def test_003_small_frame_mod(self):
num_frames = 300
total_subcarriers = 8
used_subcarriers = 4
channel_map = np.array(
(0, 0, 1, 1, 1, 1, 0,
0)) #ft.get_channel_map(used_subcarriers, total_subcarriers)
payload_symbols = 8
overlap = 4
taps = ft.generate_phydyas_filter(total_subcarriers, overlap)
preamble = ft.get_preamble(total_subcarriers)
num_preamble_symbols = len(preamble) // total_subcarriers
payload = ft.get_payload(payload_symbols, used_subcarriers)
payload = np.tile(payload, num_frames).flatten()
src = blocks.vector_source_b(payload, repeat=False)
framer = fbmc.frame_generator_bvc(used_subcarriers, total_subcarriers,
payload_symbols, overlap,
channel_map, preamble)
snk_frame = blocks.vector_sink_c(total_subcarriers) # a debug output
mod = fbmc.tx_sdft_vcc(taps, total_subcarriers)
demod = fbmc.rx_sdft_cvc(taps, total_subcarriers)
skipper = blocks.skiphead(8 * total_subcarriers, 4)
snk_rx = blocks.vector_sink_c(total_subcarriers)
deframer = fbmc.deframer_vcb(used_subcarriers, total_subcarriers,
num_preamble_symbols, payload_symbols,
overlap, channel_map)
snk = blocks.vector_sink_b(1)
self.tb.connect(src, framer, mod, demod, skipper, deframer, snk)
self.tb.connect(framer, snk_frame)
self.tb.connect(skipper, snk_rx)
self.tb.run()
res = np.array(snk.data())
print "len(res) = ", len(res), ", len(payload) = ", len(payload)
print "ref: ", payload
print "res: ", res
moddata = np.array(snk_frame.data())
print "len(moddata) = ", len(moddata)
rxdata = np.array(snk_rx.data())
print "len(rxdata) = ", len(
rxdata), " diff: ", len(moddata) - len(rxdata)
# plt.plot(rxdata.real * 0.03)
# for i in range(len(moddata)):
# if (i + 1) % total_subcarriers == 0:
# plt.axvline(i)
# plt.plot(moddata.real)
# plt.grid()
# plt.show()
print "len(payload) = ", len(payload)
print "len(result ) = ", len(res)
self.assertTupleEqual(tuple(payload[:len(res)]), tuple(res))
def test_004_config_frame_mod(self):
num_frames = 10
cfg = fbmc.fbmc_config(num_used_subcarriers=20, num_payload_sym=16, num_overlap_sym=4, modulation="QPSK",
preamble="IAM", samp_rate=250000)
total_subcarriers = cfg.num_total_subcarriers() # 8
used_subcarriers = cfg.num_used_subcarriers() # 4
channel_map = cfg.channel_map() # ft.get_channel_map(used_subcarriers, total_subcarriers)
payload_symbols = cfg.num_payload_sym() # 8
overlap = cfg.num_overlap_sym() # 4
taps = cfg.phydyas_impulse_taps(cfg.num_total_subcarriers(), cfg.num_overlap_sym()) # ft.generate_phydyas_filter(total_subcarriers, overlap)
preamble = ft.get_preamble(total_subcarriers)
num_preamble_symbols = len(preamble) // total_subcarriers
payload = ft.get_payload(payload_symbols, used_subcarriers)
payload = np.tile(payload, num_frames).flatten()
src = blocks.vector_source_b(payload, repeat=False)
framer = fbmc.frame_generator_bvc(used_subcarriers, total_subcarriers, payload_symbols, overlap, channel_map, preamble)
snk_frame = blocks.vector_sink_c(total_subcarriers) # a debug output
mod = fbmc.tx_sdft_vcc(taps, total_subcarriers)
demod = fbmc.rx_sdft_cvc(taps, total_subcarriers)
skipper = blocks.skiphead(8 * total_subcarriers, 4)
snk_rx = blocks.vector_sink_c(total_subcarriers)
