0,-1,fi,0,-1,interleaver,K,IT,trellis.TRELLIS_MIN_SUM,dimensionality,constellation,digital.TRELLIS_EUCLIDEAN,1.0)
fsmi2s = gr.unpacked_to_packed_ss(bitspersymbol,gr.GR_MSB_FIRST) # pack FSM input symbols to shorts
dst = gr.check_lfsr_32k_s()
#tb.connect (src,src_head,s2fsmi,enc_out,inter,enc_in,mod)
tb.connect (src,src_head,s2fsmi,enc,mod)
tb.connect (mod,(add,0))
tb.connect (noise,(add,1))
#tb.connect (add,head)
#tb.connect (tail,fsmi2s,dst)
tb.connect (add,dec,fsmi2s,dst)
tb.run()
#print enc_out.ST(), enc_in.ST()
ntotal = dst.ntotal ()
nright = dst.nright ()
runlength = dst.runlength ()
return (ntotal,ntotal-nright)
def run_test (f,Kb,bitspersymbol,K,dimensionality,tot_constellation,N0,seed):
tb = gr.top_block ()
# TX
src = gr.lfsr_32k_source_s()
src_head = gr.head (gr.sizeof_short,Kb/16) # packet size in shorts
s2fsmi = gr.packed_to_unpacked_ss(bitspersymbol,gr.GR_MSB_FIRST) # unpack shorts to symbols compatible with the FSM input cardinality
enc = trellis.encoder_ss(f,0) # initial state = 0
# essentially here we implement the combination of modulation and channel as a memoryless modulation (the memory induced by the channel is hidden in the FSM)
mod = gr.chunks_to_symbols_sf(tot_constellation,dimensionality)
# CHANNEL
add = gr.add_ff()
noise = gr.noise_source_f(gr.GR_GAUSSIAN,math.sqrt(N0/2),seed)
# RX
metrics = trellis.metrics_f(f.O(),dimensionality,tot_constellation,trellis.TRELLIS_EUCLIDEAN) # data preprocessing to generate metrics for Viterbi
va = trellis.viterbi_s(f,K,0,-1) # Put -1 if the Initial/Final states are not set.
fsmi2s = gr.unpacked_to_packed_ss(bitspersymbol,gr.GR_MSB_FIRST) # pack FSM input symbols to shorts
dst = gr.check_lfsr_32k_s();
tb.connect (src,src_head,s2fsmi,enc,mod)
tb.connect (mod,(add,0))
tb.connect (noise,(add,1))
tb.connect (add,metrics)
tb.connect (metrics,va,fsmi2s,dst)
tb.run()
ntotal = dst.ntotal ()
nright = dst.nright ()
runlength = dst.runlength ()
#print ntotal,nright,runlength
return (ntotal,ntotal-nright)
def run_test(f, Kb, bitspersymbol, K, dimensionality, constellation, N0, seed):
tb = gr.top_block()
# TX
# packet = [0]*Kb
# for i in range(Kb-1*16): # last 16 bits = 0 to drive the final state to 0
# packet[i] = random.randint(0, 1) # random 0s and 1s
# src = gr.vector_source_s(packet,False)
src = gr.lfsr_32k_source_s()
src_head = gr.head(gr.sizeof_short, Kb / 16) # packet size in shorts
# b2s = gr.unpacked_to_packed_ss(1,gr.GR_MSB_FIRST) # pack bits in shorts
s2fsmi = gr.packed_to_unpacked_ss(
bitspersymbol, gr.GR_MSB_FIRST
) # unpack shorts to symbols compatible with the FSM input cardinality
enc = trellis.encoder_ss(f, 0) # initial state = 0
mod = gr.chunks_to_symbols_sf(constellation, dimensionality)
# CHANNEL
add = gr.add_ff()
noise = gr.noise_source_f(gr.GR_GAUSSIAN, math.sqrt(N0 / 2), seed)
# RX
metrics = trellis.metrics_f(
f.O(), dimensionality, constellation, digital.TRELLIS_EUCLIDEAN
) # data preprocessing to generate metrics for Viterbi
va = trellis.viterbi_s(f, K, 0, -1) # Put -1 if the Initial/Final states are not set.
fsmi2s = gr.unpacked_to_packed_ss(bitspersymbol, gr.GR_MSB_FIRST) # pack FSM input symbols to shorts
# s2b = gr.packed_to_unpacked_ss(1,gr.GR_MSB_FIRST) # unpack shorts to bits
# dst = gr.vector_sink_s();
dst = gr.check_lfsr_32k_s()
tb.connect(src, src_head, s2fsmi, enc, mod)
# tb.connect (src,b2s,s2fsmi,enc,mod)
tb.connect(mod, (add, 0))
tb.connect(noise, (add, 1))
tb.connect(add, metrics)
tb.connect(metrics, va, fsmi2s, dst)
# tb.connect (metrics,va,fsmi2s,s2b,dst)
tb.run()
# A bit of cheating: run the program once and print the
# final encoder state..
# Then put it as the last argument in the viterbi block
# print "final state = " , enc.ST()
ntotal = dst.ntotal()
nright = dst.nright()
runlength = dst.runlength()
# ntotal = len(packet)
# if len(dst.data()) != ntotal:
# print "Error: not enough data\n"
# nright = 0;
# for i in range(ntotal):
# if packet[i]==dst.data()[i]:
# nright=nright+1
# else:
# print "Error in ", i
return (ntotal, ntotal - nright)
def run_test (f,Kb,bitspersymbol,K,dimensionality,constellation,N0,seed):
tb = gr.top_block ()
# TX
#packet = [0]*Kb
#for i in range(Kb-1*16): # last 16 bits = 0 to drive the final state to 0
#packet[i] = random.randint(0, 1) # random 0s and 1s
#src = gr.vector_source_s(packet,False)
src = gr.lfsr_32k_source_s()
src_head = gr.head (gr.sizeof_short,Kb/16) # packet size in shorts
#b2s = gr.unpacked_to_packed_ss(1,gr.GR_MSB_FIRST) # pack bits in shorts
s2fsmi = gr.packed_to_unpacked_ss(bitspersymbol,gr.GR_MSB_FIRST) # unpack shorts to symbols compatible with the FSM input cardinality
enc = trellis.encoder_ss(f,0) # initial state = 0
mod = gr.chunks_to_symbols_sf(constellation,dimensionality)
# CHANNEL
add = gr.add_ff()
noise = gr.noise_source_f(gr.GR_GAUSSIAN,math.sqrt(N0/2),seed)
# RX
metrics = trellis.metrics_f(f.O(),dimensionality,constellation,trellis.TRELLIS_EUCLIDEAN) # data preprocessing to generate metrics for Viterbi
va = trellis.viterbi_s(f,K,0,-1) # Put -1 if the Initial/Final states are not set.
