def main():
N = 10000
fs = 2000.0
Ts = 1.0/fs
t = scipy.arange(0, N*Ts, Ts)
# When playing with the number of channels, be careful about the filter
# specs and the channel map of the synthesizer set below.
nchans = 10
# Build the filter(s)
bw = 1000
tb = 400
proto_taps = filter.firdes.low_pass_2(1, nchans*fs,
bw, tb, 80,
filter.firdes.WIN_BLACKMAN_hARRIS)
print "Filter length: ", len(proto_taps)
# Create a modulated signal
npwr = 0.01
data = scipy.random.randint(0, 256, N)
rrc_taps = filter.firdes.root_raised_cosine(1, 2, 1, 0.35, 41)
src = gr.vector_source_b(data.astype(scipy.uint8).tolist(), False)
mod = digital.bpsk_mod(samples_per_symbol=2)
chan = filter.channel_model(npwr)
rrc = filter.fft_filter_ccc(1, rrc_taps)
# Split it up into pieces
channelizer = filter.pfb.channelizer_ccf(nchans, proto_taps, 2)
# Put the pieces back together again
syn_taps = [nchans*t for t in proto_taps]
synthesizer = filter.pfb_synthesizer_ccf(nchans, syn_taps, True)
src_snk = gr.vector_sink_c()
snk = gr.vector_sink_c()
# Remap the location of the channels
# Can be done in synth or channelizer (watch out for rotattions in
# the channelizer)
synthesizer.set_channel_map([ 0, 1, 2, 3, 4,
15, 16, 17, 18, 19])
tb = gr.top_block()
tb.connect(src, mod, chan, rrc, channelizer)
tb.connect(rrc, src_snk)
vsnk = []
for i in xrange(nchans):
tb.connect((channelizer,i), (synthesizer, i))
vsnk.append(gr.vector_sink_c())
tb.connect((channelizer,i), vsnk[i])
tb.connect(synthesizer, snk)
tb.run()
sin = scipy.array(src_snk.data()[1000:])
sout = scipy.array(snk.data()[1000:])
# Plot original signal
fs_in = nchans*fs
f1 = pylab.figure(1, figsize=(16,12), facecolor='w')
s11 = f1.add_subplot(2,2,1)
s11.psd(sin, NFFT=fftlen, Fs=fs_in)
s11.set_title("PSD of Original Signal")
s11.set_ylim([-200, -20])
s12 = f1.add_subplot(2,2,2)
s12.plot(sin.real[1000:1500], "o-b")
s12.plot(sin.imag[1000:1500], "o-r")
s12.set_title("Original Signal in Time")
start = 1
skip = 4
s13 = f1.add_subplot(2,2,3)
s13.plot(sin.real[start::skip], sin.imag[start::skip], "o")
s13.set_title("Constellation")
s13.set_xlim([-2, 2])
s13.set_ylim([-2, 2])
# Plot channels
nrows = int(scipy.sqrt(nchans))
ncols = int(scipy.ceil(float(nchans)/float(nrows)))
f2 = pylab.figure(2, figsize=(16,12), facecolor='w')
for n in xrange(nchans):
s = f2.add_subplot(nrows, ncols, n+1)
s.psd(vsnk[n].data(), NFFT=fftlen, Fs=fs_in)
s.set_title("Channel {0}".format(n))
s.set_ylim([-200, -20])
# Plot reconstructed signal
fs_out = 2*nchans*fs
f3 = pylab.figure(3, figsize=(16,12), facecolor='w')
s31 = f3.add_subplot(2,2,1)
s31.psd(sout, NFFT=fftlen, Fs=fs_out)
s31.set_title("PSD of Reconstructed Signal")
s31.set_ylim([-200, -20])
s32 = f3.add_subplot(2,2,2)
s32.plot(sout.real[1000:1500], "o-b")
s32.plot(sout.imag[1000:1500], "o-r")
s32.set_title("Reconstructed Signal in Time")
start = 2
skip = 4
s33 = f3.add_subplot(2,2,3)
s33.plot(sout.real[start::skip], sout.imag[start::skip], "o")
s33.set_title("Constellation")
s33.set_xlim([-2, 2])
s33.set_ylim([-2, 2])
pylab.show()
def main():
N = 10000
fs = 2000.0
Ts = 1.0 / fs
t = scipy.arange(0, N*Ts, Ts)
