def test_003_multiburst(self):
""" Send several bursts, see if the number of detects is correct.
Burst lengths and content are random.
"""
n_bursts = 42
fft_len = 32
cp_len = 4
tx_signal = []
for i in xrange(n_bursts):
sync_symbol = [(random.randint(0, 1) * 2) - 1
for x in range(fft_len / 2)] * 2
tx_signal += [0,] * random.randint(0, 2*fft_len) + \
sync_symbol[-cp_len:] + \
sync_symbol + \
[(random.randint(0, 1)*2)-1 for x in range(fft_len * random.randint(5,23))]
add = blocks.add_cc()
sync = digital.ofdm_sync_sc_cfb(fft_len, cp_len)
sink_freq = blocks.vector_sink_f()
sink_detect = blocks.vector_sink_b()
channel = channels.channel_model(0.005)
self.tb.connect(blocks.vector_source_c(tx_signal), channel, sync)
self.tb.connect((sync, 0), sink_freq)
self.tb.connect((sync, 1), sink_detect)
self.tb.run()
n_bursts_detected = numpy.sum(sink_detect.data())
# We allow for one false alarm or missed burst
self.assertTrue(
abs(n_bursts_detected - n_bursts) <= 1,
msg="""Because of statistics, it is possible (though unlikely)
that the number of detected bursts differs slightly. If the number of detects is
off by one or two, run the test again and see what happen.
Detection error was: %d """ % (numpy.sum(sink_detect.data()) - n_bursts))
def test_004_ofdm_packets (self):
"""
Send several bursts using ofdm_tx, see if the number of detects is correct.
Burst lengths and content are random.
"""
n_bursts = 42
fft_len = 64
cp_len = 16
# Here, coarse freq offset is allowed
max_freq_offset = 2*numpy.pi/fft_len * 4
freq_offset = ((2 * random.random()) - 1) * max_freq_offset
tx_signal = []
packets = []
tagname = "packet_length"
min_packet_length = 10
max_packet_length = 50
sync_sequence = [random.randint(0, 1)*2-1 for x in range(fft_len/2)]
for i in xrange(n_bursts):
packet_length = random.randint(min_packet_length,
max_packet_length+1)
packet = [random.randint(0, 255) for i in range(packet_length)]
packets.append(packet)
data, tags = tagged_streams.packets_to_vectors(packets, tagname, vlen=1)
total_length = len(data)
src = blocks.vector_source_b(data, False, 1, tags)
mod = ofdm_tx(packet_length_tag_key=tagname)
sync = digital.ofdm_sync_sc_cfb(fft_len, cp_len)
sink_freq = blocks.vector_sink_f()
sink_detect = blocks.vector_sink_b()
noise_level = 0.005
channel = channels.channel_model(noise_level, freq_offset / 2 / numpy.pi)
self.tb.connect(src, mod, channel, sync, sink_freq)
self.tb.connect((sync, 1), sink_detect)
self.tb.run()
self.assertEqual(numpy.sum(sink_detect.data()), n_bursts)
def test_003_multiburst (self):
""" Send several bursts, see if the number of detects is correct.
Burst lengths and content are random.
"""
n_bursts = 42
fft_len = 32
cp_len = 4
tx_signal = []
for i in xrange(n_bursts):
sync_symbol = [(random.randint(0, 1)*2)-1 for x in range(fft_len/2)] * 2
tx_signal += [0,] * random.randint(0, 2*fft_len) + \
sync_symbol[-cp_len:] + \
sync_symbol + \
[(random.randint(0, 1)*2)-1 for x in range(fft_len * random.randint(5,23))]
add = blocks.add_cc()
sync = digital.ofdm_sync_sc_cfb(fft_len, cp_len)
sink_freq = blocks.vector_sink_f()
sink_detect = blocks.vector_sink_b()
channel = channels.channel_model(0.005)
self.tb.connect(blocks.vector_source_c(tx_signal), channel, sync)
self.tb.connect((sync, 0), sink_freq)
self.tb.connect((sync, 1), sink_detect)
self.tb.run()
n_bursts_detected = numpy.sum(sink_detect.data())
# We allow for one false alarm or missed burst
self.assertTrue(abs(n_bursts_detected - n_bursts) <= 1,
msg="""Because of statistics, it is possible (though unlikely)
that the number of detected bursts differs slightly. If the number of detects is
off by one or two, run the test again and see what happen.