fsmi2s = gr.unpacked_to_packed_ss(bitspersymbol,gr.GR_MSB_FIRST) # pack FSM input symbols to shorts
#s2b = gr.packed_to_unpacked_ss(1,gr.GR_MSB_FIRST) # unpack shorts to bits
#dst = gr.vector_sink_s();
dst = gr.check_lfsr_32k_s()
tb.connect (src,src_head,s2fsmi,enc,mod)
#tb.connect (src,b2s,s2fsmi,enc,mod)
tb.connect (mod,(add,0))
tb.connect (noise,(add,1))
tb.connect (add,metrics)
tb.connect (metrics,va,fsmi2s,dst)
#tb.connect (metrics,va,fsmi2s,s2b,dst)
tb.run()
# A bit of cheating: run the program once and print the
# final encoder state..
# Then put it as the last argument in the viterbi block
#print "final state = " , enc.ST()
ntotal = dst.ntotal ()
nright = dst.nright ()
runlength = dst.runlength ()
#ntotal = len(packet)
#if len(dst.data()) != ntotal:
#print "Error: not enough data\n"
#nright = 0;
#for i in range(ntotal):
#if packet[i]==dst.data()[i]:
#nright=nright+1
#else:
#print "Error in ", i
return (ntotal,ntotal-nright)
def test_002(self):
# Test get methods
set_type = gr.GR_GAUSSIAN
set_ampl = 10
op = gr.noise_source_f(set_type, set_ampl, 10)
get_type = op.type()
get_ampl = op.amplitude()
self.assertEqual(get_type, set_type)
self.assertEqual(get_ampl, set_ampl)
def test_002(self):
# Test get methods
set_type = gr.GR_GAUSSIAN
set_ampl = 10
op = gr.noise_source_f(set_type, set_ampl, 10)
get_type = op.type
get_ampl = op.amplitude
self.assertEqual(get_type, set_type)
self.assertEqual(get_ampl, set_ampl)
def run_test(f, Kb, bitspersymbol, K, dimensionality, constellation, N0, seed,
P):
tb = gr.top_block()
# TX
src = gr.lfsr_32k_source_s()
src_head = gr.head(gr.sizeof_short, Kb / 16 * P) # packet size in shorts
s2fsmi = gr.packed_to_unpacked_ss(
bitspersymbol, gr.GR_MSB_FIRST
) # unpack shorts to symbols compatible with the FSM input cardinality
s2p = gr.stream_to_streams(gr.sizeof_short, P) # serial to parallel
enc = trellis.encoder_ss(f, 0) # initiali state = 0
mod = gr.chunks_to_symbols_sf(constellation, dimensionality)
# CHANNEL
add = []
noise = []
for i in range(P):
add.append(gr.add_ff())
noise.append(gr.noise_source_f(gr.GR_GAUSSIAN, math.sqrt(N0 / 2),
seed))
# RX
metrics = trellis.metrics_f(
f.O(), dimensionality, constellation, digital.TRELLIS_EUCLIDEAN
) # data preprocessing to generate metrics for Viterbi
va = trellis.viterbi_s(
f, K, 0, -1) # Put -1 if the Initial/Final states are not set.
p2s = gr.streams_to_stream(gr.sizeof_short, P) # parallel to serial
fsmi2s = gr.unpacked_to_packed_ss(
bitspersymbol, gr.GR_MSB_FIRST) # pack FSM input symbols to shorts
dst = gr.check_lfsr_32k_s()
tb.connect(src, src_head, s2fsmi, s2p)
for i in range(P):
tb.connect((s2p, i), (enc, i), (mod, i))
tb.connect((mod, i), (add[i], 0))
tb.connect(noise[i], (add[i], 1))
tb.connect(add[i], (metrics, i))
tb.connect((metrics, i), (va, i), (p2s, i))
tb.connect(p2s, fsmi2s, dst)
tb.run()
# A bit of cheating: run the program once and print the
# final encoder state.
# Then put it as the last argument in the viterbi block
#print "final state = " , enc.ST()
ntotal = dst.ntotal()
nright = dst.nright()
runlength = dst.runlength()
return (ntotal, ntotal - nright)
def run_test(f, Kb, bitspersymbol, K, channel, modulation, dimensionality,
tot_constellation, N0, seed):
tb = gr.top_block()
L = len(channel)
# TX
# this for loop is TOO slow in python!!!
packet = [0] * (K + 2 * L)
random.seed(seed)
for i in range(len(packet)):
packet[i] = random.randint(0, 2**bitspersymbol - 1) # random symbols
for i in range(L): # first/last L symbols set to 0
packet[i] = 0
packet[len(packet) - i - 1] = 0
src = gr.vector_source_s(packet, False)
mod = gr.chunks_to_symbols_sf(modulation[1], modulation[0])
# CHANNEL
isi = gr.fir_filter_fff(1, channel)
add = gr.add_ff()
noise = gr.noise_source_f(gr.GR_GAUSSIAN, math.sqrt(N0 / 2), seed)
# RX
skip = gr.skiphead(
gr.sizeof_float, L
) # skip the first L samples since you know they are coming from the L zero symbols
#metrics = trellis.metrics_f(f.O(),dimensionality,tot_constellation,trellis.TRELLIS_EUCLIDEAN) # data preprocessing to generate metrics for Viterbi
#va = trellis.viterbi_s(f,K+L,0,0) # Put -1 if the Initial/Final states are not set.
va = trellis.viterbi_combined_s(
f, K + L, 0, 0, dimensionality, tot_constellation,
trellis.TRELLIS_EUCLIDEAN
) # using viterbi_combined_s instead of metrics_f/viterbi_s allows larger packet lengths because metrics_f is complaining for not being able to allocate large buffers. This is due to the large f.O() in this application...
dst = gr.vector_sink_s()
tb.connect(src, mod)
tb.connect(mod, isi, (add, 0))
tb.connect(noise, (add, 1))
#tb.connect (add,metrics)
#tb.connect (metrics,va,dst)
tb.connect(add, skip, va, dst)
tb.run()
data = dst.data()
ntotal = len(data) - L
nright = 0
for i in range(ntotal):
if packet[i + L] == data[i]:
nright = nright + 1
#else:
#print "Error in ", i
return (ntotal, ntotal - nright)
def __init__(self):
gr.top_block.__init__(self)
Rs = 8000
f1 = 100
f2 = 200
npts = 2048
self.qapp = QtGui.QApplication(sys.argv)
src1 = gr.sig_source_f(Rs, gr.GR_SIN_WAVE, f1, 0.1, 0)
src2 = gr.sig_source_f(Rs, gr.GR_SIN_WAVE, f2, 0.1, 0)
src = gr.add_ff()
thr = gr.throttle(gr.sizeof_float, 100*npts)
noise = gr.noise_source_f(gr.GR_GAUSSIAN, 0.001)
add = gr.add_ff()
self.snk1 = qtgui.time_sink_f(npts, Rs,
"Complex Time Example", 3)
self.connect(src1, (src,0))
self.connect(src2, (src,1))
self.connect(src, thr, (add,0))
self.connect(noise, (add,1))
self.connect(add, self.snk1)
self.connect(src1, (self.snk1, 1))
self.connect(src2, (self.snk1, 2))
self.ctrl_win = control_box()
self.ctrl_win.attach_signal1(src1)
self.ctrl_win.attach_signal2(src2)
# Get the reference pointer to the SpectrumDisplayForm QWidget
pyQt = self.snk1.pyqwidget()
# Wrap the pointer as a PyQt SIP object
# This can now be manipulated as a PyQt4.QtGui.QWidget
pyWin = sip.wrapinstance(pyQt, QtGui.QWidget)
# Example of using signal/slot to set the title of a curve
pyWin.connect(pyWin, QtCore.SIGNAL("setTitle(int, QString)"),
pyWin, QtCore.SLOT("setTitle(int, QString)"))
pyWin.emit(QtCore.SIGNAL("setTitle(int, QString)"), 0, "sum")
self.snk1.set_title(1, "src1")
self.snk1.set_title(2, "src2")
# Can also set the color of a curve
#self.snk1.set_color(5, "blue")
#pyWin.show()
self.main_box = dialog_box(pyWin, self.ctrl_win)
self.main_box.show()
def run_test(fo, fi, interleaver, Kb, bitspersymbol, K, dimensionality,
constellation, N0, seed):
tb = gr.top_block()
# TX
src = gr.lfsr_32k_source_s()
src_head = gr.head(gr.sizeof_short, Kb / 16) # packet size in shorts
s2fsmi = gr.packed_to_unpacked_ss(
bitspersymbol, gr.GR_MSB_FIRST
) # unpack shorts to symbols compatible with the outer FSM input cardinality
enc_out = trellis.encoder_ss(fo, 0) # initial state = 0
inter = trellis.permutation(interleaver.K(), interleaver.INTER(), 1,
gr.sizeof_short)
enc_in = trellis.encoder_ss(fi, 0) # initial state = 0
mod = gr.chunks_to_symbols_sf(constellation, dimensionality)
# CHANNEL
add = gr.add_ff()
noise = gr.noise_source_f(gr.GR_GAUSSIAN, math.sqrt(N0 / 2), seed)
# RX
metrics_in = trellis.metrics_f(
fi.O(), dimensionality, constellation, digital.TRELLIS_EUCLIDEAN
) # data preprocessing to generate metrics for innner Viterbi
gnd = gr.vector_source_f([0], True)
siso_in = trellis.siso_f(
fi, K, 0, -1, True, False, trellis.TRELLIS_MIN_SUM
) # Put -1 if the Initial/Final states are not set.