deframer = fbmc.deframer_vcb(used_subcarriers, total_subcarriers, num_preamble_symbols, payload_symbols, overlap, channel_map)
snk = blocks.vector_sink_b(1)
self.tb.connect(src, framer, mod, demod, skipper, deframer, snk)
self.tb.connect(framer, snk_frame)
self.tb.connect(skipper, snk_rx)
self.tb.run()
res = np.array(snk.data())
print res
print payload
moddata = np.array(snk_frame.data())
print "len(moddata) = ", len(moddata)
rxdata = np.array(snk_rx.data())
print "len(rxdata) = ", len(rxdata)
# plt.plot(rxdata.real * 0.03)
# for i in range(len(moddata)):
# if (i + 1) % total_subcarriers == 0:
# plt.axvline(i)
# plt.plot(moddata.real)
# plt.grid()
# plt.show()
print "len(payload) = ", len(payload)
print "len(result) = ", len(res)
self.assertTupleEqual(tuple(payload[:len(res)]), tuple(res))
def test_003_small_frame_mod(self):
num_frames = 300
total_subcarriers = 8
used_subcarriers = 4
channel_map = np.array((0, 0, 1, 1, 1, 1, 0, 0)) #ft.get_channel_map(used_subcarriers, total_subcarriers)
payload_symbols = 8
overlap = 4
taps = ft.generate_phydyas_filter(total_subcarriers, overlap)
preamble = ft.get_preamble(total_subcarriers)
num_preamble_symbols = len(preamble) // total_subcarriers
payload = ft.get_payload(payload_symbols, used_subcarriers)
payload = np.tile(payload, num_frames).flatten()
src = blocks.vector_source_b(payload, repeat=False)
framer = fbmc.frame_generator_bvc(used_subcarriers, total_subcarriers, payload_symbols, overlap, channel_map, preamble)
snk_frame = blocks.vector_sink_c(total_subcarriers) # a debug output
mod = fbmc.tx_sdft_vcc(taps, total_subcarriers)
demod = fbmc.rx_sdft_cvc(taps, total_subcarriers)
skipper = blocks.skiphead(8 * total_subcarriers, 4)
snk_rx = blocks.vector_sink_c(total_subcarriers)
deframer = fbmc.deframer_vcb(used_subcarriers, total_subcarriers, num_preamble_symbols, payload_symbols, overlap, channel_map)
snk = blocks.vector_sink_b(1)
self.tb.connect(src, framer, mod, demod, skipper, deframer, snk)
self.tb.connect(framer, snk_frame)
self.tb.connect(skipper, snk_rx)
self.tb.run()
res = np.array(snk.data())
print "len(res) = ", len(res), ", len(payload) = ", len(payload)
print "ref: ", payload
print "res: ", res
moddata = np.array(snk_frame.data())
print "len(moddata) = ", len(moddata)
rxdata = np.array(snk_rx.data())
print "len(rxdata) = ", len(rxdata), " diff: ", len(moddata) - len(rxdata)
# plt.plot(rxdata.real * 0.03)
# for i in range(len(moddata)):
# if (i + 1) % total_subcarriers == 0:
# plt.axvline(i)
# plt.plot(moddata.real)
# plt.grid()
# plt.show()
print "len(payload) = ", len(payload)
print "len(result ) = ", len(res)
self.assertTupleEqual(tuple(payload[:len(res)]), tuple(res))
def test_001_setUp(self):
print "test_001_setUp"
# test if flowgraph is set up as expected!
# does not throw any runtime errors unexpectedly!
overlap = 4
L = 4
taps = np.ones(overlap * L, dtype=float)
taps = np.append(taps, [0.0, ])
phydyas = fbmc.rx_sdft_cvc(taps, L)
print "overlap: ", phydyas.overlap()
print "L: ", phydyas.L()
assert phydyas.overlap() == overlap
assert phydyas.L() == L
def test_001_setUp(self):
print "test_001_setUp"
