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gr-digitalhf/python/physical_layer/MIL_STD_188_110C.py

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## -*- python -*-
from __future__ import print_function
import numpy as np
from common import *
## ---- constellations -----------------------------------------------------------
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)
QAM16=np.array(
zip([+0.866025+0.500000j, 0.500000+0.866025j, 1.000000+0.000000j, 0.258819+0.258819j,
-0.500000+0.866025j, 0.000000+1.000000j, -0.866025+0.500000j, -0.258819+0.258819j,
+0.500000-0.866025j, 0.000000-1.000000j, 0.866025-0.500000j, 0.258819-0.258819j,
-0.866025-0.500000j, -0.500000-0.866025j, -1.000000+0.000000j, -0.258819-0.258819j],
range(16)), CONST_DTYPE)
QAM32=np.array(
zip([+0.866380+0.499386j, 0.984849+0.173415j, 0.499386+0.866380j, 0.173415+0.984849j,
+0.520246+0.520246j, 0.520246+0.173415j, 0.173415+0.520246j, 0.173415+0.173415j,
-0.866380+0.499386j, -0.984849+0.173415j, -0.499386+0.866380j, -0.173415+0.984849j,
-0.520246+0.520246j, -0.520246+0.173415j, -0.173415+0.520246j, -0.173415+0.173415j,
+0.866380-0.499386j, 0.984849-0.173415j, 0.499386-0.866380j, 0.173415-0.984849j,
+0.520246-0.520246j, 0.520246-0.173415j, 0.173415-0.520246j, 0.173415-0.173415j,
-0.866380-0.499386j, -0.984849-0.173415j, -0.499386-0.866380j, -0.173415-0.984849j,
-0.520246-0.520246j, -0.520246-0.173415j, -0.173415-0.520246j, -0.173415-0.173415j],
range(32)), CONST_DTYPE)
QAM64=np.array(
zip([+1.000000+0.000000j, 0.822878+0.568218j, 0.821137+0.152996j, 0.932897+0.360142j,
+0.000000-1.000000j, 0.822878-0.568218j, 0.821137-0.152996j, 0.932897-0.360142j,
+0.568218+0.822878j, 0.588429+0.588429j, 0.588429+0.117686j, 0.588429+0.353057j,
+0.568218-0.822878j, 0.588429-0.588429j, 0.588429-0.117686j, 0.588429-0.353057j,
+0.152996+0.821137j, 0.117686+0.588429j, 0.117686+0.117686j, 0.117686+0.353057j,
+0.152996-0.821137j, 0.117686-0.588429j, 0.117686-0.117686j, 0.117686-0.353057j,
+0.360142+0.932897j, 0.353057+0.588429j, 0.353057+0.117686j, 0.353057+0.353057j,
+0.360142-0.932897j, 0.353057-0.588429j, 0.353057-0.117686j, 0.353057-0.353057j,
+0.000000+1.000000j, -0.822878+0.568218j, -0.821137+0.152996j, -0.932897+0.360142j,
-1.000000+0.000000j, -0.822878-0.568218j, -0.821137-0.152996j, -0.932897-0.360142j,
-0.568218+0.822878j, -0.588429+0.588429j, -0.588429+0.117686j, -0.588429+0.353057j,
-0.568218-0.822878j, -0.588429-0.588429j, -0.588429-0.117686j, -0.588429-0.353057j,
-0.152996+0.821137j, -0.117686+0.588429j, -0.117686+0.117686j, -0.117686+0.353057j,
-0.152996-0.821137j, -0.117686-0.588429j, -0.117686-0.117686j, -0.117686-0.353057j,
-0.360142+0.932897j, -0.353057+0.588429j, -0.353057+0.117686j, -0.353057+0.353057j,
-0.360142-0.932897j, -0.353057-0.588429j, -0.353057-0.117686j, -0.353057-0.353057j],
range(64)), CONST_DTYPE)
## for test
QAM64 = QAM64[(7,3,24,56,35,39,60,28),]
QAM64['symbols'] = [1, 0, 2, 6, 4, 5, 7, 3]
## ---- constellation indices ---------------------------------------------------
MODE_BPSK = 0
MODE_QPSK = 1
MODE_8PSK = 2
MODE_16QAM = 3
MODE_32QAM = 4
MODE_64QAM = 5
## ---- data scrambler -----------------------------------------------------------
class ScrambleData(object):
"""data scrambling sequence generator"""
def __init__(self):
self.reset()
def reset(self):
self._state = np.array([0,0,0,0,0,0,0,0,1], dtype=np.bool)
self._taps = np.array([0,0,0,0,1,0,0,0,1], dtype=np.bool)
def next(self, num_bits):
r = np.packbits(self._state[1:])[0]&((1<<num_bits)-1)
for _ in range(num_bits):
self._advance()
return r
def _advance(self):
self._state = np.concatenate(([np.sum(self._state&self._taps)&1],
self._state[0:-1]))
## ---- preamble definitions ---------------------------------------------------
## 184 = 8*23
PREAMBLE=n_psk(8, np.array(
[1,5,1,3,6,1,3,1,1,6,3,7,7,3,5,4,3,6,6,4,5,4,0,
2,2,2,6,0,7,5,7,4,0,7,5,7,1,6,1,0,5,2,2,6,2,3,
6,0,0,5,1,4,2,2,2,3,4,0,6,2,7,4,3,3,7,2,0,2,6,
4,4,1,7,6,2,0,6,2,3,6,7,4,3,6,1,3,7,4,6,5,7,2,
0,1,1,1,4,4,0,0,5,7,7,4,7,3,5,4,1,6,5,6,6,4,6,
3,4,3,0,7,1,3,4,7,0,1,4,3,3,3,5,1,1,1,4,6,1,0,
6,0,1,3,1,4,1,7,7,6,3,0,0,7,2,7,2,0,2,6,1,1,1,
