mirror of
https://github.com/hb9fxq/gr-digitalhf
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209 lines
9.9 KiB
Python
209 lines
9.9 KiB
Python
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## -*- python -*-
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from __future__ import print_function
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import numpy as np
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import common
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PREAMBLE = common.n_psk(8, np.array([ 2,6,4,4,6,4,6,2,6,0 # 1
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,2,2,0,4,6,2,2,0,2,6 # 2
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,0,6,4,0,2,0,6,6,6,4 # 3
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,0,6,0,6,2,2,0,4,2,4 # 4
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,0,2,2,2,2,6,4,6,6,2 # 5
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,4,6,4,6,2,6,0,2,4,0 # 6
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,0,0,6,6,2,6,2,2,0,2 # 7
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,4,4,6,4,6,0,4,0,6,6 # 8
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,2,2,0,0,6,6,4,0,4,0 # 9
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,0,6,6,6,4,6,4,6,0,2 # 10
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,2,6,0,0,0,2,6,2,0,0 # 11
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,6,2,6,0,4,6,6,4,0,6 # 12
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,2,6,2,4,4,2,0,6,2,6 # 13
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,0,0,4,2,4,0,6,0,4,4 # 14
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,2,2,6,0,2,2,0,6,4,2 # 15
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,2,4,0,6,0,4,6,4,0,2 # 16
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,2,0,2,2,2,2,4,4,0,2 # 17
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,6,2,2,4,6,6,6,2,6,4 # 18
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,2,0,0,0,2,2,4,0,0,6 # 19
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,6,4,2,0,0,0,0,2,0,4 # 20
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,2,2,4])) ## 203 symbols
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## ---- constellatios -----------------------------------------------------------
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BPSK=np.array(zip(np.exp(2j*np.pi*np.arange(2)/2), [0,1]), common.CONST_DTYPE)
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QPSK=np.array(zip(np.exp(2j*np.pi*np.arange(4)/4), [0,1,3,2]), common.CONST_DTYPE)
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PSK8=np.array(zip(np.exp(2j*np.pi*np.arange(8)/8), [0,1,3,2,6,7,5,4]), common.CONST_DTYPE)
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## ---- constellation indices ---------------------------------------------------
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MODE_BPSK=0
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MODE_QPSK=1
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MODE_8PSK=2
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## ---- physcal layer class -----------------------------------------------------
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class PhysicalLayer(object):
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"""Physical layer description for STANAG 4539 Appendix D"""
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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 = -1
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self._constellations = [BPSK, QPSK, PSK8]
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self._preamble = self.get_preamble()
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self._mode = {}
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self._mode_description = 'UNKNOWN'
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def get_constellations(self):
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return self._constellations
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def get_next_frame(self, symbols):
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"""returns a tuple describing the frame:
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[0] ... known+unknown symbols and scrambling
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[1] ... modulation type after descrambling
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[2] ... a boolean indicating if the processing should continue
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[3] ... a boolean indicating if the soft decision for the unknown
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symbols are saved"""
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print('-------------------- get_frame --------------------',
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self._frame_counter, len(symbols))
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success = True
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if self._frame_counter == -1: ## preamble mode
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if len(symbols) == 0:
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return [self._preamble,MODE_BPSK,success,False]
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else:
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success = self.decode_preamble(symbols)
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return [self._preamble,MODE_BPSK,success,False]
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# else: ## data mode
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# self._frame_counter += 1
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# ##print('test:', symbols[self._mode['unknown']:], np.mean(np.real(symbols[self._mode['unknown']:])))
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# if self._mode['known'] == 0: ## orthogonal WALSH modulation
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# success = True
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# for i in range(5):
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# a = symbols[32*i:32*(i+1)]
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# success &= np.max(np.imag(np.mean(a.reshape(8,4),0))) < 0.25
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# elif self._frame_counter < self._num_frames_per_block-2:
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# success = np.mean(np.real(symbols[self._mode['unknown']:])) > 0.4 or np.max(np.imag(symbols[self._mode['unknown']:])) < 0.6
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# if not success:
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# print('aborting: ', symbols[self._mode['unknown']:])# np.mean(np.real(symbols[self._mode['unknown']:])),
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# #np.max(np.imag(symbols[self._mode['unknown']:])))
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# return [self.get_next_data_frame(success),self._mode['ci'],success,success]
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def get_next_data_frame(self, success):
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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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# scramble_for_frame = common.n_psk(8, np.array([self._scr_data.next()
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# for _ in range(self._frame_len)]))
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# a = common.make_scr(scramble_for_frame, scramble_for_frame)
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# n_unknown = self._mode['unknown']
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# a['symb'][0:n_unknown] = 0
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# if self._mode['known'] != 0 and 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] *= common.n_psk(2, WALSH8[self._d1d2[idx_d1d2]][:])
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# a['symb'][n_unknown+8:n_unknown+16] *= common.n_psk(2, WALSH8[self._d1d2[idx_d1d2]][:])
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# if not success:
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# self._frame_counter = -1
