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https://github.com/hb9fxq/gr-digitalhf
synced 2024-12-22 07:09:59 +00:00
preamble doppler estimation for 110D mode added
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1034b6045e
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@ -793,13 +793,13 @@ class PhysicalLayer(object):
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if self._superframe_counter != 0:
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self._state = 'FIXED'
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return [self._fixed_s,MODE_BPSK,success,False]
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else: ## TODO
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else:
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self._frame_counter = 0
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if self._wid != MODE_WALSH:
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self._scr_data.reset()
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self._state = 'MP'
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[mode,a] = self.get_next_data_frame(success)
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return [a,mode,success,False]
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return [a,mode,success,True]
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else: ## WID0
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self._frame_counter += 1
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return [self._walsh_s,MODE_BPSK,success,True]
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@ -808,10 +808,13 @@ class PhysicalLayer(object):
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if self._wid != MODE_WALSH:
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[mode,a] = self.get_next_data_frame(success)
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self._frame_counter += (self._state == 'MP')
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success = np.abs(np.real(np.mean(symbols[::2]))) > 0.7 if self._state == 'MP' else True
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return [a,mode,success,success]
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success = np.abs(np.real(np.mean(symbols[::2]))) > 0.5 if self._state == 'MP' else True
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if success == False:
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print('TEST: {} success={} {} {}'.format(self._state, success, symbols[::2], np.mean(symbols[::2])))
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return [a,mode,success,True]
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else: ## WID0
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self._frame_counter += 1
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## TODO: WALSH16 check for BW > 30kHz
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m = len(symbols)//32
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z = np.zeros(8*m, dtype=np.complex64)
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for i,s in enumerate(symbols.reshape(m, 32)):
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@ -831,9 +834,11 @@ class PhysicalLayer(object):
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self._state = 'DATA'
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a = np.zeros(self._known, common.SYMB_SCRAMBLE_DTYPE)
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if (self._frame_counter % self._intl_frames) == self._intl_frames-1:
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print('next_data_fram MP_shifted ', self._frame_counter, self._intl_frames)
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a['symb'][:] = self._mp_shifted
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a['scramble'][:] = self._mp_shifted
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else:
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print('next_data_frame MP_regular', self._frame_counter, self._intl_frames)
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a['symb'][:] = self._mp
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a['scramble'][:] = self._mp
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return MODE_BPSK,a
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@ -842,7 +847,7 @@ class PhysicalLayer(object):
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a = np.zeros(self._unknown, common.SYMB_SCRAMBLE_DTYPE)
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a['scramble'][:] = 1
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self._scr_data.reset()
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if self._wid_mode <= MODE_8PSK: ## not QAM
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if self._data_mode <= MODE_8PSK: ## not QAM
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for i in range(self._unknown):
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a['scramble'][i] = np.exp(2j*np.pi*self._scr_data.next()/8)
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else: ## QAM modes
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@ -852,7 +857,22 @@ class PhysicalLayer(object):
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def get_doppler(self, iq_samples):
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"""quality check and doppler estimation for preamble"""
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print('get_doppler', len(iq_samples))
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success,doppler = True,0
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sps = self._sps
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wlen = self._wlen
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_,zp = self.get_preamble_z()
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cc = np.correlate(iq_samples, zp)
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imax = np.argmax(np.abs(cc[0:wlen*sps]))
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idx = np.arange(wlen*sps)
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print('get_doppler ccmax: ', imax, np.abs(cc[imax]))
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pks = [np.correlate(iq_samples[imax+i*wlen*sps+idx],
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zp[i*wlen*sps+idx])[0]
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for i in range((len(iq_samples)-imax) // (wlen*sps))]
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print('get_doppler pks: ', pks, np.angle(pks))
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doppler = common.freq_est(pks)/(wlen*sps)
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print('get_doppler doppler: ', doppler)
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## TODO
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return success,doppler
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@ -901,7 +921,7 @@ class PhysicalLayer(object):
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success = np.all(b[0:3] == 0)
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b = np.flip(b)
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self._wid = wid = np.packbits(b[0:4])[0]>>4
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self._intl_type = INTERLEAVERS[np.packbits(b[4:6])[0]>>6]
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self._intl_type = 'L'# INTERLEAVERS[np.packbits(b[4:6])[0]>>6]
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self._constraint_length = 'K=7' if b[6] == 0 else 'K=9'
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self._data_mode = WID_MODE[self._wid]
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print('WID:', self._wid, self._intl_type, self._constraint_length,self._data_mode)
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@ -960,12 +980,12 @@ class PhysicalLayer(object):
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def decode_soft_dec(self, soft_dec):
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print('decode_soft_dec', len(soft_dec), soft_dec.dtype)
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interleaver_is_full = False
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if self._wid == MODE_WALSH: ## TODO
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if self._wid == MODE_WALSH: ## TODO: WALSH16 decoding for BW > 30kHz
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n = len(soft_dec) // 32
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soft_bits = np.zeros(2*n, dtype=np.float32)
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for i in range(n):
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w = np.sum(soft_dec[32*i:32*(i+1)].reshape(4,8),0)
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b = FROM_WALSH4[np.packbits(w[0:4]>0)[0]]
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b = FROM_WALSH4[np.packbits(w[0:4]>0)[0]] ## TODO use 2nd half of WALSH bits
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print('WALSH', i, w, b)
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abs_soft_dec = np.mean(np.abs(w))
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soft_bits[2*i] = abs_soft_dec*(2*(b>>1)-1)
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