mirror of https://github.com/hb9fxq/gr-digitalhf
132 lines
5.1 KiB
Python
132 lines
5.1 KiB
Python
## -*- python -*-
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import numpy as np
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class PhysicalLayer(object):
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"""Physical layer description for STANAG 4285"""
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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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def __init__(self, sps):
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"""intialization"""
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self._sps = sps
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self._mode = self.MODE_QPSK
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self._frame_counter = 0
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self._is_first_frame = True
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self._constellations = [self.make_psk(2, [0,1]),
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self.make_psk(4, [0,1,3,2]),
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self.make_psk(8, [1,0,2,3,6,7,5,4])]
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self._preamble = self.get_preamble()
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self._data = self.get_data()
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def set_mode(self, mode):
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"""set phase modultation type"""
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print('set_mode', mode)
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self._mode = int(mode)
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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 symbols are saved"""
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## print('-------------------- get_frame --------------------', self._frame_counter, len(symbols))
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if len(symbols) == 0: ## 1st preamble
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self._frame_counter = 0
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success,frame_description = True,[]
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if (self._frame_counter%2) == 0:
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frame_description = [self._preamble,self.MODE_BPSK,success,False]
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else:
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idx = range(30,80)
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z = symbols[idx]*np.conj(self._preamble['symb'][idx])
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## print('quality_preamble',np.sum(np.real(z)<0), symbols[idx])
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success = np.bool(np.sum(np.real(z)<0) < 30)
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frame_description = [self._data,self._mode,success,True]
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self._frame_counter += 1
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return frame_description
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def get_doppler(self, iq_samples):
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"""returns a tuple
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[0] ... quality flag
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[1] ... doppler estimate (rad/symbol) if available"""
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## print('-------------------- get_doppler --------------------', self._frame_counter,len(iq_samples))
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success,doppler = False,0
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if len(iq_samples) == 0:
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return success,doppler
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sps = self._sps
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zp = np.array([x for x in self._preamble['symb'][9:40]
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for _ in range(sps)], dtype=np.complex64)
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cc = np.correlate(iq_samples, zp)
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imax = np.argmax(np.abs(cc[0:18*sps]))
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pks = cc[(imax,imax+31*sps),]
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tpks = cc[imax+15*sps:imax+16*sps]
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## print('doppler: ', np.abs(pks), np.abs(tpks))
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success = np.bool(np.mean(np.abs(pks)) > 5*np.mean(np.abs(tpks)))
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doppler = np.diff(np.unwrap(np.angle(pks)))[0]/31/self._sps if success else 0
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return success,doppler
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def is_preamble(self):
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return self._frame_counter == 0
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def quality_data(self, s):
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"""quality check for the data frame"""
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known_symbols = np.mod(range(176),48)>=32
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print('quality_data',np.sum(np.real(s[known_symbols])<0))
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success = np.sum(np.real(s[known_symbols])<0) < 20
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return success,0 ## no doppler estimate for data frames
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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 2,np.array([z for z in a['symb'][0:31] for _ in range(self._sps)])
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@staticmethod
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def get_preamble():
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"""preamble symbols + scrambler(=1)"""
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state = np.array([1,1,0,1,0], dtype=np.bool)
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taps = np.array([0,0,1,0,1], dtype=np.bool)
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p = np.zeros(80, dtype=np.uint8)
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for i in range(80):
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p[i] = state[-1]
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state = np.concatenate(([np.sum(state&taps)&1], state[0:-1]))
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a = np.zeros(80, dtype=[('symb',np.complex64), ('scramble', np.complex64)])
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## BPSK modulation
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constellation = PhysicalLayer.make_psk(2,range(2))['points']
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a['symb'] = constellation[p,]
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a['scramble'] = 1
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return a
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@staticmethod
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def get_data():
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"""data symbols + scrambler; for unknown symbols 'symb'=0"""
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state = np.array([1,1,1,1,1,1,1,1,1], dtype=np.bool)
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taps = np.array([0,0,0,0,1,0,0,0,1], dtype=np.bool)
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p = np.zeros(176, dtype=np.uint8)
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for i in range(176):
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p[i] = np.sum(state[-3:]*[4,2,1])
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for _ in range(3):
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state = np.concatenate(([np.sum(state&taps)&1], state[0:-1]))
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a=np.zeros(176, dtype=[('symb',np.complex64), ('scramble', np.complex64)])
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## 8PSK modulation
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constellation = PhysicalLayer.make_psk(8,range(8))['points']
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a['scramble'] = constellation[p,]
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known_symbols = np.mod(range(176),48)>=32
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a['symb'][known_symbols] = a['scramble'][known_symbols]
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return a
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@staticmethod
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def make_psk(n, gray_code):
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"""generates n-PSK constellation data"""
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c = np.zeros(n, dtype=[('points', np.complex64), ('symbols', np.int32)])
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c['points'] = np.exp(2*np.pi*1j*np.arange(n)/n)
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c['symbols'] = gray_code
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return c
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