deinter = trellis.permutation(interleaver.K(), interleaver.DEINTER(),
fi.I(), gr.sizeof_float)
va_out = trellis.viterbi_s(
fo, K, 0, -1) # Put -1 if the Initial/Final states are not set.
fsmi2s = gr.unpacked_to_packed_ss(
bitspersymbol, gr.GR_MSB_FIRST) # pack FSM input symbols to shorts
dst = gr.check_lfsr_32k_s()
tb.connect(src, src_head, s2fsmi, enc_out, inter, enc_in, mod)
tb.connect(mod, (add, 0))
tb.connect(noise, (add, 1))
tb.connect(add, metrics_in)
tb.connect(gnd, (siso_in, 0))
tb.connect(metrics_in, (siso_in, 1))
tb.connect(siso_in, deinter, va_out, fsmi2s, dst)
tb.run()
ntotal = dst.ntotal()
nright = dst.nright()
runlength = dst.runlength()
return (ntotal, ntotal - nright)
def __init__(self, alpha=0.1, noise_mag=0):
"""
Parameters:
alpha: float
noise_mag: float
"""
gr.hier_block2.__init__(
self, "Phase Noise Generator",
gr.io_signature(1, 1, gr.sizeof_gr_complex),
gr.io_signature(1, 1, gr.sizeof_gr_complex),
)
##################################################
# Parameters
##################################################
self.alpha = alpha
self.noise_mag = noise_mag
##################################################
# Blocks
##################################################
self.gr_transcendental_0_0 = gr.transcendental("sin", "float")
self.gr_transcendental_0 = gr.transcendental("cos", "float")
self.gr_single_pole_iir_filter_xx_0 = gr.single_pole_iir_filter_ff(
alpha, 1)
self.gr_noise_source_x_0 = gr.noise_source_f(gr.GR_GAUSSIAN, noise_mag,
42)
self.gr_multiply_xx_0 = gr.multiply_vcc(1)
self.gr_float_to_complex_0 = gr.float_to_complex(1)
##################################################
# Connections
##################################################
self.connect((self.gr_float_to_complex_0, 0),
(self.gr_multiply_xx_0, 1))
self.connect((self.gr_noise_source_x_0, 0),
(self.gr_single_pole_iir_filter_xx_0, 0))
self.connect((self.gr_multiply_xx_0, 0), (self, 0))
self.connect((self, 0), (self.gr_multiply_xx_0, 0))
self.connect((self.gr_single_pole_iir_filter_xx_0, 0),
(self.gr_transcendental_0, 0))
self.connect((self.gr_single_pole_iir_filter_xx_0, 0),
(self.gr_transcendental_0_0, 0))
self.connect((self.gr_transcendental_0, 0),
(self.gr_float_to_complex_0, 0))
self.connect((self.gr_transcendental_0_0, 0),
(self.gr_float_to_complex_0, 1))
def run_test (f,Kb,bitspersymbol,K,dimensionality,constellation,N0,seed,P):
fg = gr.flow_graph ()
# TX
src = gr.lfsr_32k_source_s()
src_head = gr.head (gr.sizeof_short,Kb/16*P) # packet size in shorts
s2fsmi=gr.packed_to_unpacked_ss(bitspersymbol,gr.GR_MSB_FIRST) # unpack shorts to symbols compatible with the FSM input cardinality
s2p = gr.stream_to_streams(gr.sizeof_short,P) # serial to parallel
enc = trellis.encoder_ss(f,0) # initiali state = 0
mod = gr.chunks_to_symbols_sf(constellation,dimensionality)
# CHANNEL
add=[]
noise=[]
for i in range(P):
add.append(gr.add_ff())
noise.append(gr.noise_source_f(gr.GR_GAUSSIAN,math.sqrt(N0/2),seed))
# RX
metrics = trellis.metrics_f(f.O(),dimensionality,constellation,trellis.TRELLIS_EUCLIDEAN) # data preprocessing to generate metrics for Viterbi
va = trellis.viterbi_s(f,K,0,-1) # Put -1 if the Initial/Final states are not set.
p2s = gr.streams_to_stream(gr.sizeof_short,P) # parallel to serial
fsmi2s=gr.unpacked_to_packed_ss(bitspersymbol,gr.GR_MSB_FIRST) # pack FSM input symbols to shorts
dst = gr.check_lfsr_32k_s()
fg.connect (src,src_head,s2fsmi,s2p)
for i in range(P):
fg.connect ((s2p,i),(enc,i),(mod,i))
fg.connect ((mod,i),(add[i],0))
fg.connect (noise[i],(add[i],1))
fg.connect (add[i],(metrics,i))
fg.connect ((metrics,i),(va,i),(p2s,i))
fg.connect (p2s,fsmi2s,dst)
fg.run()
# A bit of cheating: run the program once and print the
# final encoder state.
# Then put it as the last argument in the viterbi block
#print "final state = " , enc.ST()
ntotal = dst.ntotal ()
nright = dst.nright ()
runlength = dst.runlength ()
return (ntotal,ntotal-nright)
def run_test(fo, fi, interleaver, Kb, bitspersymbol, K, dimensionality,
constellation, Es, N0, IT, seed):
tb = gr.top_block()
# TX
src = gr.lfsr_32k_source_s()
src_head = gr.head(gr.sizeof_short, Kb / 16) # packet size in shorts
s2fsmi = gr.packed_to_unpacked_ss(
bitspersymbol, gr.GR_MSB_FIRST
) # unpack shorts to symbols compatible with the outer FSM input cardinality
#src = gr.vector_source_s([0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1],False)
enc = trellis.pccc_encoder_ss(fo, 0, fi, 0, interleaver, K)
code = gr.vector_sink_s()
mod = gr.chunks_to_symbols_sf(constellation, dimensionality)
# CHANNEL
add = gr.add_ff()
noise = gr.noise_source_f(gr.GR_GAUSSIAN, math.sqrt(N0 / 2), seed)
# RX
metrics_in = trellis.metrics_f(
fi.O() * fo.O(), dimensionality, constellation,
digital.TRELLIS_EUCLIDEAN
) # data preprocessing to generate metrics for innner SISO
scale = gr.multiply_const_ff(1.0 / N0)
dec = trellis.pccc_decoder_s(fo, 0, -1, fi, 0, -1, interleaver, K, IT,
trellis.TRELLIS_MIN_SUM)
fsmi2s = gr.unpacked_to_packed_ss(
bitspersymbol, gr.GR_MSB_FIRST) # pack FSM input symbols to shorts
dst = gr.check_lfsr_32k_s()
tb.connect(src, src_head, s2fsmi, enc, mod)
#tb.connect (src,enc,mod)
#tb.connect(enc,code)
tb.connect(mod, (add, 0))
tb.connect(noise, (add, 1))
tb.connect(add, metrics_in, scale, dec, fsmi2s, dst)
tb.run()
#print code.data()
ntotal = dst.ntotal()
nright = dst.nright()
runlength = dst.runlength()
return (ntotal, ntotal - nright)
def run_test (f,Kb,bitspersymbol,K,channel,modulation,dimensionality,tot_constellation,N0,seed):
fg = gr.flow_graph ()
L = len(channel)