# test if flowgraph is set up as expected!
# does not throw any runtime errors unexpectedly!
overlap = 4
L = 4
taps = np.ones(overlap * L, dtype=float)
taps = np.append(taps, [
0.0,
])
phydyas = fbmc.rx_sdft_cvc(taps, L)
print "overlap: ", phydyas.overlap()
print "L: ", phydyas.L()
assert phydyas.overlap() == overlap
assert phydyas.L() == L
def test_005_legacy(self):
print "\ntest_005_legacy"
# test if flowgraph is set up as expected!
multiple = 9
overlap = 4
L = 32
taps = ft.generate_phydyas_filter(L, overlap)
data = np.arange(1, multiple * L // 2 + 1 + 1, dtype=np.complex)
# instatiated blocks and flowgraph
phydyas = fbmc.rx_sdft_cvc(taps, L)
pfb = fbmc.rx_polyphase_cvc(taps, L)
print "phydyas: L = ", phydyas.L(), ", overlap = ", phydyas.overlap()
src = blocks.vector_source_c(data, vlen=1)
snk0 = blocks.vector_sink_c(L)
snk1 = blocks.vector_sink_c(L)
tb = gr.top_block()
tb.connect(src, phydyas, snk0)
tb.connect(src, pfb, snk1)
# run fg and get results
tb.run()
res0 = np.array(snk0.data())
res1 = np.array(snk1.data())
ref = self.get_reference_output(data, taps, L, overlap, multiple)
ftref = ft.rx(data[:-L // 2], taps, L)
print "\nFBMC blocks"
print ftref
lcut = 2
rcut = 5
print "\nGR blocks"
print res0[lcut * L:-rcut * L].reshape((-1, L)).T
print "\nIOTA blocks"
print res1[8 * L:].reshape((-1, L)).T
ftref = ftref.T
ftref = ftref.flatten()
self.assertComplexTuplesAlmostEqual(ftref, res0[lcut * L:-rcut * L], 3)
self.assertComplexTuplesAlmostEqual(ftref, res1[8 * L:], 3)
def test_005_legacy(self):
print "\ntest_005_legacy"
# test if flowgraph is set up as expected!
multiple = 9
overlap = 4
L = 32
taps = ft.generate_phydyas_filter(L, overlap)
data = np.arange(1, multiple * L // 2 + 1 + 1, dtype=np.complex)
# instatiated blocks and flowgraph
phydyas = fbmc.rx_sdft_cvc(taps, L)
pfb = fbmc.rx_polyphase_cvc(taps, L)
print "phydyas: L = ", phydyas.L(), ", overlap = ", phydyas.overlap()
src = blocks.vector_source_c(data, vlen=1)
snk0 = blocks.vector_sink_c(L)
snk1 = blocks.vector_sink_c(L)
tb = gr.top_block()
tb.connect(src, phydyas, snk0)
tb.connect(src, pfb, snk1)
# run fg and get results
tb.run()
res0 = np.array(snk0.data())
res1 = np.array(snk1.data())
ref = self.get_reference_output(data, taps, L, overlap, multiple)
ftref = ft.rx(data[: -L // 2], taps, L)
print "\nFBMC blocks"
print ftref
lcut = 2
rcut = 5
print "\nGR blocks"
print res0[lcut * L: -rcut * L].reshape((-1, L)).T
print "\nIOTA blocks"
print res1[8 * L:].reshape((-1, L)).T
ftref = ftref.T
ftref = ftref.flatten()
self.assertComplexTuplesAlmostEqual(ftref, res0[lcut * L: -rcut * L], 3)
self.assertComplexTuplesAlmostEqual(ftref, res1[8 * L:], 3)
def test_003_filter(self):
print "\ntest003_filter"