2,7,7,5,3,3,6,0,5,3,3,1,0,7,1,1,0,3,0,4,0,7,3]))
## 103 = 31 + 1 + 3*13 + 1 + 31
REINSERTED_PREAMBLE=n_psk(8, np.array(
[0,0,0,0,0,2,4,6,0,4,0,4,0,6,4,2,0,0,0,0,0,2,4,6,0,4,0,4,0,6,4, ## MP+
2,
0,4,0,4,0,0,4,4,0,0,0,0,0, # + D0
0,4,0,4,0,0,4,4,0,0,0,0,0, # + D1
0,4,0,4,0,0,4,4,0,0,0,0,0, # + D2
6,
4,4,4,4,4,6,0,2,4,0,4,0,4,2,0,6,4,4,4,4,4,6,0,2,4,0,4,0,4,2,0])) ## MP-
## length 31
MINI_PROBE_PLUS=n_psk(8, np.array(
[0,0,0,0,0,2,4,6,0,4,0,4,0,6,4,2,0,0,0,0,0,2,4,6,0,4,0,4,0,6,4]))
## length 31
MINI_PROBE_MINUS=n_psk(8, np.array(
[4,4,4,4,4,6,0,2,4,0,4,0,4,2,0,6,4,4,4,4,4,6,0,2,4,0,4,0,4,2,0]))
## ---- physcal layer class -----------------------------------------------------
class PhysicalLayer(object):
"""Physical layer description for MIL-STD-188-110 Appendix C = STANAG 4539"""
def __init__(self, sps):
"""intialization"""
self._sps = sps
self._frame_counter = -1
self._constellations = [BPSK, QPSK, PSK8, QAM16, QAM32, QAM64]
self._preamble = self.get_preamble()
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._frame_counter)
## --- preamble frame ----
if self._frame_counter == -1:
return [self._preamble,MODE_BPSK,True,False]
## ----- (re)inserted preamble ------
if self._frame_counter == 0:
self.a = self.make_reinserted_preamble()
return [self.a, MODE_QPSK,False,False]
if self._frame_counter >= 1:
self.a = self.make_data_frame()
return [self.a, MODE_64QAM,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))
#if len(symbols)!=0:
# print('symb=', symbols)
success,doppler = False,0
if self._frame_counter == -1: ## -- preamble ----
success,doppler = self.get_doppler_from_preamble(symbols, iq_samples)
if len(symbols) != 0:
for s in symbols:
print(s)
self._frame_counter = 0
elif self._frame_counter >= 0 and self._frame_counter <5: ## -- reinserted preamble ----
for s in symbols:
print(s)
self._frame_counter += 1
success = True
else: ## ------------------------ data frame ----
for s in symbols:
print(s)
success = False
self._frame_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
idx = np.arange(23*sps)
zp = self.get_preamble_z(self._sps)
cc = np.correlate(iq_samples, zp[idx])
imax = np.argmax(np.abs(cc[0:23*sps]))
pks = [np.correlate(iq_samples[imax+i*23*sps+idx],
zp[i*23*sps+idx])[0]
for i in range(7)]
success = np.mean(np.abs(pks)) > 2*np.mean(np.abs(cc[imax+11*sps+range(-sps,sps)]))
print('test:',imax, np.mean(np.abs(pks)), np.mean(np.abs(cc[imax+11*sps+range(-sps,sps)])))
if success:
print('doppler apks', np.abs(pks))
print('doppler ppks', np.angle(pks),
np.diff(np.unwrap(np.angle(pks)))/23,
np.mean(np.diff(np.unwrap(np.angle(pks)))/23))
doppler = freq_est(pks[1:])/23;
print('success=', success, 'doppler=', doppler)
return success,doppler
def make_reinserted_preamble(self):
a=np.array(zip(REINSERTED_PREAMBLE,
REINSERTED_PREAMBLE),
dtype=[('symb', np.complex64),
('scramble', np.complex64)])
a['symb'][32:32+3*13] = 0 ## D0,D1,D2
return a
def make_data_frame(self):
a=np.zeros(256+31, dtype=[('symb', np.complex64),
('scramble', np.complex64)])
a['scramble'] = 1
a['symb'][256:] = MINI_PROBE_MINUS
a['scramble'][256:] = MINI_PROBE_MINUS
return a
@staticmethod
def get_preamble():
"""preamble symbols + scrambler"""
return np.array(zip(PREAMBLE,
PREAMBLE),
dtype=[('symb', np.complex64),
('scramble', np.complex64)])
@staticmethod
def get_preamble_z(sps):
"""preamble symbols for preamble correlation"""
return np.array([z for z in PREAMBLE for _ in range(sps)])
if __name__ == '__main__':
print(PREAMBLE)
z = n_psk(8,PREAMBLE)
cc = [np.sum(z[0:23]*np.conj(z[23*i:23*i+23])) for i in range(6)]
print(np.abs(cc))
print(np.angle(cc)/np.pi*4)
print(all(z==PhysicalLayer.get_preamble()['symb']))
print(len(PhysicalLayer.get_preamble()['symb']))
s = ScrambleData()
print([s.next(1) for _ in range(511)])
print([s.next(1) for _ in range(511)] ==
[s.next(1) for _ in range(511)])
#print(QAM64)
#print(QAM32)
#print(QAM16)
#print(PSK8)
#print(QPSK)
#print(BPSK)
#print(MINI_PROBE_PLUS)
#print(MINI_PROBE_MINUS)
#print(MINI_PROBE_PLUS*MINI_PROBE_MINUS)
#for i in range(len(QAM64)):
# print(QAM64['points'][i])
print([s.next(6) for _ in range(256)])
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