# TX
# this for loop is TOO slow in python!!!
packet = [0]*(K+2*L)
random.seed(seed)
for i in range(len(packet)):
packet[i] = random.randint(0, 2**bitspersymbol - 1) # random symbols
for i in range(L): # first/last L symbols set to 0
packet[i] = 0
packet[len(packet)-i-1] = 0
src = gr.vector_source_s(packet,False)
mod = gr.chunks_to_symbols_sf(modulation[1],modulation[0])
# CHANNEL
isi = gr.fir_filter_fff(1,channel)
add = gr.add_ff()
noise = gr.noise_source_f(gr.GR_GAUSSIAN,math.sqrt(N0/2),seed)
# RX
skip = gr.skiphead(gr.sizeof_float, L) # skip the first L samples since you know they are coming from the L zero symbols
#metrics = trellis.metrics_f(f.O(),dimensionality,tot_constellation,trellis.TRELLIS_EUCLIDEAN) # data preprocessing to generate metrics for Viterbi
#va = trellis.viterbi_s(f,K+L,-1,0) # Put -1 if the Initial/Final states are not set.
va = trellis.viterbi_combined_fs(f,K+L,0,0,dimensionality,tot_constellation,trellis.TRELLIS_EUCLIDEAN) # using viterbi_combined_fs instead of metrics_f/viterbi_s allows larger packet lengths because metrics_f is complaining for not being able to allocate large buffers. This is due to the large f.O() in this application...
dst = gr.vector_sink_s()
fg.connect (src,mod)
fg.connect (mod,isi,(add,0))
fg.connect (noise,(add,1))
#fg.connect (add,metrics)
#fg.connect (metrics,va,dst)
fg.connect (add,skip,va,dst)
fg.run()
data = dst.data()
ntotal = len(data) - L
nright=0
for i in range(ntotal):
if packet[i+L]==data[i]:
nright=nright+1
#else:
#print "Error in ", i
return (ntotal,ntotal-nright)
def __init__(self):
gr.top_block.__init__(self)
self.sample_rate = 10e3
s0 = gr.noise_source_f (gr.GR_UNIFORM, MAX)
s1 = foo.ct_source_f (100000)
u = foo.peak_fv (MAX, 80, "an identifier")
self.connect(s0, (u,0))
self.connect(s1, (u,1))
v = foo.wait_vv ()
self.connect(u, v)
t = gr.null_sink (4 * 1024)
self.connect(v, t)
def run_test(fo, fi, interleaver, Kb, bitspersymbol, K, channel, modulation,
dimensionality, tot_constellation, Es, N0, IT, seed):
tb = gr.top_block()
L = len(channel)
# TX
# this for loop is TOO slow in python!!!
packet = [0] * (K)
random.seed(seed)
for i in range(len(packet)):
packet[i] = random.randint(0, 2**bitspersymbol - 1) # random symbols
src = gr.vector_source_s(packet, False)
enc_out = trellis.encoder_ss(fo, 0) # initial state = 0
inter = trellis.permutation(interleaver.K(), interleaver.INTER(), 1,
gr.sizeof_short)
mod = gr.chunks_to_symbols_sf(modulation[1], modulation[0])
# CHANNEL
isi = gr.fir_filter_fff(1, channel)
add = gr.add_ff()
noise = gr.noise_source_f(gr.GR_GAUSSIAN, math.sqrt(N0 / 2), seed)
# RX
(head, tail) = make_rx(tb, fo, fi, dimensionality, tot_constellation, K,
interleaver, IT, Es, N0, trellis.TRELLIS_MIN_SUM)
dst = gr.vector_sink_s()
tb.connect(src, enc_out, inter, mod)
tb.connect(mod, isi, (add, 0))
tb.connect(noise, (add, 1))
tb.connect(add, head)
tb.connect(tail, dst)
tb.run()
data = dst.data()
ntotal = len(data)
nright = 0
for i in range(ntotal):
if packet[i] == data[i]:
nright = nright + 1
#else:
#print "Error in ", i
return (ntotal, ntotal - nright)
def run_test(fo, fi, interleaver, Kb, bitspersymbol, K, dimensionality, constellation, N0, seed):
tb = gr.top_block()
# TX
src = gr.lfsr_32k_source_s()
src_head = gr.head(gr.sizeof_short, Kb / 16) # packet size in shorts
s2fsmi = gr.packed_to_unpacked_ss(
bitspersymbol, gr.GR_MSB_FIRST
) # unpack shorts to symbols compatible with the outer FSM input cardinality
enc_out = trellis.encoder_ss(fo, 0) # initial state = 0
inter = trellis.permutation(interleaver.K(), interleaver.INTER(), 1, gr.sizeof_short)
enc_in = trellis.encoder_ss(fi, 0) # initial state = 0
mod = gr.chunks_to_symbols_sf(constellation, dimensionality)
# CHANNEL
add = gr.add_ff()
noise = gr.noise_source_f(gr.GR_GAUSSIAN, math.sqrt(N0 / 2), seed)
# RX
metrics_in = trellis.metrics_f(
fi.O(), dimensionality, constellation, trellis.TRELLIS_EUCLIDEAN
) # data preprocessing to generate metrics for innner Viterbi
va_in = trellis.viterbi_s(fi, K, 0, -1) # Put -1 if the Initial/Final states are not set.
deinter = trellis.permutation(interleaver.K(), interleaver.DEINTER(), 1, gr.sizeof_short)
metrics_out = trellis.metrics_s(
fo.O(), 1, [0, 1, 2, 3], trellis.TRELLIS_HARD_SYMBOL
) # data preprocessing to generate metrics for outer Viterbi (hard decisions)
va_out = trellis.viterbi_s(fo, K, 0, -1) # Put -1 if the Initial/Final states are not set.
