mirror of
https://github.com/hb9fxq/gr-digitalhf
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342 lines
16 KiB
Python
342 lines
16 KiB
Python
## -*- 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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from digitalhf.digitalhf_swig import viterbi27
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## ---- Walsh-8 codes -----------------------------------------------------------
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WALSH8 = np.array([[0,0,0,0, 0,0,0,0], # 0 - 000
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[0,1,0,1, 0,1,0,1], # 1 - 001
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[0,0,1,1, 0,0,1,1], # 2 - 010
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[0,1,1,0, 0,1,1,0], # 3 - 011
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[0,0,0,0, 1,1,1,1], # 4 - 100
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[0,1,0,1, 1,0,1,0], # 5 - 010
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[0,0,1,1, 1,1,0,0], # 6 - 011
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[0,1,1,0, 1,0,0,1]], # 7 - 111
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dtype=np.uint8)
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FROM_WALSH8 = -np.ones(256, dtype=np.int8)
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for i in range(8):
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FROM_WALSH8[np.packbits(WALSH8[i][:])[0]] = i
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## ---- Walsh-4 codes -----------------------------------------------------------
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WALSH4 = np.array([[0,0,0,0], # 0 - 00
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[0,1,0,1], # 1 - 01
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[0,1,1,0], # 3 - 11 modified gray coding!
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[0,0,1,1]], # 2 - 10 modified gray coding!
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dtype=np.uint8)
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FROM_WALSH4 = -np.ones(256, dtype=np.int8)
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for i in range(4):
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FROM_WALSH4[np.packbits(WALSH4[i][:])[0]] = i
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## ---- tri-bit codes -----------------------------------------------------------
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TRIBIT = np.zeros((8,32), dtype=np.uint8)
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for i in range(8):
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TRIBIT[i][:] = np.concatenate([WALSH8[i][:] for j in range(4)])
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## ---- tri-bit scramble sequence for preamble ----------------------------------
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TRIBIT_SCRAMBLE = np.array(
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[7,4,3,0,5,1,5,0,2,2,1,1,5,7,4,3,5,0,2,6,2,1,6,2,0,0,5,0,5,2,6,6],
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dtype=np.uint8)
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## ---- preamble symbols ---------------------------------------------------------
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D1=D2=C1=C2=C3=0 ## not known
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PRE_SYMBOLS = common.n_psk(2, np.concatenate(
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[TRIBIT[i][:] for i in [0,1,3,0,1,3,1,2,0,D1,D2,C1,C2,C3,0]]))
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PRE_SYMBOLS[9*32:14*32] = 0
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## ---- preamble scramble symbols ------------------------------------------------
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PRE_SCRAMBLE = common.n_psk(8, np.concatenate([TRIBIT_SCRAMBLE for _ in range(15)]))
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## ---- data scrambler -----------------------------------------------------------
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class ScrambleData(object):
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"""data scrambling sequence generator"""
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def __init__(self):
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self.reset()
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def reset(self):
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self._state = 0xBAD
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self._counter = 0
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def next(self):
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if self._counter == 160:
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self.reset()
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for _ in range(8):
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self._advance()
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self._counter += 1
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return self._state&7
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def _advance(self):
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msb = self._state>>11
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self._state = (self._state<<1)&4095
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if msb:
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self._state ^= 0x053
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return self._state
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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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## ---- mode definitions --------------------------------------------------------
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MODE = [[{} for _ in range(8)] for _ in range(8)]
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MODE[7][6] = {'bit_rate':4800, 'ci':MODE_8PSK, 'interleaver':['N', 1, 1], 'unknown':32,'known':16, 'nsymb': 1, 'coding_rate': 'n/a', 'repeat': 1}
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MODE[7][7] = {'bit_rate':2400, 'ci':MODE_8PSK, 'interleaver':['S', 40, 72], 'unknown':32,'known':16, 'nsymb': 1, 'coding_rate': '1/2', 'repeat': 1}
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MODE[6][4] = {'bit_rate':2400, 'ci':MODE_8PSK, 'interleaver':['S', 40, 72], 'unknown':32,'known':16, 'nsymb': 1, 'coding_rate': '1/2', 'repeat': 1}
