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https://github.com/hb9fxq/gr-digitalhf
synced 2024-12-22 07:09:59 +00:00
working prel. version of 110C mode decoder
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21f8d9e228
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@ -73,7 +73,7 @@ class ScrambleData(object):
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return r
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def _advance(self):
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self._state = np.concatenate(([np.sum(self._state&self._taps)&1],
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self._state = np.concatenate(([self._state.dot(self._taps)&1],
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self._state[0:-1]))
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## ---- preamble definitions ---------------------------------------------------
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@ -97,7 +97,8 @@ REINSERTED_PREAMBLE=common.n_psk(8, np.array(
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0,4,0,4,0,0,4,4,0,0,0,0,0, # + D2
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6,
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4,4,4,4,4,6,0,2,4,0,4,0,4,2,0,6,4,4,4,4,4,6,0,2,4,0,4,0,4,2,0])) ## MP-
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## length 31
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## length 31 mini-probes
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MINI_PROBE=[common.n_psk(8, np.array([0,0,0,0,0,2,4,6,0,4,0,4,0,6,4,2,0,0,0,0,0,2,4,6,0,4,0,4,0,6,4])), ## sign = + (0)
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common.n_psk(8, np.array([4,4,4,4,4,6,0,2,4,0,4,0,4,2,0,6,4,4,4,4,4,6,0,2,4,0,4,0,4,2,0]))] ## sign = - (1)
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@ -155,8 +156,8 @@ class DeIntl_DePunct(object):
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def __init__(self, size, incr):
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self._size = size
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self._i = 0
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self._array = np.zeros(size, dtype=np.float32)
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self._idx = np.mod(incr*np.arange(size, dtype=np.uint32), size)
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self._array = np.zeros(size, dtype=np.float64)
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self._idx = np.mod(incr*np.arange(size, dtype=np.int32), size)
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print('deinterleaver: ', size, incr, self._idx[0:100])
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def fetch(self, a):
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@ -173,6 +174,7 @@ class DeIntl_DePunct(object):
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self._i += n
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result = np.zeros(0, dtype=np.float64)
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if self._i == self._size:
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print('deinterleaver: ', self._idx[0:100])
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print('==== TEST ====', self._array)
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#tmp = np.zeros(self._size, dtype=np.float32)
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tmp = self._array[self._idx]
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@ -237,7 +239,7 @@ class PhysicalLayer(object):
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success = self.decode_reinserted_preamble(symbols)
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else:
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success = self.get_data_frame_quality(symbols)
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return [self.make_data_frame(success),self._constellation_index,success,not got_reinserted_preamble]
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return [self.make_data_frame(success),self._constellation_index,success,True]
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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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@ -278,7 +280,7 @@ class PhysicalLayer(object):
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return np.abs(np.mean(symbols[-32:])) > 0.5
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def get_data_frame_quality(self, symbols):
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print('get_data_frame_quality', symbols[-31:])
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print('get_data_frame_quality', np.mean(symbols[-31:]))
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return np.abs(np.mean(symbols[-31:])) > 0.5
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def decode_reinserted_preamble(self, symbols):
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@ -364,7 +366,7 @@ class PhysicalLayer(object):
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if r.shape[0] == 0:
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return []
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self._viterbi_decoder.reset()
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decoded_bits = self._viterbi_decoder.udpate(r)
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decoded_bits = np.roll(self._viterbi_decoder.udpate(r), 7)
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print('bits=', decoded_bits[:100])
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print('quality={}% ({},{})'.format(120.0*self._viterbi_decoder.quality()/(2*len(decoded_bits)),
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self._viterbi_decoder.quality(),
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