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processing data in smaller chunks (188-110A)

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This commit is contained in:
cmayer 2018-11-03 12:03:34 +01:00
parent 54ceab0892
commit 64096f2d97
2 changed files with 42 additions and 42 deletions
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2
lib/adaptive_dfe_impl.cc
View file

@ -546,7 +546,7 @@ void adaptive_dfe_impl::update_pll(float doppler) {
_ud = delta_f;
_df +=_b[0]*_ud + _b[1]*ud_old;
}
GR_LOG_DEBUG(d_logger, str(boost::format("PLL: df=%f delta_f=%f (rad/symb)") % _df % delta_f));
GR_LOG_DEBUG(d_logger, str(boost::format("PLL: df=%f delta_f=%f (rad/sample)") % _df % delta_f));
}
void adaptive_dfe_impl::insert_sample(gr_complex z) {
// insert sample into the circular buffer

82
python/physical_layer/MIL_STD_188_110A.py
View file

@ -103,8 +103,7 @@ class PhysicalLayer(object):
def __init__(self, sps):
"""intialization"""
self._sps = sps
self._frame_counter = 0
self._is_first_frame = True
self._frame_counter = -1
self._constellations = [self.make_psk(2, [0,1]),
self.make_psk(4, [0,1,3,2]),
self.make_psk(8, [0,1,3,2,7,6,4,5])] ## TODO: check 8PSK gray code
@ -127,24 +126,24 @@ class PhysicalLayer(object):
symbols are saved"""
print('-------------------- get_frame --------------------',
self._pre_counter, self._frame_counter)
## --- preamble frame ----
if self._pre_counter != 0:
self._scr_data.reset()
return [self._preamble,MODE_BPSK,True,False]
num_symb = 11520 if self._mode['interleaver'][0] == 'L' else 1440
a = np.zeros(num_symb, dtype=[('symb', np.complex64),
('scramble', np.complex64)])
n_known = self._mode['known']
## ----- data frame ------
if self._frame_counter == self._num_frames_per_block:
self._frame_counter = 0
a = np.zeros(self._frame_len, dtype=[('symb', np.complex64),
('scramble', np.complex64)])
n_unknown = self._mode['unknown']
counter_d1d2 = 0
for i in range(0,num_symb,n_known+n_unknown):
a['symb'][i :i+n_unknown ] = 0
a['symb'][i+n_unknown:i+n_unknown+n_known] = 1
if i>=num_symb-2*(n_unknown+n_known):
a['symb'][i+0:i+ 8] *= n_psk(2, WALSH[self._d1d2[counter_d1d2]][:])
a['symb'][i+8:i+16] *= n_psk(2, WALSH[self._d1d2[counter_d1d2]][:])
counter_d1d2 += 1
a['symb'] = 1;
a['symb'][0:n_unknown] = 0
if self._frame_counter >= self._num_frames_per_block-2:
idx_d1d2 = self._frame_counter - self._num_frames_per_block + 2;
a['symb'][n_unknown :n_unknown+ 8] *= n_psk(2, WALSH[self._d1d2[idx_d1d2]][:])
a['symb'][n_unknown+8:n_unknown+16] *= n_psk(2, WALSH[self._d1d2[idx_d1d2]][:])
a['scramble'] = n_psk(8, np.array([self._scr_data.next() for _ in range(num_symb)]))
a['scramble'] = n_psk(8, np.array([self._scr_data.next() for _ in range(self._frame_len)]))
a['symb'] *= a['scramble']
self._frame_counter += 1
return [a, self._mode['ci'],False,True]
@ -157,35 +156,23 @@ class PhysicalLayer(object):
self._frame_counter,len(symbols),len(iq_samples))
success = False
doppler = 0
if self._frame_counter == 0:
success,doppler = self.quality_preamble(symbols,iq_samples)
if self._frame_counter == -1: ## -- preamble ----
success,doppler = self.get_doppler_from_preamble(symbols, iq_samples)
if len(symbols) != 0:
data = [FROM_WALSH[walsh_to_num
(np.real
(np.sum
(symbols[i:i+32].reshape((4,8)),0))<0)]
for i in range(0,15*32,32)]
print('data=',data)
self._pre_counter = sum((np.array(data[11:14])&3)
*(1<<2*np.arange(3)[::-1]))
self._d1d2 = data[9:11]
self._mode = MODE[data[9]][data[10]]
print('pre_counter', self._pre_counter, 'mode', self._mode)
self._is_first_frame = not success
success = True
else:
for i in range(0,len(symbols),40):
print(i,symbols[i:i+40], np.mean(np.abs(symbols[i:i+40])))
success = np.mean(np.abs(symbols[0:40])) > 0.5
success = self.decode_preamble(symbols)
if self._pre_counter == 0:
self._frame_counter = 0
print('pre_counter', self._pre_counter,
'mode', self._mode)
else: ## ------------------------ data frame ----
print(self._frame_counter,symbols, np.mean(np.abs(symbols)))
success = np.mean(np.abs(symbols[0:20])) > 0.5
if not success:
self._frame_counter = 0
self._frame_counter = -1
self._pre_counter = -1
return success,doppler
def is_preamble(self):
return self._frame_counter == 0
def quality_preamble(self, symbols, iq_samples):
def get_doppler_from_preamble(self, symbols, iq_samples):
"""quality check and doppler estimation for preamble"""
success = True
doppler = 0
@ -206,10 +193,23 @@ class PhysicalLayer(object):
success = np.mean(apks[(0,3),]) > 2*np.mean(apks[(1,2),])
doppler = np.diff(np.unwrap(np.angle(pks[(0,3),])))[0]/(3*32) if success else 0
print('success=', success, 'doppler=', doppler)
#if len(symbols) != 0:
## TODO: check the symbols
return success,doppler
def decode_preamble(self, symbols):
data = [FROM_WALSH[walsh_to_num
(np.real
(np.sum
(symbols[i:i+32].reshape((4,8)),0))<0)]
for i in range(0,15*32,32)]
print('data=',data)
self._pre_counter = sum((np.array(data[11:14])&3)*(1<<2*np.arange(3)[::-1]))
self._d1d2 = data[9:11]
self._mode = MODE[data[9]][data[10]]
self._block_len = 11520 if self._mode['interleaver'][0] == 'L' else 1440
self._frame_len = self._mode['known'] + self._mode['unknown']
self._num_frames_per_block = self._block_len/self._frame_len;
return True
@staticmethod
def get_preamble():
"""preamble symbols + scrambler"""