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#!/usr/bin/env python
#
# Copyright 2020 Free Software Foundation, Inc.
#
# This file is part of GNU Radio
#
# SPDX-License-Identifier: GPL-3.0-or-later
#
#
from gnuradio import gr, gr_unittest
import random
import numpy
from gnuradio import digital, blocks, channels
class qa_linear_equalizer(gr_unittest.TestCase):
def unpack_values(self, values_in, bits_per_value, bits_per_symbol):
# verify that 8 is divisible by bits_per_symbol
m = bits_per_value / bits_per_symbol
# print(m)
mask = 2**(bits_per_symbol) - 1
if bits_per_value != m * bits_per_symbol:
print(
"error - bits per symbols must fit nicely into bits_per_value bit values")
return []
num_values = len(values_in)
num_symbols = int(num_values * (m))
cur_byte = 0
cur_bit = 0
out = []
for i in range(num_symbols):
s = (
values_in[cur_byte] >> (
bits_per_value -
bits_per_symbol -
cur_bit)) & mask
out.append(s)
cur_bit += bits_per_symbol
if cur_bit >= bits_per_value:
cur_bit = 0
cur_byte += 1
return out
def map_symbols_to_constellation(self, symbols, cons):
l = list(map(lambda x: cons.points()[x], symbols))
return l
def setUp(self):
random.seed(987654)
self.tb = gr.top_block()
self.num_data = num_data = 10000
self.sps = sps = 4
self.eb = eb = 0.35
self.preamble = preamble = [
0x27,
0x2F,
0x18,
0x5D,
0x5B,
0x2A,
0x3F,
0x71,
0x63,
0x3C,
0x17,
0x0C,
0x0A,
0x41,
0xD6,
0x1F,
0x4C,
0x23,
0x65,
0x68,
0xED,
0x1C,
0x77,
0xA7,
0x0E,
0x0A,
0x9E,
0x47,
0x82,
0xA4,
0x57,
0x24,
]
self.payload_size = payload_size = 300 # bytes
self.data = data = [0] * 4 + \
[random.getrandbits(8) for i in range(payload_size)]
self.gain = gain = .001 # LMS gain
self.corr_thresh = corr_thresh = 3e6
self.num_taps = num_taps = 16
def tearDown(self):
self.tb = None
def transform(self, src_data, gain, const):
SRC = blocks.vector_source_c(src_data, False)
EQU = digital.lms_dd_equalizer_cc(4, gain, 1, const.base())
DST = blocks.vector_sink_c()
self.tb.connect(SRC, EQU, DST)
self.tb.run()
return DST.data()
def test_001_identity(self):
# Constant modulus signal so no adjustments
const = digital.constellation_qpsk()
src_data = const.points() * 1000
N = 100 # settling time
expected_data = src_data[N:]
result = self.transform(src_data, 0.1, const)[N:]
N = -500
self.assertComplexTuplesAlmostEqual(expected_data[N:], result[N:], 5)
def test_qpsk_3tap_lms_training(self):
# set up fg
gain = 0.01 # LMS gain
num_taps = 16
num_samp = 2000
num_test = 500
cons = digital.constellation_qpsk().base()
rxmod = digital.generic_mod(
cons, False, self.sps, True, self.eb, False, False)
modulated_sync_word_pre = digital.modulate_vector_bc(
rxmod.to_basic_block(), self.preamble + self.preamble, [1])
# compensate for the RRC filter delay
modulated_sync_word = modulated_sync_word_pre[86:(512 + 86)]
corr_max = numpy.abs(
numpy.dot(
modulated_sync_word,
numpy.conj(modulated_sync_word)))
corr_calc = self.corr_thresh / (corr_max * corr_max)
preamble_symbols = self.map_symbols_to_constellation(
self.unpack_values(self.preamble, 8, 2), cons)
alg = digital.adaptive_algorithm_lms(cons, gain).base()
evm = digital.meas_evm_cc(cons, digital.evm_measurement_t.EVM_PERCENT)
leq = digital.linear_equalizer(
num_taps,
self.sps,
alg,
False,
preamble_symbols,
'corr_est')
correst = digital.corr_est_cc(
modulated_sync_word,
self.sps,
12,
corr_calc,
digital.THRESHOLD_ABSOLUTE)
constmod = digital.generic_mod(
constellation=cons,
differential=False,
samples_per_symbol=4,
pre_diff_code=True,
excess_bw=0.35,
verbose=False,
log=False)
chan = channels.channel_model(
noise_voltage=0.0,
frequency_offset=0.0,
epsilon=1.0,
taps=(1.0 + 1.0j, 0.63 - .22j, -.1 + .07j),
noise_seed=0,
block_tags=False)
vso = blocks.vector_source_b(self.preamble + self.data, True, 1, [])
head = blocks.head(gr.sizeof_float * 1, num_samp)
vsi = blocks.vector_sink_f()
self.tb.connect(vso, constmod, chan, correst, leq, evm, head, vsi)
self.tb.run()
# look at the last 1000 samples, should converge quickly, below 5% EVM
upper_bound = list(20.0 * numpy.ones((num_test,)))
lower_bound = list(0.0 * numpy.zeros((num_test,)))
output_data = vsi.data()
output_data = output_data[-num_test:]
self.assertLess(output_data, upper_bound)
self.assertGreater(output_data, lower_bound)
if __name__ == '__main__':
gr_unittest.run(qa_linear_equalizer)
|
