From 64096f2d97dc027ffa5bb55a08653f49dcee2f49 Mon Sep 17 00:00:00 2001 From: cmayer Date: Sat, 3 Nov 2018 12:03:34 +0100 Subject: [PATCH] processing data in smaller chunks (188-110A) --- lib/adaptive_dfe_impl.cc | 2 +- python/physical_layer/MIL_STD_188_110A.py | 82 +++++++++++------------ 2 files changed, 42 insertions(+), 42 deletions(-) diff --git a/lib/adaptive_dfe_impl.cc b/lib/adaptive_dfe_impl.cc index c68ad00..8b56319 100644 --- a/lib/adaptive_dfe_impl.cc +++ b/lib/adaptive_dfe_impl.cc @@ -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 diff --git a/python/physical_layer/MIL_STD_188_110A.py b/python/physical_layer/MIL_STD_188_110A.py index 9bb4682..c114b62 100644 --- a/python/physical_layer/MIL_STD_188_110A.py +++ b/python/physical_layer/MIL_STD_188_110A.py @@ -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"""