# When playing with the number of channels, be careful about the filter
# specs and the channel map of the synthesizer set below.
nchans = 10
# Build the filter(s)
bw = 1000
tb = 400
proto_taps = filter.firdes.low_pass_2(1, nchans*fs,
bw, tb, 80,
filter.firdes.WIN_BLACKMAN_hARRIS)
print("Filter length: ", len(proto_taps))
# Create a modulated signal
npwr = 0.01
data = scipy.random.randint(0, 256, N)
rrc_taps = filter.firdes.root_raised_cosine(1, 2, 1, 0.35, 41)
src = blocks.vector_source_b(data.astype(scipy.uint8).tolist(), False)
mod = digital.bpsk_mod(samples_per_symbol=2)
chan = channels.channel_model(npwr)
rrc = filter.fft_filter_ccc(1, rrc_taps)
# Split it up into pieces
channelizer = filter.pfb.channelizer_ccf(nchans, proto_taps, 2)
# Put the pieces back together again
syn_taps = [nchans*t for t in proto_taps]
synthesizer = filter.pfb_synthesizer_ccf(nchans, syn_taps, True)
src_snk = blocks.vector_sink_c()
snk = blocks.vector_sink_c()
# Remap the location of the channels
# Can be done in synth or channelizer (watch out for rotattions in
# the channelizer)
synthesizer.set_channel_map([ 0, 1, 2, 3, 4,
15, 16, 17, 18, 19])
tb = gr.top_block()
tb.connect(src, mod, chan, rrc, channelizer)
tb.connect(rrc, src_snk)
vsnk = []
for i in range(nchans):
tb.connect((channelizer,i), (synthesizer, i))
vsnk.append(blocks.vector_sink_c())
tb.connect((channelizer,i), vsnk[i])
tb.connect(synthesizer, snk)
tb.run()
sin = scipy.array(src_snk.data()[1000:])
sout = scipy.array(snk.data()[1000:])
# Plot original signal
fs_in = nchans*fs
f1 = pylab.figure(1, figsize=(16,12), facecolor='w')
s11 = f1.add_subplot(2,2,1)
s11.psd(sin, NFFT=fftlen, Fs=fs_in)
s11.set_title("PSD of Original Signal")
s11.set_ylim([-200, -20])
s12 = f1.add_subplot(2,2,2)
s12.plot(sin.real[1000:1500], "o-b")
s12.plot(sin.imag[1000:1500], "o-r")
s12.set_title("Original Signal in Time")
start = 1
skip = 2
s13 = f1.add_subplot(2,2,3)
s13.plot(sin.real[start::skip], sin.imag[start::skip], "o")
s13.set_title("Constellation")
s13.set_xlim([-2, 2])
s13.set_ylim([-2, 2])
# Plot channels
nrows = int(scipy.sqrt(nchans))
ncols = int(scipy.ceil(float(nchans) / float(nrows)))
f2 = pylab.figure(2, figsize=(16,12), facecolor='w')
for n in range(nchans):
s = f2.add_subplot(nrows, ncols, n+1)
s.psd(vsnk[n].data(), NFFT=fftlen, Fs=fs_in)
s.set_title("Channel {0}".format(n))
s.set_ylim([-200, -20])
# Plot reconstructed signal
fs_out = 2*nchans*fs
f3 = pylab.figure(3, figsize=(16,12), facecolor='w')
s31 = f3.add_subplot(2,2,1)
s31.psd(sout, NFFT=fftlen, Fs=fs_out)
s31.set_title("PSD of Reconstructed Signal")
s31.set_ylim([-200, -20])
s32 = f3.add_subplot(2,2,2)
s32.plot(sout.real[1000:1500], "o-b")
s32.plot(sout.imag[1000:1500], "o-r")
s32.set_title("Reconstructed Signal in Time")
start = 0
skip = 4
s33 = f3.add_subplot(2,2,3)
s33.plot(sout.real[start::skip], sout.imag[start::skip], "o")
s33.set_title("Constellation")
s33.set_xlim([-2, 2])
s33.set_ylim([-2, 2])
pylab.show()
import scipy
from gnuradio import gr, digital
import cspl
N = 10e5
ntag = 10e1
gr_est = digital.SNR_EST_M2M4
bits = tuple(range(0,255))*100
SNR = 10.0**(10/10.0) # 5dB
scale = scipy.sqrt(SNR)
src = gr.vector_source_b(bits, False)
mod = digital.bpsk_mod()
chn = gr.channel_model(1.0/scale)
snr = digital.mpsk_snr_est_cc(gr_est)
demod = digital.bpsk_demod()
snk = cspl.snr_file_sink_b("data.foo")
tb = gr.top_block()
tb.connect(src, mod, chn, snr, demod, snk)
tb.run()