multiple = 9
overlap = 2
L = 4
taps = np.append([
0.0,
], np.ones(overlap * L - 1, dtype=float))
taps = np.append(taps, [
0.0,
])
data = np.arange(1, multiple * L // 2 + 1 + 1, dtype=np.complex)
print "len(data) = ", len(data)
print taps
print data
# instatiated blocks and flowgraph
phydyas = fbmc.rx_sdft_cvc(taps, L)
print "phydyas: L = ", phydyas.L(), ", overlap = ", phydyas.overlap(
), ", history() = ", phydyas.history()
src = blocks.vector_source_c(data, vlen=1)
snk = blocks.vector_sink_c(L)
tb = gr.top_block()
tb.connect(src, phydyas, snk)
# run fg and get results
tb.run()
res = np.array(snk.data())
print "len(res) = ", len(res)
ref = self.get_reference_output(data, taps, L, overlap, multiple)
ftref = ft.rx(data[:-L // 2], taps, L)
print "\nFBMC blocks"
print ftref
print "\nGR blocks"
print res.reshape((-1, L)).T
ftref = ftref.T
ftref = ftref.flatten()
self.assertComplexTuplesAlmostEqual(ftref, res[2 * L:-L])
def test_002_modulation(self):
total_subcarriers = 8
overlap = 4
taps = ft.generate_phydyas_filter(total_subcarriers, overlap)
zvals = np.zeros(overlap * total_subcarriers, dtype=complex)
rvals = np.arange(1, total_subcarriers + 1, dtype=complex)
dummy_frame = np.concatenate((zvals, rvals))
data = np.tile(dummy_frame, 4).flatten()
src = blocks.vector_source_c(data, repeat=False, vlen=total_subcarriers)
mod = fbmc.tx_sdft_vcc(taps, total_subcarriers)
demod = fbmc.rx_sdft_cvc(taps, total_subcarriers)
snk = blocks.vector_sink_c(total_subcarriers)
self.tb.connect(src, mod, demod, snk)
self.tb.run()
res = np.array(snk.data())
def test_004_phydyas_taps(self):
print "\ntest_004_phydyas_taps"
# test if flowgraph is set up as expected!
multiple = 9
overlap = 4
L = 32
taps = ft.generate_phydyas_filter(L, overlap)
# taps = np.append([0.0, ], np.ones(overlap * L - 1, dtype=float))
# taps = np.append(taps, [0.0, ])
data = np.arange(1, multiple * L // 2 + 1 + 1, dtype=np.complex)
# instatiated blocks and flowgraph
phydyas = fbmc.rx_sdft_cvc(taps, L)
print "phydyas: L = ", phydyas.L(), ", overlap = ", phydyas.overlap()
src = blocks.vector_source_c(data, vlen=1)
snk = blocks.vector_sink_c(L)
tb = gr.top_block()
tb.connect(src, phydyas, snk)
# run fg and get results
tb.run()
res = np.array(snk.data())
ref = self.get_reference_output(data, taps, L, overlap, multiple)
ftref = ft.rx(data[:-L // 2], taps, L)
print "\nFBMC blocks"
print np.shape(ftref)
print ftref
lcut = 2
rcut = 5
print "\nGR blocks"
print np.shape(res)
print res[lcut * L:-rcut * L].reshape((-1, L)).T
# print res.reshape((-1, L)).T
ftref = ftref.T
ftref = ftref.flatten()
self.assertComplexTuplesAlmostEqual(ftref, res[lcut * L:-rcut * L], 3)
def test_004_phydyas_taps(self):
print "\ntest_004_phydyas_taps"
