## -*- python -*- from __future__ import print_function import numpy as np from common import * ## ---- Walsh-4 codes ----------------------------------------------------------- WALSH = np.array([[0,0,0,0, 0,0,0,0], [0,1,0,1, 0,1,0,1], [0,0,1,1, 0,0,1,1], [0,1,1,0, 0,1,1,0], [0,0,0,0, 1,1,1,1], [0,1,0,1, 1,0,1,0], [0,0,1,1, 1,1,0,0], [0,1,1,0, 1,0,0,1]], dtype=np.uint8) FROM_WALSH = -np.ones(256, dtype=np.int8) for i in range(8): FROM_WALSH[np.packbits(WALSH[i][:])[0]] = i ## ---- tri-bit codes ----------------------------------------------------------- TRIBIT = np.zeros((8,32), dtype=np.uint8) for i in range(8): TRIBIT[i][:] = np.concatenate([WALSH[i][:] for j in range(4)]) ## ---- tri-bit scramble sequence for preamble ---------------------------------- TRIBIT_SCRAMBLE = np.array( [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], dtype=np.uint8) ## ---- preamble symbols --------------------------------------------------------- D1=D2=C1=C2=C3=0 ## not known PRE_SYMBOLS = n_psk(2, np.concatenate( [TRIBIT[i][:] for i in [0,1,3,0,1,3,1,2,0,D1,D2,C1,C2,C3,0]])) PRE_SYMBOLS[9*32:14*32] = 0 ## ---- preamble scramble symbols ------------------------------------------------ PRE_SCRAMBLE = n_psk(8, np.concatenate([TRIBIT_SCRAMBLE for _ in range(15)])) ## ---- data scrambler ----------------------------------------------------------- class ScrambleData(object): """data scrambling sequence generator""" def __init__(self): self.reset() def reset(self): self._state = 0xBAD self._counter = 0 def next(self): if self._counter == 160: self.reset() for _ in range(8): self._advance() self._counter += 1 return self._state&7 def _advance(self): msb = self._state>>11 self._state = (self._state<<1)&4095 if msb: self._state ^= 0x053 return self._state ## ---- constellatios ----------------------------------------------------------- BPSK=np.array(zip(np.exp(2j*np.pi*np.arange(2)/2), [0,1]), CONST_DTYPE) QPSK=np.array(zip(np.exp(2j*np.pi*np.arange(4)/4), [0,1,3,2]), CONST_DTYPE) PSK8=np.array(zip(np.exp(2j*np.pi*np.arange(8)/8), [0,1,3,2,7,6,4,5]), CONST_DTYPE) ## ---- constellation indices --------------------------------------------------- MODE_BPSK=0 MODE_QPSK=1 MODE_8PSK=2 ## ---- mode definitions -------------------------------------------------------- MODE = [[{} for _ in range(8)] for _ in range(8)] MODE[7][6] = {'bit_rate':4800, 'ci':MODE_8PSK, 'interleaver':['N', 1, 1], 'unknown':32,'known':16, 'nsymb': 1, 'coding_rate': -1 } MODE[7][7] = {'bit_rate':2400, 'ci':MODE_8PSK, 'interleaver':['N', 1, 1], 'unknown':32,'known':16, 'nsymb': 1, 'coding_rate':1./2} MODE[6][4] = {'bit_rate':2400, 'ci':MODE_8PSK, 'interleaver':['S', 40, 72], 'unknown':32,'known':16, 'nsymb': 1, 'coding_rate':1./2} MODE[4][4] = {'bit_rate':2400, 'ci':MODE_8PSK, 'interleaver':['L', 40,576], 'unknown':32,'known':16, 'nsymb': 1, 'coding_rate':1./2} MODE[6][5] = {'bit_rate':1200, 'ci':MODE_QPSK, 'interleaver':['S', 40, 36], 'unknown':20,'known':20, 'nsymb': 1, 'coding_rate':1./2} MODE[4][5] = {'bit_rate':1200, 'ci':MODE_QPSK, 'interleaver':['L', 40,288], 'unknown':20,'known':20, 'nsymb': 1, 'coding_rate':1./2} MODE[6][6] = {'bit_rate': 600, 'ci':MODE_BPSK, 'interleaver':['S', 40, 18], 'unknown':20,'known':20, 'nsymb': 1, 'coding_rate':1./2} MODE[4][6] = {'bit_rate': 600, 'ci':MODE_BPSK, 'interleaver':['L', 40,144], 'unknown':20,'known':20, 'nsymb': 1, 'coding_rate':1./2} MODE[6][7] = {'bit_rate': 300, 'ci':MODE_BPSK, 'interleaver':['S', 40, 18], 'unknown':20,'known':20, 'nsymb': 1, 'coding_rate':1./4} MODE[4][7] = {'bit_rate': 300, 'ci':MODE_BPSK, 'interleaver':['L', 40,144], 'unknown':20,'known':20, 'nsymb': 1, 'coding_rate':1./4} MODE[7][4] = {'bit_rate': 150, 'ci':MODE_BPSK, 'interleaver':['S', 40, 18], 'unknown':20,'known':20, 'nsymb': 1, 'coding_rate':1./8} MODE[5][4] = {'bit_rate': 150, 'ci':MODE_BPSK, 'interleaver':['L', 40,144], 'unknown':20,'known':20, 'nsymb': 1, 'coding_rate':1./8} MODE[7][5] = {'bit_rate': 75, 'ci':MODE_QPSK, 'interleaver':['S', 10, 9], 'unknown':-1,'known': 0, 'nsymb':32, 'coding_rate':1./2} MODE[5][4] = {'bit_rate': 