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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, 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])
modulated_sync_word = modulated_sync_word_pre[86:(512+86)] # compensate for the RRC filter delay
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)
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