# test if flowgraph is set up as expected!
multiple = 9
overlap = 4
L = 32
taps = ft.generate_phydyas_filter(L, overlap)
# taps = np.append([0.0, ], np.ones(overlap * L - 1, dtype=float))
# taps = np.append(taps, [0.0, ])
data = np.arange(1, multiple * L // 2 + 1 + 1, dtype=np.complex)
# instatiated blocks and flowgraph
phydyas = fbmc.rx_sdft_cvc(taps, L)
print "phydyas: L = ", phydyas.L(), ", overlap = ", phydyas.overlap()
src = blocks.vector_source_c(data, vlen=1)
snk = blocks.vector_sink_c(L)
tb = gr.top_block()
tb.connect(src, phydyas, snk)
# run fg and get results
tb.run()
res = np.array(snk.data())
ref = self.get_reference_output(data, taps, L, overlap, multiple)
ftref = ft.rx(data[: -L // 2], taps, L)
print "\nFBMC blocks"
print np.shape(ftref)
print ftref
lcut = 2
rcut = 5
print "\nGR blocks"
print np.shape(res)
print res[lcut * L: -rcut * L].reshape((-1, L)).T
# print res.reshape((-1, L)).T
ftref = ftref.T
ftref = ftref.flatten()
self.assertComplexTuplesAlmostEqual(ftref, res[lcut * L: -rcut * L], 3)
def test_002_modulation(self):
total_subcarriers = 8
overlap = 4
taps = ft.generate_phydyas_filter(total_subcarriers, overlap)
zvals = np.zeros(overlap * total_subcarriers, dtype=complex)
rvals = np.arange(1, total_subcarriers + 1, dtype=complex)
dummy_frame = np.concatenate((zvals, rvals))
data = np.tile(dummy_frame, 4).flatten()
src = blocks.vector_source_c(data,
repeat=False,
vlen=total_subcarriers)
mod = fbmc.tx_sdft_vcc(taps, total_subcarriers)
demod = fbmc.rx_sdft_cvc(taps, total_subcarriers)
snk = blocks.vector_sink_c(total_subcarriers)
self.tb.connect(src, mod, demod, snk)
self.tb.run()
res = np.array(snk.data())
def test_002_buffers(self):
print "\ntest002_buffers"
# test if fg runs as expected on a minimum example!
multiple = 5
overlap = 1 # must be 1 in this test!
L = 4
taps = np.array([0, 1, 2, 1]) # np.ones(overlap * L, dtype=float)
taps = np.append(taps, [
0.0,
])
data = np.arange(1, multiple * L // 2 + 1 + 1, dtype=np.complex)
# set Up flowgraph
phydyas = fbmc.rx_sdft_cvc(taps, L)
print "phydyas: L = ", phydyas.L(), ", overlap = ", phydyas.overlap()
src = blocks.vector_source_c(data, vlen=1)
snk = blocks.vector_sink_c(L)
tb = gr.top_block()
tb.connect(src, phydyas, snk)
# run fg and get results
tb.run()
res = np.array(snk.data())
# check results
ref = self.get_reference_output(data, taps, L, overlap, multiple)
print "\nJohannes test"
print ref.reshape((-1, L)).T
ftref = ft.rx(data[:-L // 2], taps, L)
print "\nFBMC blocks"
print ftref
print "\nGR blocks"
print res.reshape((-1, L)).T
ftref = ftref.T
self.assertComplexTuplesAlmostEqual(ftref.flatten(),
res.flatten()[2 * L:])
def test_003_filter(self):
print "\ntest003_filter"
multiple = 9
overlap = 2
L = 4
taps = np.append([0.0, ], np.ones(overlap * L - 1, dtype=float))
taps = np.append(taps, [0.0, ])
data = np.arange(1, multiple * L // 2 + 1 + 1, dtype=np.complex)
print "len(data) = ", len(data)
print taps
print data
# instatiated blocks and flowgraph
phydyas = fbmc.rx_sdft_cvc(taps, L)
print "phydyas: L = ", phydyas.L(), ", overlap = ", phydyas.overlap(), ", history() = ", phydyas.history()
src = blocks.vector_source_c(data, vlen=1)
snk = blocks.vector_sink_c(L)
tb = gr.top_block()
tb.connect(src, phydyas, snk)
# run fg and get results
tb.run()
res = np.array(snk.data())
print "len(res) = ", len(res)
ref = self.get_reference_output(data, taps, L, overlap, multiple)
ftref = ft.rx(data[: -L // 2], taps, L)
print "\nFBMC blocks"
print ftref
print "\nGR blocks"
print res.reshape((-1, L)).T
ftref = ftref.T
ftref = ftref.flatten()
self.assertComplexTuplesAlmostEqual(ftref, res[2 * L: -L])
def test_002_buffers(self):
print "\ntest002_buffers"
# test if fg runs as expected on a minimum example!
multiple = 5
overlap = 1 # must be 1 in this test!
