mirror of https://github.com/hb9fxq/gr-digitalhf
processing data in smaller chunks (188-110A)
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cmayer
parent
54ceab0892
commit
64096f2d97
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@ -546,7 +546,7 @@ void adaptive_dfe_impl::update_pll(float doppler) {
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_ud = delta_f;
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_df +=_b[0]*_ud + _b[1]*ud_old;
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}
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GR_LOG_DEBUG(d_logger, str(boost::format("PLL: df=%f delta_f=%f (rad/symb)") % _df % delta_f));
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GR_LOG_DEBUG(d_logger, str(boost::format("PLL: df=%f delta_f=%f (rad/sample)") % _df % delta_f));
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}
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void adaptive_dfe_impl::insert_sample(gr_complex z) {
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// insert sample into the circular buffer
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@ -103,8 +103,7 @@ class PhysicalLayer(object):
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def __init__(self, sps):
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"""intialization"""
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self._sps = sps
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self._frame_counter = 0
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self._is_first_frame = True
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self._frame_counter = -1
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self._constellations = [self.make_psk(2, [0,1]),
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self.make_psk(4, [0,1,3,2]),
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self.make_psk(8, [0,1,3,2,7,6,4,5])] ## TODO: check 8PSK gray code
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@ -127,24 +126,24 @@ class PhysicalLayer(object):
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symbols are saved"""
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print('-------------------- get_frame --------------------',
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self._pre_counter, self._frame_counter)
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## --- preamble frame ----
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if self._pre_counter != 0:
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self._scr_data.reset()
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return [self._preamble,MODE_BPSK,True,False]
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num_symb = 11520 if self._mode['interleaver'][0] == 'L' else 1440
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a = np.zeros(num_symb, dtype=[('symb', np.complex64),
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('scramble', np.complex64)])
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n_known = self._mode['known']
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## ----- data frame ------
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if self._frame_counter == self._num_frames_per_block:
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self._frame_counter = 0
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a = np.zeros(self._frame_len, dtype=[('symb', np.complex64),
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('scramble', np.complex64)])
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n_unknown = self._mode['unknown']
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counter_d1d2 = 0
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for i in range(0,num_symb,n_known+n_unknown):
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a['symb'][i :i+n_unknown ] = 0
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a['symb'][i+n_unknown:i+n_unknown+n_known] = 1
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if i>=num_symb-2*(n_unknown+n_known):
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a['symb'][i+0:i+ 8] *= n_psk(2, WALSH[self._d1d2[counter_d1d2]][:])
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a['symb'][i+8:i+16] *= n_psk(2, WALSH[self._d1d2[counter_d1d2]][:])
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counter_d1d2 += 1
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a['symb'] = 1;
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a['symb'][0:n_unknown] = 0
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if self._frame_counter >= self._num_frames_per_block-2:
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idx_d1d2 = self._frame_counter - self._num_frames_per_block + 2;
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a['symb'][n_unknown :n_unknown+ 8] *= n_psk(2, WALSH[self._d1d2[idx_d1d2]][:])
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a['symb'][n_unknown+8:n_unknown+16] *= n_psk(2, WALSH[self._d1d2[idx_d1d2]][:])
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a['scramble'] = n_psk(8, np.array([self._scr_data.next() for _ in range(num_symb)]))
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a['scramble'] = n_psk(8, np.array([self._scr_data.next() for _ in range(self._frame_len)]))
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a['symb'] *= a['scramble']
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self._frame_counter += 1
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return [a, self._mode['ci'],False,True]
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@ -157,35 +156,23 @@ class PhysicalLayer(object):
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self._frame_counter,len(symbols),len(iq_samples))
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success = False
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doppler = 0
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if self._frame_counter == 0:
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success,doppler = self.quality_preamble(symbols,iq_samples)
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if self._frame_counter == -1: ## -- preamble ----
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success,doppler = self.get_doppler_from_preamble(symbols, iq_samples)
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if len(symbols) != 0:
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data = [FROM_WALSH[walsh_to_num
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(np.real
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(np.sum
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(symbols[i:i+32].reshape((4,8)),0))<0)]
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for i in range(0,15*32,32)]
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print('data=',data)
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self._pre_counter = sum((np.array(data[11:14])&3)
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*(1<<2*np.arange(3)[::-1]))
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self._d1d2 = data[9:11]
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self._mode = MODE[data[9]][data[10]]
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print('pre_counter', self._pre_counter, 'mode', self._mode)
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self._is_first_frame = not success
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success = True
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else:
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for i in range(0,len(symbols),40):
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print(i,symbols[i:i+40], np.mean(np.abs(symbols[i:i+40])))
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success = np.mean(np.abs(symbols[0:40])) > 0.5
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success = self.decode_preamble(symbols)
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if self._pre_counter == 0:
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self._frame_counter = 0
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print('pre_counter', self._pre_counter,
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'mode', self._mode)
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else: ## ------------------------ data frame ----
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print(self._frame_counter,symbols, np.mean(np.abs(symbols)))
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success = np.mean(np.abs(symbols[0:20])) > 0.5
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if not success:
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self._frame_counter = 0
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self._frame_counter = -1
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self._pre_counter = -1
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return success,doppler
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def is_preamble(self):
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return self._frame_counter == 0
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def quality_preamble(self, symbols, iq_samples):
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def get_doppler_from_preamble(self, symbols, iq_samples):
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"""quality check and doppler estimation for preamble"""
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success = True
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doppler = 0
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@ -206,10 +193,23 @@ class PhysicalLayer(object):
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success = np.mean(apks[(0,3),]) > 2*np.mean(apks[(1,2),])
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doppler = np.diff(np.unwrap(np.angle(pks[(0,3),])))[0]/(3*32) if success else 0
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print('success=', success, 'doppler=', doppler)
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#if len(symbols) != 0:
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## TODO: check the symbols
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return success,doppler
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def decode_preamble(self, symbols):
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data = [FROM_WALSH[walsh_to_num
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(np.real
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(np.sum
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(symbols[i:i+32].reshape((4,8)),0))<0)]
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for i in range(0,15*32,32)]
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print('data=',data)
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self._pre_counter = sum((np.array(data[11:14])&3)*(1<<2*np.arange(3)[::-1]))
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self._d1d2 = data[9:11]
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self._mode = MODE[data[9]][data[10]]
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self._block_len = 11520 if self._mode['interleaver'][0] == 'L' else 1440
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self._frame_len = self._mode['known'] + self._mode['unknown']
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self._num_frames_per_block = self._block_len/self._frame_len;
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return True
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@staticmethod
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def get_preamble():
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"""preamble symbols + scrambler"""
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