def test_003_snr(self):
nframes = 30
timeslots = 5
subcarriers = 1024
active_subcarriers = 936
overlap = 2
cp_len = subcarriers // 2
ramp_len = cp_len // 2
active_ratio = subcarriers / active_subcarriers
subcarrier_map = get_subcarrier_map(subcarriers, active_subcarriers,
dc_free=True)
preambles = mapped_preamble(self.seed, self.filtertype, self.filteralpha,
active_subcarriers, subcarriers,
subcarrier_map, overlap, cp_len, ramp_len)
core_preamble = preambles[1]
sigenergy = calculate_energy(core_preamble)
data = np.copy(core_preamble)
snrs = np.arange(3, 3 * nframes, 3, dtype=np.float)
snrs_lin = 10. ** (snrs / 10.)
expected_snrs_lin = np.concatenate(((np.inf,), snrs_lin))
for i, snr_lin in enumerate(snrs_lin):
nscale = calculate_noise_scale(
snr_lin, sigenergy, active_ratio, core_preamble.size)
noise = get_noise_vector(core_preamble.size, nscale)
d = core_preamble + noise
data = np.concatenate((data, d))
dut = gfdm.channel_estimator_cc(
timeslots, subcarriers, active_subcarriers, True, 1, core_preamble)
src = blocks.vector_source_c(data)
snk = blocks.vector_sink_c()
self.tb.connect(src, dut, snk)
self.tb.run()
res = np.array(snk.data())
self.assertEqual(res.size, nframes * timeslots * subcarriers)
tags = snk.tags()
snr_tags = [t for t in tags if pmt.eq(t.key, pmt.mp("snr_lin"))]
for i, t in enumerate(snr_tags):
self.assertEqual(t.offset, i * timeslots * subcarriers)
res_lin = pmt.to_float(t.value)
res_db = 10. * np.log10(res_lin)
ref_db = 10. * np.log10(expected_snrs_lin[i])
# print(f"Reference: {ref_db:6.3f}dB\t{res_db:6.3f}dB")
if np.isfinite(ref_db):
self.assertTrue(np.abs(res_db - ref_db) < 1.)
def test_002_selective(self):
timeslots = 5
subcarriers = 64
active_subcarriers = 52
overlap = 2
cp_len = subcarriers // 2
ramp_len = cp_len // 2
active_symbols = timeslots * active_subcarriers
subcarrier_map = get_subcarrier_map(subcarriers,
active_subcarriers,
dc_free=True)
preambles = mapped_preamble(
self.seed,
self.filtertype,
self.filteralpha,
active_subcarriers,
subcarriers,
subcarrier_map,
overlap,
cp_len,
ramp_len,
)
full_preamble = preambles[0]
core_preamble = preambles[1]
h = np.array([1.0, 0.5, 0.1j, 0.1 + 0.05j], dtype=complex)
data = np.convolve(full_preamble, h, "full")[0:full_preamble.size]
data = data[cp_len:-ramp_len]
self.assertEqual(data.size, core_preamble.size)
dut = gfdm.channel_estimator_cc(timeslots, subcarriers,
active_subcarriers, True, 1,
core_preamble)
src = blocks.vector_source_c(data)
snk = blocks.vector_sink_c()
self.tb.connect(src, dut, snk)
self.tb.run()
res = np.array(snk.data())
lowres = res[0:active_symbols // 2]
hires = res[-active_symbols // 2:]
fh = np.fft.fft(h, timeslots * subcarriers)
lowfh = fh[0:active_symbols // 2]
hifh = fh[-active_symbols // 2:]
self.assertComplexTuplesAlmostEqual(lowres, lowfh, 1)
self.assertComplexTuplesAlmostEqual(hires, hifh, 1)
def test_001_simple(self):
timeslots = 3
subcarriers = 32
active_subcarriers = 24
overlap = 2
cp_len = subcarriers // 2
ramp_len = cp_len // 2
subcarrier_map = get_subcarrier_map(subcarriers,
active_subcarriers,
dc_free=True)
preambles = mapped_preamble(
self.seed,
self.filtertype,
self.filteralpha,
active_subcarriers,
subcarriers,
subcarrier_map,
overlap,
cp_len,
ramp_len,
)
core_preamble = preambles[1]
dut = gfdm.channel_estimator_cc(timeslots, subcarriers,
active_subcarriers, True, 1,
core_preamble)
src = blocks.vector_source_c(core_preamble)
snk = blocks.vector_sink_c()
self.tb.connect(src, dut, snk)
self.tb.run()
res = np.array(snk.data())
self.assertComplexTuplesAlmostEqual(res,
np.ones(res.size, dtype=res.dtype),
6)