fsmi2s = gr.unpacked_to_packed_ss(bitspersymbol, gr.GR_MSB_FIRST) # pack FSM input symbols to shorts
dst = gr.check_lfsr_32k_s()
tb.connect(src, src_head, s2fsmi, enc_out, inter, enc_in, mod)
tb.connect(mod, (add, 0))
tb.connect(noise, (add, 1))
tb.connect(add, metrics_in)
tb.connect(metrics_in, va_in, deinter, metrics_out, va_out, fsmi2s, dst)
tb.run()
ntotal = dst.ntotal()
nright = dst.nright()
runlength = dst.runlength()
return (ntotal, ntotal - nright)
def run_test(fo, fi, interleaver, Kb, bitspersymbol, K, dimensionality,
constellation, Es, N0, IT, seed):
tb = gr.top_block()
# TX
src = gr.lfsr_32k_source_s()
src_head = gr.head(gr.sizeof_short, Kb / 16) # packet size in shorts
s2fsmi = gr.packed_to_unpacked_ss(
bitspersymbol, gr.GR_MSB_FIRST
) # unpack shorts to symbols compatible with the outer FSM input cardinality
enc_out = trellis.encoder_ss(fo, 0) # initial state = 0
inter = trellis.permutation(interleaver.K(), interleaver.INTER(), 1,
gr.sizeof_short)
enc_in = trellis.encoder_ss(fi, 0) # initial state = 0
mod = gr.chunks_to_symbols_sf(constellation, dimensionality)
# CHANNEL
add = gr.add_ff()
noise = gr.noise_source_f(gr.GR_GAUSSIAN, math.sqrt(N0 / 2), seed)
# RX
(head, tail) = make_rx(tb, fo, fi, dimensionality, constellation, K,
interleaver, IT, Es, N0, trellis.TRELLIS_MIN_SUM)
#(head,tail) = make_rx(tb,fo,fi,dimensionality,constellation,K,interleaver,IT,Es,N0,trellis.TRELLIS_SUM_PRODUCT)
fsmi2s = gr.unpacked_to_packed_ss(
bitspersymbol, gr.GR_MSB_FIRST) # pack FSM input symbols to shorts
dst = gr.check_lfsr_32k_s()
tb.connect(src, src_head, s2fsmi, enc_out, inter, enc_in, mod)
tb.connect(mod, (add, 0))
tb.connect(noise, (add, 1))
tb.connect(add, head)
tb.connect(tail, fsmi2s, dst)
tb.run()
#print enc_out.ST(), enc_in.ST()
ntotal = dst.ntotal()
nright = dst.nright()
runlength = dst.runlength()
return (ntotal, ntotal - nright)
def __init__(self):
gr.top_block.__init__(self)
Rs = 8000
f1 = 1000
f2 = 2000
fftsize = 2048
self.qapp = QtGui.QApplication(sys.argv)
src1 = gr.sig_source_f(Rs, gr.GR_SIN_WAVE, f1, 0.1, 0)
src2 = gr.sig_source_f(Rs, gr.GR_SIN_WAVE, f2, 0.1, 0)
src = gr.add_ff()
thr = gr.throttle(gr.sizeof_float, 100*fftsize)
noise = gr.noise_source_f(gr.GR_GAUSSIAN, 0.001)
add = gr.add_ff()
self.snk1 = qtgui.sink_f(fftsize, gr.firdes.WIN_BLACKMAN_hARRIS,
0, Rs,
"Float Signal Example",
True, True, True, False)
self.connect(src1, (src,0))
self.connect(src2, (src,1))
self.connect(src, thr, (add,0))
self.connect(noise, (add,1))
self.connect(add, self.snk1)
self.ctrl_win = control_box()
self.ctrl_win.attach_signal1(src1)
self.ctrl_win.attach_signal2(src2)
# Get the reference pointer to the SpectrumDisplayForm QWidget
pyQt = self.snk1.pyqwidget()
# Wrap the pointer as a PyQt SIP object
# This can now be manipulated as a PyQt4.QtGui.QWidget
pyWin = sip.wrapinstance(pyQt, QtGui.QWidget)
self.main_box = dialog_box(pyWin, self.ctrl_win)
self.main_box.show()
def __init__(self):
gr.top_block.__init__(self)
Rs = 8000
f1 = 1000
f2 = 2000
fftsize = 2048
self.qapp = QtGui.QApplication(sys.argv)
src1 = gr.sig_source_f(Rs, gr.GR_SIN_WAVE, f1, 0.1, 0)
src2 = gr.sig_source_f(Rs, gr.GR_SIN_WAVE, f2, 0.1, 0)
src = gr.add_ff()
thr = gr.throttle(gr.sizeof_float, 100*fftsize)
noise = gr.noise_source_f(gr.GR_GAUSSIAN, 0.001)
add = gr.add_ff()
self.snk1 = qtgui.sink_f(fftsize, gr.firdes.WIN_BLACKMAN_hARRIS,
0, Rs,
"Float Signal Example",
True, True, True, False)
self.connect(src1, (src,0))
self.connect(src2, (src,1))
self.connect(src, thr, (add,0))
self.connect(noise, (add,1))
self.connect(add, self.snk1)
self.ctrl_win = control_box()
self.ctrl_win.attach_signal1(src1)
self.ctrl_win.attach_signal2(src2)
# Get the reference pointer to the SpectrumDisplayForm QWidget
pyQt = self.snk1.pyqwidget()
# Wrap the pointer as a PyQt SIP object
# This can now be manipulated as a PyQt4.QtGui.QWidget
pyWin = sip.wrapinstance(pyQt, QtGui.QWidget)
self.main_box = dialog_box(pyWin, self.ctrl_win)
self.main_box.show()
def run_test (fo,fi,interleaver,Kb,bitspersymbol,K,channel,modulation,dimensionality,tot_constellation,Es,N0,IT,seed):
tb = gr.top_block ()
L = len(channel)
# TX
# this for loop is TOO slow in python!!!
packet = [0]*(K)
random.seed(seed)
for i in range(len(packet)):
packet[i] = random.randint(0, 2**bitspersymbol - 1) # random symbols
src = gr.vector_source_s(packet,False)
enc_out = trellis.encoder_ss(fo,0) # initial state = 0
inter = trellis.permutation(interleaver.K(),interleaver.INTER(),1,gr.sizeof_short)
mod = gr.chunks_to_symbols_sf(modulation[1],modulation[0])
# CHANNEL
isi = gr.fir_filter_fff(1,channel)
add = gr.add_ff()
noise = gr.noise_source_f(gr.GR_GAUSSIAN,math.sqrt(N0/2),seed)
# RX
(head,tail) = make_rx(tb,fo,fi,dimensionality,tot_constellation,K,interleaver,IT,Es,N0,trellis.TRELLIS_MIN_SUM)
dst = gr.vector_sink_s();
tb.connect (src,enc_out,inter,mod)
tb.connect (mod,isi,(add,0))
tb.connect (noise,(add,1))
tb.connect (add,head)
tb.connect (tail,dst)
tb.run()
data = dst.data()
ntotal = len(data)
nright=0
for i in range(ntotal):
if packet[i]==data[i]:
nright=nright+1
#else:
#print "Error in ", i
return (ntotal,ntotal-nright)
def run_test(fo, fi, interleaver, Kb, bitspersymbol, K, dimensionality,
constellation, Es, N0, IT, seed):
tb = gr.top_block()
# TX
src = gr.lfsr_32k_source_s()
src_head = gr.head(gr.sizeof_short, Kb / 16) # packet size in shorts
s2fsmi = gr.packed_to_unpacked_ss(
bitspersymbol, gr.GR_MSB_FIRST
) # unpack shorts to symbols compatible with the outer FSM input cardinality
enc = trellis.sccc_encoder_ss(fo, 0, fi, 0, interleaver, K)
mod = gr.chunks_to_symbols_sf(constellation, dimensionality)
# CHANNEL
add = gr.add_ff()
noise = gr.noise_source_f(gr.GR_GAUSSIAN, math.sqrt(N0 / 2), seed)
# RX
dec = trellis.sccc_decoder_combined_fs(fo, 0, -1, fi, 0, -1, interleaver,
K, IT, trellis.TRELLIS_MIN_SUM,
dimensionality, constellation,
digital.TRELLIS_EUCLIDEAN, 1.0)
fsmi2s = gr.unpacked_to_packed_ss(
bitspersymbol, gr.GR_MSB_FIRST) # pack FSM input symbols to shorts
dst = gr.check_lfsr_32k_s()
#tb.connect (src,src_head,s2fsmi,enc_out,inter,enc_in,mod)
tb.connect(src, src_head, s2fsmi, enc, mod)
tb.connect(mod, (add, 0))
tb.connect(noise, (add, 1))
#tb.connect (add,head)
#tb.connect (tail,fsmi2s,dst)
tb.connect(add, dec, fsmi2s, dst)
tb.run()
#print enc_out.ST(), enc_in.ST()
ntotal = dst.ntotal()
nright = dst.nright()
runlength = dst.runlength()
return (ntotal, ntotal - nright)
def run_test(f, Kb, bitspersymbol, K, dimensionality, constellation, N0, seed):
tb = gr.top_block()
# TX
src = gr.lfsr_32k_source_s()
src_head = gr.head(gr.sizeof_short, Kb / 16) # packet size in shorts
s2fsmi = gr.packed_to_unpacked_ss(
bitspersymbol, gr.GR_MSB_FIRST
) # unpack shorts to symbols compatible with the FSM input cardinality
enc = trellis.encoder_ss(f, 0) # initial state = 0
mod = gr.chunks_to_symbols_sf(constellation, dimensionality)
# CHANNEL
add = gr.add_ff()
noise = gr.noise_source_f(gr.GR_GAUSSIAN, math.sqrt(N0 / 2), seed)
# RX
va = trellis.viterbi_combined_fs(
f, K, 0, -1, dimensionality, constellation, trellis.TRELLIS_EUCLIDEAN
) # Put -1 if the Initial/Final states are not set.