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# self._pre_counter = -1
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# return a
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return False
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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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r = {'success': False, ## -- quality flag
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'use_amp_est': False, ##self._frame_counter < 0,
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'doppler': 0} ## -- doppler estimate (rad/symb)
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if len(iq_samples) != 0:
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sps = self._sps
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## find starting point
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_,zp = self.get_preamble_z()
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cc = np.correlate(iq_samples, zp[0:40*sps])
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imax = np.argmax(np.abs(cc[0:20*sps]))
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print('imax=', imax, len(iq_samples), len(cc))
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apk = np.abs(cc[imax])
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tpk = np.abs(cc[imax+20*sps])
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print('imax=', imax, 'apk=', apk, 'tpk=', tpk)
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r['success'] = np.bool(apk > 2*tpk)
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if r['success']:
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idx = np.arange(40*sps)
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pks = [np.vdot(zp[ i*40*sps+idx],
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iq_samples[imax+i*40*sps+idx])
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for i in range(4)]
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r['doppler'] = common.freq_est(pks)/(40*sps)
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print('success=', r['success'], 'doppler=', r['doppler'],
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np.abs(np.array(pks)),
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np.angle(np.array(pks)))
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return r
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def decode_preamble(self, symbols):
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print('decode_preamble', symbols)
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mean_symb = np.mean(symbols[-40:])
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success = np.real(mean_symb) > 0.6
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print('decode_preamble', mean_symb, success)
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return success
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# data = [FROM_WALSH8[np.packbits
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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)[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([(x&3)*(1<<2*y) for (x,y) in zip(data[11:14][::-1], range(3))])
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# self._d1d2 = data[9:11]
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# print('MODE:', data[9:11])
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# self._mode = mode = MODE[data[9]][data[10]]
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# self._block_len = 11520 if mode['interleaver'][0] == 'L' else 1440
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# self._frame_len = mode['known'] + mode['unknown']
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# if mode['known'] == 0: ## orthogonal WALSH modulation
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# self._num_frames_per_block = mode['interleaver'][1]*mode['interleaver'][2]/2*32/160
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# else:
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# self._num_frames_per_block = self._block_len/self._frame_len
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# self._deinterleaver = Deinterleaver(mode['interleaver'][1], mode['interleaver'][2])
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# self._depuncturer = common.Depuncturer(repeat=mode['repeat'])
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# self._viterbi_decoder = viterbi27(0x6d, 0x4f)
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# self._mode_description = 'MIL_STD_188-110A: (%d,%d) %dbps intl=%s [U=%d,K=%d]' % (data[9],data[10],
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# mode['bit_rate'],
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# mode['interleaver'][0],
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# mode['unknown'], mode['known'])
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# print(self._d1d2, mode, self._frame_len, self._mode_description)
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def set_mode(self, _):
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pass
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def get_mode(self):
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return self._mode_description
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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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if self._mode['known'] == 0: ## orthogonal WALSH modulation
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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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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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soft_bits[2*i+1] = abs_soft_dec*(2*(b &1)-1)
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print('WALSH soft_bits=', soft_bits)
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r = self._deinterleaver.load(soft_bits)
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else:
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r = self._deinterleaver.load(soft_dec)
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print('decode_soft_dec r=', r.shape)
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if r.shape[0] == 0:
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return [],0.0
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##print('deinterleaved bits: ', [x for x in 1*(r>0)])
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rd = self._depuncturer.process(r)
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self._viterbi_decoder.reset()
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decoded_bits = self._viterbi_decoder.udpate(rd)
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##print('bits=', decoded_bits)
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quality = 100.0*self._viterbi_decoder.quality()/(2*len(decoded_bits))
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print('quality={}%'.format(quality))
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return decoded_bits,quality
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@staticmethod
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def get_preamble():
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"""preamble symbols + scrambler"""
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return common.make_scr(PREAMBLE,PREAMBLE)
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def get_preamble_z(self):
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"""preamble symbols for preamble correlation"""
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a = PhysicalLayer.get_preamble()
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return 1,np.array([z for z in a['symb']
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for _ in range(self._sps)])
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if __name__ == '__main__':
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print(PREAMBLE)
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