75, 'ci':MODE_QPSK, 'interleaver':['L', 20, 36], 'unknown':-1,'known': 0, 'nsymb':32, 'coding_rate':1./2} ## ---- physcal layer class ----------------------------------------------------- class PhysicalLayer(object): """Physical layer description for MIL-STD-188-110 Appendix A""" def __init__(self, sps): """intialization""" self._sps = sps self._frame_counter = -1 self._constellations = [BPSK, QPSK, PSK8] self._preamble = self.get_preamble() self._pre_counter = -1 self._d1d2 = [-1,-1] ## D1,D2 self._mode = {} self._scr_data = ScrambleData() def get_constellations(self): return self._constellations def get_frame(self): """returns a tuple describing the frame: [0] ... known+unknown symbols and scrambling [1] ... modulation type after descrambling [2] ... a boolean indicating whethere or not raw IQ samples needed [3] ... a boolean indicating if the soft decision for the unknown 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] ## ----- data frame ------ if self._frame_counter == self._num_frames_per_block: self._frame_counter = 0 scramble_for_frame = n_psk(8, np.array([self._scr_data.next() for _ in range(self._frame_len)])) a = np.array(zip(scramble_for_frame, scramble_for_frame), dtype=[('symb', np.complex64), ('scramble', np.complex64)]) n_unknown = self._mode['unknown'] 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]][:]) self._frame_counter += 1 return [a, self._mode['ci'],False,True] def get_doppler(self, symbols, iq_samples): """returns a tuple [0] ... quality flag [1] ... doppler estimate (rad/symbol) if available""" print('-------------------- get_doppler --------------------', self._frame_counter,len(symbols),len(iq_samples)) success,doppler = False,0 if self._frame_counter == -1: ## -- preamble ---- success,doppler = self.get_doppler_from_preamble(symbols, iq_samples) if len(symbols) != 0: 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 = -1 self._pre_counter = -1 return success,doppler def get_doppler_from_preamble(self, symbols, iq_samples): """quality check and doppler estimation for preamble""" success,doppler = True,0 if len(iq_samples) != 0: sps = self._sps zp = np.array([z for z in PhysicalLayer.get_preamble()['symb'] for _ in range(sps)], dtype=np.complex64) ## find starting point cc = np.correlate(iq_samples, zp[0:3*32*sps]) imax = np.argmax(np.abs(cc[0:2*32*sps])) pks = cc[(imax, imax+3*32*sps),] tpks = cc[imax+3*16*sps:imax+5*16*sps] print('imax=', imax, 'apks=',np.abs(pks), np.mean(np.abs(pks)), np.mean(np.abs(tpks)), np.abs(tpks)) success = np.mean(np.abs(pks)) > 2*np.mean(np.abs(tpks)) doppler = np.diff(np.unwrap(np.angle(pks)))[0]/(3*32) if success else 0 if success: idx = np.arange(32*sps) pks = [np.correlate(iq_samples[imax+i*32*sps+idx], zp[i*32*sps+idx])[0] for i in range(9)] doppler = freq_est(pks)/32 print('success=', success, 'doppler=', doppler) return success,doppler def decode_preamble(self, symbols): data = [FROM_WALSH[np.packbits (np.real (np.sum (symbols[i:i+32].reshape((4,8)),0))<0)[0]] for i in range(0,15*32,32)] print('data=',data) self._pre_counter = sum([(x&3)*(1<<2*y) for (x,y) in zip(data[11:14][::-1], range(3))]) 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""" return np.array(zip(PRE_SCRAMBLE*PRE_SYMBOLS, PRE_SCRAMBLE), dtype=[('symb', np.complex64), ('scramble', np.complex64)]) @staticmethod def get_preamble_z(sps): """preamble symbols for preamble correlation""" a = PhysicalLayer.get_preamble() return np.array([z for z in a['symb'][0:32*3] for _ in range(sps)]) if __name__ == '__main__': def gen_data_scramble(): def advance(s): msb = s>>11 s = (s<<1)&((1<<12)-1) if msb: s ^= 0x053 return s a = np.zeros(160, dtype=np.uint8) s = 0xBAD for i in range(160): for _ in range(8): s = advance(s) a[i] = s&7; return a p=PhysicalLayer(5) z1=np.array([x for x in PRE_SYMBOLS for _ in range(5)]) z2=np.array([x for x in PRE_SCRAMBLE for _ in range(5)]) z=z1*z2 for i in range(3): print(i, all(z[32*5*i:32*5*(i+1)] == z[32*5*(3+i):32*5*(3+i+1)])) print(np.sum(np.sum(z[0:32*5] * np.conj(z[32*5*3:32*5*4])))) print(WALSH[1][:]) print(sum(WALSH[1][:]*(1<