fsmi2s = gr.unpacked_to_packed_ss(
bitspersymbol, gr.GR_MSB_FIRST) # pack FSM input symbols to shorts
dst = gr.check_lfsr_32k_s()
tb.connect(src, src_head, s2fsmi, enc, mod)
tb.connect(mod, (add, 0))
tb.connect(noise, (add, 1))
tb.connect(add, va, fsmi2s, dst)
tb.run()
# A bit of cheating: run the program once and print the
# final encoder state..
# Then put it as the last argument in the viterbi block
#print "final state = " , enc.ST()
ntotal = dst.ntotal()
nright = dst.nright()
runlength = dst.runlength()
return (ntotal, ntotal - nright)
def run_test(fo, fi, interleaver, Kb, bitspersymbol, K, dimensionality, constellation, Es, N0, IT, seed):
tb = gr.top_block()
# TX
src = gr.lfsr_32k_source_s()
src_head = gr.head(gr.sizeof_short, Kb / 16) # packet size in shorts
s2fsmi = gr.packed_to_unpacked_ss(
bitspersymbol, gr.GR_MSB_FIRST
) # unpack shorts to symbols compatible with the outer FSM input cardinality
enc_out = trellis.encoder_ss(fo, 0) # initial state = 0
inter = trellis.permutation(interleaver.K(), interleaver.INTER(), 1, gr.sizeof_short)
enc_in = trellis.encoder_ss(fi, 0) # initial state = 0
mod = gr.chunks_to_symbols_sf(constellation, dimensionality)
# CHANNEL
add = gr.add_ff()
noise = gr.noise_source_f(gr.GR_GAUSSIAN, math.sqrt(N0 / 2), seed)
# RX
(head, tail) = make_rx(
tb, fo, fi, dimensionality, constellation, K, interleaver, IT, Es, N0, trellis.TRELLIS_MIN_SUM
)
# (head,tail) = make_rx(tb,fo,fi,dimensionality,constellation,K,interleaver,IT,Es,N0,trellis.TRELLIS_SUM_PRODUCT)
fsmi2s = gr.unpacked_to_packed_ss(bitspersymbol, gr.GR_MSB_FIRST) # pack FSM input symbols to shorts
dst = gr.check_lfsr_32k_s()
tb.connect(src, src_head, s2fsmi, enc_out, inter, enc_in, mod)
tb.connect(mod, (add, 0))
tb.connect(noise, (add, 1))
tb.connect(add, head)
tb.connect(tail, fsmi2s, dst)
tb.run()
# print enc_out.ST(), enc_in.ST()
ntotal = dst.ntotal()
nright = dst.nright()
runlength = dst.runlength()
return (ntotal, ntotal - nright)
def run_test (f,Kb,bitspersymbol,K,dimensionality,constellation,N0,seed):
tb = gr.top_block ()
# TX
src = gr.lfsr_32k_source_s()
src_head = gr.head (gr.sizeof_short,Kb/16) # packet size in shorts
s2fsmi = gr.packed_to_unpacked_ss(bitspersymbol,gr.GR_MSB_FIRST) # unpack shorts to symbols compatible with the FSM input cardinality
enc = trellis.encoder_ss(f,0) # initial state = 0
mod = gr.chunks_to_symbols_sf(constellation,dimensionality)
# CHANNEL
add = gr.add_ff()
noise = gr.noise_source_f(gr.GR_GAUSSIAN,math.sqrt(N0/2),seed)
# RX
va = trellis.viterbi_combined_fs(f,K,0,-1,dimensionality,constellation,digital.TRELLIS_EUCLIDEAN) # Put -1 if the Initial/Final states are not set.
fsmi2s = gr.unpacked_to_packed_ss(bitspersymbol,gr.GR_MSB_FIRST) # pack FSM input symbols to shorts
dst = gr.check_lfsr_32k_s();
tb.connect (src,src_head,s2fsmi,enc,mod)
tb.connect (mod,(add,0))
tb.connect (noise,(add,1))
tb.connect (add,va,fsmi2s,dst)
tb.run()