Detection error was: %d """ % (numpy.sum(sink_detect.data()) - n_bursts)
)
def test_000(self):
N = 1000 # number of samples to use
fs = 1000 # baseband sampling rate
freq = 100
signal = analog.sig_source_c(fs, analog.GR_SIN_WAVE, freq, 1)
head = blocks.head(gr.sizeof_gr_complex, N)
op = channels.channel_model(0.0, 0.0, 1.0, [
1,
], 0)
snk = blocks.vector_sink_c()
snk1 = blocks.vector_sink_c()
op.set_noise_voltage(0.0)
op.set_frequency_offset(0.0)
op.set_taps([
1,
])
op.set_timing_offset(1.0)
self.tb.connect(signal, head, op, snk)
self.tb.connect(op, snk1)
self.tb.run()
dst_data = snk.data()
exp_data = snk1.data()
self.assertComplexTuplesAlmostEqual(exp_data, dst_data, 5)
def test_002_rx_only_noise(self):
""" Run the RX with only noise, check it doesn't crash
or return a burst. """
len_tag_key = 'frame_len'
samples = (0,) * 1000
channel = channels.channel_model(0.1)
rx_fg = ofdm_rx_fg(samples, len_tag_key, channel)
rx_fg.run()
self.assertEqual(len(rx_fg.get_rx_bytes()), 0)
def test_004_tx1packet_large_fO(self):
""" Transmit one packet, with slight AWGN and large frequency offset.
Check packet is received and no bit errors have occurred. """
fft_len = 64
len_tag_key = 'frame_len'
n_bytes = 21
test_data = tuple([random.randint(0, 255) for x in range(n_bytes)])
#test_data = tuple([255 for x in range(n_bytes)])
# 1.0/fft_len is one sub-carrier
frequency_offset = 1.0 / fft_len * 2.5
channel = channels.channel_model(0.00001, frequency_offset)
# Tx
tx_fg = ofdm_tx_fg(test_data, len_tag_key)
tx_fg.run()
tx_samples = tx_fg.get_tx_samples()
# Rx
rx_fg = ofdm_rx_fg(tx_samples, len_tag_key, channel, prepend_zeros=100)
rx_fg.run()
rx_data = rx_fg.get_rx_bytes()
self.assertEqual(test_data, rx_data)
def test_004_ofdm_packets(self):
"""
Send several bursts using ofdm_tx, see if the number of detects is correct.
Burst lengths and content are random.