L = 4
taps = np.array([0, 1, 2, 1]) # np.ones(overlap * L, dtype=float)
taps = np.append(taps, [0.0, ])
data = np.arange(1, multiple * L // 2 + 1 + 1, dtype=np.complex)
# set Up flowgraph
phydyas = fbmc.rx_sdft_cvc(taps, L)
print "phydyas: L = ", phydyas.L(), ", overlap = ", phydyas.overlap()
src = blocks.vector_source_c(data, vlen=1)
snk = blocks.vector_sink_c(L)
tb = gr.top_block()
tb.connect(src, phydyas, snk)
# run fg and get results
tb.run()
res = np.array(snk.data())
# check results
ref = self.get_reference_output(data, taps, L, overlap, multiple)
print "\nJohannes test"
print ref.reshape((-1, L)).T
ftref = ft.rx(data[: -L // 2], taps, L)
print "\nFBMC blocks"
print ftref
print "\nGR blocks"
print res.reshape((-1, L)).T
ftref = ftref.T
self.assertComplexTuplesAlmostEqual(ftref.flatten(), res.flatten()[2*L:])
def test_004_config_frame_mod(self):
num_frames = 10
cfg = fbmc.fbmc_config(num_used_subcarriers=20,
num_payload_sym=16,
num_overlap_sym=4,
modulation="QPSK",
preamble="IAM",
samp_rate=250000)
total_subcarriers = cfg.num_total_subcarriers() # 8
used_subcarriers = cfg.num_used_subcarriers() # 4
channel_map = cfg.channel_map(
) # ft.get_channel_map(used_subcarriers, total_subcarriers)
payload_symbols = cfg.num_payload_sym() # 8
overlap = cfg.num_overlap_sym() # 4
taps = cfg.phydyas_impulse_taps(
cfg.num_total_subcarriers(), cfg.num_overlap_sym(
)) # ft.generate_phydyas_filter(total_subcarriers, overlap)
preamble = ft.get_preamble(total_subcarriers)
num_preamble_symbols = len(preamble) // total_subcarriers
payload = ft.get_payload(payload_symbols, used_subcarriers)
payload = np.tile(payload, num_frames).flatten()
src = blocks.vector_source_b(payload, repeat=False)
framer = fbmc.frame_generator_bvc(used_subcarriers, total_subcarriers,
payload_symbols, overlap,
channel_map, preamble)
snk_frame = blocks.vector_sink_c(total_subcarriers) # a debug output
mod = fbmc.tx_sdft_vcc(taps, total_subcarriers)
demod = fbmc.rx_sdft_cvc(taps, total_subcarriers)
skipper = blocks.skiphead(8 * total_subcarriers, 4)
snk_rx = blocks.vector_sink_c(total_subcarriers)
deframer = fbmc.deframer_vcb(used_subcarriers, total_subcarriers,
num_preamble_symbols, payload_symbols,
overlap, channel_map)
snk = blocks.vector_sink_b(1)
self.tb.connect(src, framer, mod, demod, skipper, deframer, snk)
self.tb.connect(framer, snk_frame)
self.tb.connect(skipper, snk_rx)
self.tb.run()
res = np.array(snk.data())
print res
print payload
moddata = np.array(snk_frame.data())
print "len(moddata) = ", len(moddata)
rxdata = np.array(snk_rx.data())
print "len(rxdata) = ", len(rxdata)
# plt.plot(rxdata.real * 0.03)
# for i in range(len(moddata)):
# if (i + 1) % total_subcarriers == 0:
# plt.axvline(i)
# plt.plot(moddata.real)
# plt.grid()
# plt.show()
print "len(payload) = ", len(payload)
print "len(result) = ", len(res)
self.assertTupleEqual(tuple(payload[:len(res)]), tuple(res))