# A bit of cheating: run the program once and print the
# final encoder state..
# Then put it as the last argument in the viterbi block
#print "final state = " , enc.ST()
ntotal = dst.ntotal ()
nright = dst.nright ()
runlength = dst.runlength ()
return (ntotal,ntotal-nright)
def run_test (fo,fi,interleaver,Kb,bitspersymbol,K,dimensionality,constellation,Es,N0,IT,seed):
tb = gr.top_block ()
# TX
src = gr.lfsr_32k_source_s()
src_head = gr.head (gr.sizeof_short,Kb/16) # packet size in shorts
s2fsmi = gr.packed_to_unpacked_ss(bitspersymbol,gr.GR_MSB_FIRST) # unpack shorts to symbols compatible with the outer FSM input cardinality
#src = gr.vector_source_s([0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1],False)
enc = trellis.pccc_encoder_ss(fo,0,fi,0,interleaver,K)
code = gr.vector_sink_s()
mod = gr.chunks_to_symbols_sf(constellation,dimensionality)
# CHANNEL
add = gr.add_ff()
noise = gr.noise_source_f(gr.GR_GAUSSIAN,math.sqrt(N0/2),seed)
# RX
metrics_in = trellis.metrics_f(fi.O()*fo.O(),dimensionality,constellation,digital.TRELLIS_EUCLIDEAN) # data preprocessing to generate metrics for innner SISO
scale = gr.multiply_const_ff(1.0/N0)
dec = trellis.pccc_decoder_s(fo,0,-1,fi,0,-1,interleaver,K,IT,trellis.TRELLIS_MIN_SUM)
fsmi2s = gr.unpacked_to_packed_ss(bitspersymbol,gr.GR_MSB_FIRST) # pack FSM input symbols to shorts
dst = gr.check_lfsr_32k_s()
tb.connect (src,src_head,s2fsmi,enc,mod)
#tb.connect (src,enc,mod)
#tb.connect(enc,code)
tb.connect (mod,(add,0))
tb.connect (noise,(add,1))
tb.connect (add,metrics_in,scale,dec,fsmi2s,dst)
tb.run()
#print code.data()
ntotal = dst.ntotal ()
nright = dst.nright ()
runlength = dst.runlength ()
return (ntotal,ntotal-nright)
def __init__(self, N, fs, bw, tw, atten, D):
gr.top_block.__init__(self)
self._nsamps = N
self._fs = fs
self._bw = bw
self._tw = tw
self._at = atten
self._decim = D
taps = filter.firdes.low_pass_2(1, self._fs, self._bw, self._tw, self._at)
print "Num. Taps: ", len(taps)
self.src = gr.noise_source_f(gr.GR_GAUSSIAN, 1)
self.head = gr.head(gr.sizeof_float, self._nsamps)
self.filt0 = filter.fir_filter_fff(self._decim, taps)
self.vsnk_src = gr.vector_sink_f()
self.vsnk_out = gr.vector_sink_f()
self.connect(self.src, self.head, self.vsnk_src)
self.connect(self.head, self.filt0, self.vsnk_out)
def __init__(self, N, fs, bw, tw, atten, D):
gr.top_block.__init__(self)
self._nsamps = N
self._fs = fs
self._bw = bw
self._tw = tw
self._at = atten
self._decim = D
taps = filter.firdes.low_pass_2(1, self._fs, self._bw, self._tw,
self._at)
print "Num. Taps: ", len(taps)
self.src = gr.noise_source_f(gr.GR_GAUSSIAN, 1)
self.head = gr.head(gr.sizeof_float, self._nsamps)
self.filt0 = filter.fir_filter_fff(self._decim, taps)
self.vsnk_src = gr.vector_sink_f()
self.vsnk_out = gr.vector_sink_f()
self.connect(self.src, self.head, self.vsnk_src)
self.connect(self.head, self.filt0, self.vsnk_out)
def run_test (fo,fi,interleaver,Kb,bitspersymbol,K,dimensionality,tot_constellation,Es,N0,IT,seed):
fg = gr.flow_graph ()
# TX
src = gr.lfsr_32k_source_s()
src_head = gr.head (gr.sizeof_short,Kb/16) # packet size in shorts
s2fsmi = gr.packed_to_unpacked_ss(bitspersymbol,gr.GR_MSB_FIRST) # unpack shorts to symbols compatible with the iouter FSM input cardinality
enc_out = trellis.encoder_ss(fo,0) # initial state = 0
inter = trellis.permutation(interleaver.K(),interleaver.INTER(),1,gr.sizeof_short)
enc_in = trellis.encoder_ss(fi,0) # initial state = 0
# essentially here we implement the combination of modulation and channel as a memoryless modulation (the memory induced by the channel is hidden in the innner FSM)
mod = gr.chunks_to_symbols_sf(tot_constellation,dimensionality)
# CHANNEL
add = gr.add_ff()
noise = gr.noise_source_f(gr.GR_GAUSSIAN,math.sqrt(N0/2),seed)
# RX
(head,tail) = make_rx(fg,fo,fi,dimensionality,tot_constellation,K,interleaver,IT,Es,N0,trellis.TRELLIS_MIN_SUM)
fsmi2s = gr.unpacked_to_packed_ss(bitspersymbol,gr.GR_MSB_FIRST) # pack FSM input symbols to shorts
dst = gr.check_lfsr_32k_s();
fg.connect (src,src_head,s2fsmi,enc_out,inter,enc_in,mod)
fg.connect (mod,(add,0))
fg.connect (noise,(add,1))
fg.connect (add,head)
fg.connect (tail,fsmi2s,dst)
fg.run()
ntotal = dst.ntotal ()
nright = dst.nright ()
runlength = dst.runlength ()
#print ntotal,nright,runlength
return (ntotal,ntotal-nright)
def __init__(self):
gr.top_block.__init__(self, "Top Block")
Qt.QWidget.__init__(self)
self.setWindowTitle("Top Block")
self.setWindowIcon(Qt.QIcon.fromTheme('gnuradio-grc'))
self.top_scroll_layout = Qt.QVBoxLayout()
self.setLayout(self.top_scroll_layout)
self.top_scroll = Qt.QScrollArea()
self.top_scroll.setFrameStyle(Qt.QFrame.NoFrame)
self.top_scroll_layout.addWidget(self.top_scroll)
self.top_scroll.setWidgetResizable(True)
self.top_widget = Qt.QWidget()
self.top_scroll.setWidget(self.top_widget)
self.top_layout = Qt.QVBoxLayout(self.top_widget)
self.top_grid_layout = Qt.QGridLayout()
self.top_layout.addLayout(self.top_grid_layout)
##################################################
# Variables
##################################################
self.snr_db = snr_db = 3
self.noiseAmplitude = noiseAmplitude = sqrt(1 / (10**(0.1 * snr_db)))
self.constellation = constellation = [-1.3416, -0.4472, 0.4472, 1.3416]
self.bits = bits = 2
##################################################
# Blocks
##################################################
self.random_source_x_0 = gr.vector_source_b(
map(int, numpy.random.randint(0, 4, 10000)), True)
self.qtgui_sink_x_0_0_1 = qtgui.sink_c(
1024, #fftsize
firdes.WIN_BLACKMAN_hARRIS, #wintype
0, #fc
400, #bw
"QT GUI Plot", #name
False, #plotfreq
False, #plotwaterfall
False, #plottime
True, #plotconst
)
self.qtgui_sink_x_0_0_1.set_update_time(1.0 / 100)
self._qtgui_sink_x_0_0_1_win = sip.wrapinstance(
self.qtgui_sink_x_0_0_1.pyqwidget(), Qt.QWidget)
self.top_layout.addWidget(self._qtgui_sink_x_0_0_1_win)
self.gr_tag_debug_0 = gr.tag_debug(gr.sizeof_float * 1, "")
self.gr_noise_source_x_0 = gr.noise_source_f(gr.GR_GAUSSIAN,
noiseAmplitude, 0)
self.gr_file_sink_1 = gr.file_sink(gr.sizeof_gr_complex * 1,
"symbols.bin")
self.gr_file_sink_1.set_unbuffered(False)
self.gr_file_sink_0_0 = gr.file_sink(gr.sizeof_char * 1, "output.bin")
self.gr_file_sink_0_0.set_unbuffered(False)
self.gr_file_sink_0 = gr.file_sink(gr.sizeof_char * 1, "input.bin")
self.gr_file_sink_0.set_unbuffered(False)
self.digital_constellation_decoder_cb_0 = digital.constellation_decoder_cb(
digital.constellation_calcdist([-3, -1, 1, 3], [], 1, 1).base())
self.digital_chunks_to_symbols_xx_0 = digital.chunks_to_symbols_bf(
(constellation), 1)
self.const_source_x_0 = gr.sig_source_f(0, gr.GR_CONST_WAVE, 0, 0, 0)