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MODE[4][4] = {'bit_rate':2400, 'ci':MODE_8PSK, 'interleaver':['L', 40,576], 'unknown':32,'known':16, 'nsymb': 1, 'coding_rate': '1/2', 'repeat': 1}
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MODE[6][5] = {'bit_rate':1200, 'ci':MODE_QPSK, 'interleaver':['S', 40, 36], 'unknown':20,'known':20, 'nsymb': 1, 'coding_rate': '1/2', 'repeat': 1}
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MODE[4][5] = {'bit_rate':1200, 'ci':MODE_QPSK, 'interleaver':['L', 40,288], 'unknown':20,'known':20, 'nsymb': 1, 'coding_rate': '1/2', 'repeat': 1}
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MODE[6][6] = {'bit_rate': 600, 'ci':MODE_BPSK, 'interleaver':['S', 40, 18], 'unknown':20,'known':20, 'nsymb': 1, 'coding_rate': '1/2', 'repeat': 1}
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MODE[4][6] = {'bit_rate': 600, 'ci':MODE_BPSK, 'interleaver':['L', 40,144], 'unknown':20,'known':20, 'nsymb': 1, 'coding_rate': '1/2', 'repeat': 1}
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MODE[6][7] = {'bit_rate': 300, 'ci':MODE_BPSK, 'interleaver':['S', 40, 18], 'unknown':20,'known':20, 'nsymb': 1, 'coding_rate': '1/4', 'repeat': 2}
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MODE[4][7] = {'bit_rate': 300, 'ci':MODE_BPSK, 'interleaver':['L', 40,144], 'unknown':20,'known':20, 'nsymb': 1, 'coding_rate': '1/4', 'repeat': 2}
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MODE[7][4] = {'bit_rate': 150, 'ci':MODE_BPSK, 'interleaver':['S', 40, 18], 'unknown':20,'known':20, 'nsymb': 1, 'coding_rate': '1/8', 'repeat': 4}
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MODE[5][4] = {'bit_rate': 150, 'ci':MODE_BPSK, 'interleaver':['L', 40,144], 'unknown':20,'known':20, 'nsymb': 1, 'coding_rate': '1/8', 'repeat': 4}
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## 75 bps othogonal WALSH modulation
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MODE[7][5] = {'bit_rate': 75, 'ci':MODE_BPSK, 'interleaver':['S', 10, 9], 'unknown':160,'known': 0, 'nsymb':32, 'coding_rate': '1/2', 'repeat': 1}
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MODE[5][5] = {'bit_rate': 75, 'ci':MODE_BPSK, 'interleaver':['L', 20, 36], 'unknown':160,'known': 0, 'nsymb':32, 'coding_rate': '1/2', 'repeat': 1}
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## ---- deinterleaver -----------------------------------------------------------
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class Deinterleaver(object):
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"""deinterleave"""
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def __init__(self, rows, cols):
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self._a = np.zeros((rows, cols), dtype=np.float32)
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self._i = 0
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self._j = 0
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self._di = 9 if rows==40 else 7
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self._dj = -17 if rows==40 else -7
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self._buffer = np.zeros(0, dtype=np.float32)
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print('deinterleaver: ', rows, cols, self._di, self._dj)
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def fetch(self, a):
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pass
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def load(self, a):
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self._buffer = np.append(self._buffer, a)
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print('interleaver load', self._a.shape, a.shape, self._buffer.shape)
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if self._buffer.shape[0] < self._a.shape[0]:
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return np.zeros(0, dtype=np.float32)
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print('interleaver load buffer:', len(self._buffer),self._i,self._j)
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i = np.arange(self._a.shape[0])
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j = (self._j + self._dj*np.arange(self._a.shape[0])) % self._a.shape[1]
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self._a[i,j] = self._buffer[0:self._a.shape[0]]
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self._buffer = np.delete(self._buffer, i)
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self._j += 1
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print('interleaver load buffer:', len(self._buffer),self._i,self._j)
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if self._j == self._a.shape[1]:
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self._j = 0
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print('==================== interleaver is full! ====================')
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return np.concatenate([self._a[(self._di*i)%self._a.shape[0],j] for j in range(self._a.shape[1])])
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else:
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return np.zeros(0, dtype=np.float32)
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## ---- physcal layer class -----------------------------------------------------
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class PhysicalLayer(object):
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"""Physical layer description for MIL-STD-188-110 Appendix A"""
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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._pre_counter = -1
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self._d1d2 = [-1,-1] ## D1,D2
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self._mode = {}
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self._scr_data = ScrambleData()
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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._pre_counter, self._frame_counter)
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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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if self._pre_counter != 0:
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return [self._preamble,MODE_BPSK,success,False]
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else:
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self._frame_counter = 0
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self._scr_data.reset()
