gr-digitalhf/python/physical_layer/STANAG_4285.py

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## -*- python -*-
import numpy as np
class PhysicalLayer(object):
"""Physical layer description for STANAG 4285"""
MODE_BPSK=0
MODE_QPSK=1
MODE_8PSK=2
def __init__(self, sps):
"""intialization"""
self._sps = sps
self._mode = self.MODE_BPSK
self._frame_counter = 0
self._is_first_frame = True
self._constellations = [self.make_psk(2, [0,1]),
self.make_psk(4, [0,1,3,2]),
self.make_psk(8, [1,0,2,3,6,7,5,4])]
self._preamble = self.get_preamble()
self._data = self.get_data()
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def set_mode(self, mode):
"""set phase modultation type"""
print('set_mode', mode)
self._mode = int(mode)
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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
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[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._frame_counter)
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return [self._preamble,self.MODE_BPSK,True,False] if self.is_preamble() else [self._data,self._mode,False,True]
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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 = self.quality_preamble(symbols,iq_samples) if self.is_preamble() else self.quality_data(symbols)
if len(symbols) != 0:
self._frame_counter = (self._frame_counter+1)&1 if success else 0
self._is_first_frame = not success
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return success,doppler
def is_preamble(self):
return self._frame_counter == 0
def quality_preamble(self, symbols, iq_samples):
"""quality check and doppler estimation for preamble"""
success = True
doppler = 0
if len(iq_samples) != 0:
sps = self._sps
zp = np.array([x for x in self._preamble['symb'][9:40]
for _ in range(sps)], dtype=np.complex64)
cc = np.correlate(iq_samples, zp)
imax = np.argmax(np.abs(cc[0:18*sps]))
pks = cc[(imax,imax+31*sps),]
tpks = cc[imax+15*sps:imax+16*sps]
success = np.mean(np.abs(pks)) > 2*np.mean(np.abs(tpks))
doppler = np.diff(np.unwrap(np.angle(pks)))[0]/31 if success else 0
if len(symbols) != 0:
idx = range(30,80) if self._is_first_frame else range(80)
z = symbols[idx]*np.conj(self._preamble['symb'][idx])
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print('quality_preamble',np.sum(np.real(z)<0))
success = np.sum(np.real(z)<0) < 30
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return success,doppler
def quality_data(self, s):
"""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
return success,0 ## no doppler estimate for data frames
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@staticmethod
def get_preamble():
"""preamble symbols + scrambler(=1)"""
state = np.array([1,1,0,1,0], dtype=np.bool)
taps = np.array([0,0,1,0,1], dtype=np.bool)
p = np.zeros(80, dtype=np.uint8)
for i in range(80):
p[i] = state[-1]
state = np.concatenate(([np.sum(state&taps)&1], state[0:-1]))
a = np.zeros(80, dtype=[('symb',np.complex64), ('scramble', np.complex64)])
## BPSK modulation
constellation = PhysicalLayer.make_psk(2,range(2))['points']
a['symb'] = constellation[p,]
a['scramble'] = 1
return a
@staticmethod
def get_data():
"""data symbols + scrambler; for unknown symbols 'symb'=0"""
state = np.array([1,1,1,1,1,1,1,1,1], dtype=np.bool)
taps = np.array([0,0,0,0,1,0,0,0,1], dtype=np.bool)
p = np.zeros(176, dtype=np.uint8)
for i in range(176):
p[i] = np.sum(state[-3:]*[4,2,1])
for _ in range(3):
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state = np.concatenate(([np.sum(state&taps)&1], state[0:-1]))
a=np.zeros(176, dtype=[('symb',np.complex64), ('scramble', np.complex64)])
## 8PSK modulation
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constellation = PhysicalLayer.make_psk(8,range(8))['points']
a['scramble'] = constellation[p,]
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known_symbols = np.mod(range(176),48)>=32
a['symb'][known_symbols] = a['scramble'][known_symbols]
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return a
@staticmethod
def make_psk(n, gray_code):
"""generates n-PSK constellation data"""
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c = np.zeros(n, dtype=[('points', np.complex64), ('symbols', np.uint8)])
c['points'] = np.exp(2*np.pi*1j*np.arange(n)/n)
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c['symbols'] = gray_code
return c