self.blocks_throttle_0 = blocks.throttle(gr.sizeof_float * 1, 100)
self.blocks_float_to_complex_0 = blocks.float_to_complex(1)
self.blocks_add_xx_0 = blocks.add_vff(1)
self.blks2_error_rate_0 = grc_blks2.error_rate(
type='BER',
win_size=10000,
bits_per_symbol=bits,
)
##################################################
# Connections
##################################################
self.connect((self.random_source_x_0, 0), (self.blks2_error_rate_0, 0))
self.connect((self.random_source_x_0, 0),
(self.digital_chunks_to_symbols_xx_0, 0))
self.connect((self.gr_noise_source_x_0, 0), (self.blocks_add_xx_0, 1))
self.connect((self.digital_constellation_decoder_cb_0, 0),
(self.blks2_error_rate_0, 1))
self.connect((self.random_source_x_0, 0), (self.gr_file_sink_0, 0))
self.connect((self.digital_constellation_decoder_cb_0, 0),
(self.gr_file_sink_0_0, 0))
self.connect((self.blocks_throttle_0, 0),
(self.blocks_float_to_complex_0, 0))
self.connect((self.blocks_float_to_complex_0, 0),
(self.digital_constellation_decoder_cb_0, 0))
self.connect((self.const_source_x_0, 0),
(self.blocks_float_to_complex_0, 1))
self.connect((self.blocks_add_xx_0, 0), (self.blocks_throttle_0, 0))
self.connect((self.digital_chunks_to_symbols_xx_0, 0),
(self.blocks_add_xx_0, 0))
self.connect((self.blocks_float_to_complex_0, 0),
(self.qtgui_sink_x_0_0_1, 0))
self.connect((self.blocks_float_to_complex_0, 0),
(self.gr_file_sink_1, 0))
self.connect((self.blks2_error_rate_0, 0), (self.gr_tag_debug_0, 0))
def test_001(self):
# Just confirm that we can instantiate a noise source
op = gr.noise_source_f(gr.GR_GAUSSIAN, 10, 10)
def test_001(self):
# Just confirm that we can instantiate a noise source
op = gr.noise_source_f(gr.GR_GAUSSIAN, 10, 10)
def make_noise_gronly():
global tb
n = gr.noise_source_f(gr.GR_UNIFORM,1)
ftob = gr.binary_slicer_fb()
tb.connect(n,ftob)
return ftob
def make_noise_gronly():
global tb
n = gr.noise_source_f(gr.GR_UNIFORM, 1)
ftob = gr.binary_slicer_fb()
tb.connect(n, ftob)
return ftob
def __init__(self, frame, panel, vbox, argv, panel_info):
stdgui2.std_top_block.__init__(self, frame, panel, vbox, argv)
signal_type = default_signal_type # not yet an option
# command line options
parser = OptionParser(option_class=eng_option)
parser.add_option("-r", "--sample-rate",type="eng_float",
default=default_rate,
help="set flow graph sample rate [default=%s]" % default_rate)
parser.add_option ("-f", "--freq", type="eng_float",
default=default_freq,
help="set signal frequency [default=%s]" % default_freq)
parser.add_option ("-a", "--amplitude", type="eng_float",
default=default_ampl,
help="set signal amplitude [default %s]" % default_ampl)
parser.add_option ("--gaussian", dest="noise_type",
action="store_const", const=gr.GR_GAUSSIAN,
help="generate Gaussian noise (-x is variance)",
default=default_noise_type)
parser.add_option ("--uniform", dest="noise_type",
action="store_const", const=gr.GR_UNIFORM,
help="generate Uniform noise (-x is peak-to-peak)")
parser.add_option ("-x", "--noise_amplitude", type="eng_float",
default=default_noise_ampl,
help="set noise amplitude (variance or p-p) [default %s]" % default_noise_ampl)
parser.add_option("-n", "--frame_decim", type="int",
default=default_frame_decim,
help="set oscope frame decimation factor to DECIM [default=%s]" % default_frame_decim)
parser.add_option("-v", "--v_scale", type="eng_float",
default=default_v_scale,
help="set oscope initial V/div [default=%s]" % default_v_scale)
parser.add_option("-t", "--t_scale", type="eng_float",
default=default_t_scale,
help="set oscope initial s/div [default=%s]" % default_t_scale)
parser.add_option("-l", "--ref_level", type="eng_float",
default=default_ref_level,
help="set fft reference level [default=%sdB]" % default_ref_level)
(options, args) = parser.parse_args ()
if len(args) != 0:
parser.print_help()
raise SystemExit, 1
# flow graph, including scope and fft frames
sample_rate = int(options.sample_rate)
# signal, noise sources need to be attributes so callbacks can get them
self.signal = gr.sig_source_f(sample_rate, gr.GR_SIN_WAVE, options.freq,
options.amplitude, 0.0)
# Seed copied from example at cswiger/noise_source.html
self.noise = gr.noise_source_f(options.noise_type,
options.noise_amplitude,
2482)
add = gr.add_ff()
throttle = gr.throttle(gr.sizeof_float, sample_rate)
(scope_title, scope_panel, scope_vbox) = panel_info[1] #0 is ctrl panel
scope = scopesink2.scope_sink_f(scope_panel,
sample_rate=sample_rate,
frame_decim=options.frame_decim,
v_scale=options.v_scale,
t_scale=options.t_scale)
scope_vbox.Add (scope.win, 1, wx.EXPAND)
fft_size = 256
(fft_title, fft_panel, fft_vbox) = panel_info[2] # 0 is control panel
fft = fftsink2.fft_sink_f (fft_panel,title=fft_title,
fft_size=fft_size*2,
sample_rate=sample_rate, baseband_freq=0,
ref_level=options.ref_level,
y_per_div=10)
fft_vbox.Add (fft.win, 1, wx.EXPAND)
self.connect (self.signal, (add,0))
self.connect (self.noise, (add,1))
self.connect (add, throttle)
# self.connect (throttle, (scope, 0))
# self.connect (throttle, (scope, 1)) # can't leave scope in unconnected
self.connect (throttle, scope)
self.connect (throttle, fft)
# control panel frame
sliders = form.form()
vbox.Add((0,10), 0)
hbox = wx.BoxSizer(wx.HORIZONTAL)
hbox.Add((10,0), 0)
sliders['signal_type'] = form.static_text_field(parent=panel,sizer=hbox)
vbox.Add(hbox, 0, wx.EXPAND)
vbox.Add((0,10), 0)
hbox = wx.BoxSizer(wx.HORIZONTAL)
hbox.Add((10,0), 0)
sliders['signal_freq'] = form.slider_field(parent=panel, sizer=hbox,
label="Frequency",
weight=3,
min=0, max=2*options.freq,
callback=self.set_signal_freq)
vbox.Add(hbox, 0, wx.EXPAND)
vbox.Add((0,10), 0)
hbox = wx.BoxSizer(wx.HORIZONTAL) # apparently you can rebind hbox
hbox.Add((10,0), 0)
sliders['signal_ampl'] = form.slider_field(parent=panel, sizer=hbox,
label="Amplitude",
weight=3,
min=0, max=usrp_full_scale,
callback=self.set_signal_ampl)
vbox.Add(hbox, 0, wx.EXPAND)
vbox.Add((0,20), 0)
hbox = wx.BoxSizer(wx.HORIZONTAL)
hbox.Add((10,0), 0)
sliders['noise_type'] = form.static_text_field(parent=panel,sizer=hbox)
vbox.Add(hbox, 0, wx.EXPAND)
vbox.Add((0,10), 0)
hbox = wx.BoxSizer(wx.HORIZONTAL)
hbox.Add((10,0), 0)
sliders['noise_ampl'] = form.slider_field(parent=panel, sizer=hbox,
label="Amplitude",
weight=3,
min=0, max=usrp_full_scale,
callback=self.set_noise_ampl)
vbox.Add(hbox, 0, wx.EXPAND)
vbox.Add((0,10), 0)
sliders['signal_type'].set_value("Signal: %s"
% signal_string[signal_type])
sliders['noise_type'].set_value("Noise: %s"
% noise_string[options.noise_type])
sliders['signal_freq'].set_value(options.freq)
sliders['signal_ampl'].set_value(options.amplitude)
sliders['noise_ampl'].set_value(options.noise_amplitude)