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return [self.get_next_data_frame(success),self._mode['ci'],success,success]
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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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def get_doppler(self, iq_samples):
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"""quality check and doppler estimation for preamble"""
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success,doppler = True,0
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if len(iq_samples) != 0:
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sps = self._sps
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zp = np.array([z for z in PhysicalLayer.get_preamble()['symb']
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for _ in range(sps)], dtype=np.complex64)
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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[0:3*32*sps])
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imax = np.argmax(np.abs(cc[0:2*32*sps]))
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print('imax=', imax, len(iq_samples), len(cc))
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apks = np.abs(cc[(imax, imax+3*32*sps),])
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tpks = np.abs(cc[imax+3*16*sps:imax+5*16*sps])
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print('imax=', imax, 'apks=',apks,
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np.mean(apks), np.mean(tpks))
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success = np.mean(apks) > 5*np.mean(tpks) and apks[0]/apks[1] > 0.5 and apks[0]/apks[1] < 2.0
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if success:
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idx = np.arange(32*sps)
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pks = [np.correlate(iq_samples[imax+i*32*sps+idx],
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zp[ i*32*sps+idx])[0]
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for i in range(9)]
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doppler = common.freq_est(pks)/(32*sps)
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print('success=', success, 'doppler=', doppler,
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np.abs(np.array(pks)),
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np.angle(np.array(pks)))
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return success,doppler
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def decode_preamble(self, symbols):
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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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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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print(self._d1d2, mode, self._frame_len)
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return True
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def set_mode(self, _):
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pass
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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 []
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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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print('quality={}%'.format(100.0*self._viterbi_decoder.quality()/(2*len(decoded_bits))))
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return decoded_bits
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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(PRE_SCRAMBLE*PRE_SYMBOLS,
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PRE_SCRAMBLE)
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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 0,np.array([z for z in a['symb'][0:3*32]
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for _ in range(self._sps)])
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if __name__ == '__main__':
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def gen_data_scramble():
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def advance(s):
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msb = s>>11
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s = (s<<1)&((1<<12)-1)
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if msb: s ^= 0x053
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return s
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a = np.zeros(160, dtype=np.uint8)
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s = 0xBAD
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for i in range(160):
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for _ in range(8): s = advance(s)
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a[i] = s&7;
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return a
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sps = 5;
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p=PhysicalLayer(sps)
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z1=np.array([x for x in PRE_SYMBOLS for _ in range(sps)])
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z2=np.array([x for x in PRE_SCRAMBLE for _ in range(sps)])
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z=z1*z2;
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_,_z=p.get_preamble_z()
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print(all(z[0:3*32*sps]==_z[0:3*32*sps]))
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for i in range(3):
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print(i, all(z[32*sps*i:32*sps*(i+1)] == z[32*sps*(3+i):32*sps*(3+i+1)]))
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#print(np.sum(np.sum(z[0:32*5] * np.conj(z[32*5*3:32*5*4]))))
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#print(WALSH8[1][:])
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#print(sum(WALSH8[1][:]*(1<<np.array(range(7,-1,-1)))))
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#print(FROM_WALSH8)
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#print(gen_data_scramble())
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s=ScrambleData()
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#print([s.next() for _ in range(160)])
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#print([s.next() for _ in range(160)])
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#print(np.round(np.angle(PRE_SYMBOLS*PRE_SCRAMBLE)/np.pi*4))
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