"""
n_bursts = 42
fft_len = 64
cp_len = 16
# Here, coarse freq offset is allowed
max_freq_offset = 2 * numpy.pi / fft_len * 4
freq_offset = ((2 * random.random()) - 1) * max_freq_offset
tx_signal = []
packets = []
tagname = "packet_length"
min_packet_length = 10
max_packet_length = 50
sync_sequence = [
random.randint(0, 1) * 2 - 1 for x in range(fft_len / 2)
]
for i in xrange(n_bursts):
packet_length = random.randint(min_packet_length,
max_packet_length + 1)
packet = [random.randint(0, 255) for i in range(packet_length)]
packets.append(packet)
data, tags = tagged_streams.packets_to_vectors(packets,
tagname,
vlen=1)
total_length = len(data)
src = blocks.vector_source_b(data, False, 1, tags)
mod = ofdm_tx(packet_length_tag_key=tagname)
sync = digital.ofdm_sync_sc_cfb(fft_len, cp_len)
sink_freq = blocks.vector_sink_f()
sink_detect = blocks.vector_sink_b()
noise_level = 0.005
channel = channels.channel_model(noise_level,
freq_offset / 2 / numpy.pi)
self.tb.connect(src, mod, channel, sync, sink_freq)
self.tb.connect((sync, 1), sink_detect)
self.tb.run()
self.assertEqual(numpy.sum(sink_detect.data()), n_bursts)
def test_002_freq (self):
""" Add a fine frequency offset and see if that get's detected properly """
fft_len = 32
cp_len = 4
# This frequency offset is normalized to rads, i.e. \pi == f_s/2
max_freq_offset = 2*numpy.pi/fft_len # Otherwise, it's coarse
freq_offset = ((2 * random.random()) - 1) * max_freq_offset
sig_len = (fft_len + cp_len) * 10
sync_symbol = [(random.randint(0, 1)*2)-1 for x in range(fft_len/2)] * 2
tx_signal = sync_symbol[-cp_len:] + \
sync_symbol + \
[(random.randint(0, 1)*2)-1 for x in range(sig_len)]
sync = digital.ofdm_sync_sc_cfb(fft_len, cp_len, True)
sink_freq = blocks.vector_sink_f()
sink_detect = blocks.vector_sink_b()
channel = channels.channel_model(0.005, freq_offset / 2.0 / numpy.pi)
self.tb.connect(blocks.vector_source_c(tx_signal), channel, sync)
self.tb.connect((sync, 0), sink_freq)
self.tb.connect((sync, 1), sink_detect)
self.tb.run()
phi_hat = sink_freq.data()[sink_detect.data().index(1)]
est_freq_offset = 2 * phi_hat / fft_len
self.assertAlmostEqual(est_freq_offset, freq_offset, places=2)
def test_000(self):
N = 1000 # number of samples to use
fs = 1000 # baseband sampling rate
freq = 100
signal = analog.sig_source_c(fs, analog.GR_SIN_WAVE, freq, 1)
head = blocks.head(gr.sizeof_gr_complex, N)
op = channels.channel_model(0.0, 0.0, 1.0, [1,], 0)
snk = blocks.vector_sink_c()
snk1 = blocks.vector_sink_c()
op.set_noise_voltage(0.0)
op.set_frequency_offset(0.0)
op.set_taps([1,])
op.set_timing_offset(1.0)
self.tb.connect(signal, head, op, snk)
self.tb.connect(op, snk1)
self.tb.run()
dst_data = snk.data()
exp_data = snk1.data()
self.assertComplexTuplesAlmostEqual(exp_data, dst_data, 5)
def test_002_freq(self):
""" Add a fine frequency offset and see if that get's detected properly """
fft_len = 32
cp_len = 4
# This frequency offset is normalized to rads, i.e. \pi == f_s/2
max_freq_offset = 2 * numpy.pi / fft_len # Otherwise, it's coarse
freq_offset = ((2 * random.random()) - 1) * max_freq_offset
sig_len = (fft_len + cp_len) * 10
sync_symbol = [(random.randint(0, 1) * 2) - 1
for x in range(fft_len / 2)] * 2
tx_signal = sync_symbol[-cp_len:] + \
sync_symbol + \
[(random.randint(0, 1)*2)-1 for x in range(sig_len)]
sync = digital.ofdm_sync_sc_cfb(fft_len, cp_len, True)
sink_freq = blocks.vector_sink_f()
sink_detect = blocks.vector_sink_b()
channel = channels.channel_model(0.005, freq_offset / 2.0 / numpy.pi)
self.tb.connect(blocks.vector_source_c(tx_signal), channel, sync)
self.tb.connect((sync, 0), sink_freq)
self.tb.connect((sync, 1), sink_detect)
self.tb.run()
phi_hat = sink_freq.data()[sink_detect.data().index(1)]
est_freq_offset = 2 * phi_hat / fft_len
self.assertAlmostEqual(est_freq_offset, freq_offset, places=2)
