mirror of
https://github.com/hb9fxq/gr-digitalhf
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233 lines
10 KiB
Python
233 lines
10 KiB
Python
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## -*- python -*-
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from __future__ import print_function
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import numpy as np
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def n_psk(n,x):
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return np.complex64(np.exp(2j*np.pi*x/n))
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## ---- constellations -----------------------------------------------------------
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CONST_DTYPE=np.dtype([('points', np.complex64),
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('symbols', np.uint8)])
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BPSK=np.array(zip(np.exp(2j*np.pi*np.arange(2)/2), range(2)), CONST_DTYPE)
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QPSK=np.array(zip(np.exp(2j*np.pi*np.arange(4)/4), [0,1,3,2]), CONST_DTYPE)
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PSK8=np.array(zip(np.exp(2j*np.pi*np.arange(8)/8), [0,1,3,2,7,6,4,5]), CONST_DTYPE)
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QAM16=np.array(
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zip([+0.866025+0.500000j, 0.500000+0.866025j, 1.000000+0.000000j, 0.258819+0.258819j,
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-0.500000+0.866025j, 0.000000+1.000000j, -0.866025+0.500000j, -0.258819+0.258819j,
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+0.500000-0.866025j, 0.000000-1.000000j, 0.866025-0.500000j, 0.258819-0.258819j,
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-0.866025-0.500000j, -0.500000-0.866025j, -1.000000+0.000000j, -0.258819-0.258819j],
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range(16)), CONST_DTYPE)
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QAM32=np.array(
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zip([+0.866380+0.499386j, 0.984849+0.173415j, 0.499386+0.866380j, 0.173415+0.984849j,
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+0.520246+0.520246j, 0.520246+0.173415j, 0.173415+0.520246j, 0.173415+0.173415j,
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-0.866380+0.499386j, -0.984849+0.173415j, -0.499386+0.866380j, -0.173415+0.984849j,
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-0.520246+0.520246j, -0.520246+0.173415j, -0.173415+0.520246j, -0.173415+0.173415j,
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+0.866380-0.499386j, 0.984849-0.173415j, 0.499386-0.866380j, 0.173415-0.984849j,
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+0.520246-0.520246j, 0.520246-0.173415j, 0.173415-0.520246j, 0.173415-0.173415j,
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-0.866380-0.499386j, -0.984849-0.173415j, -0.499386-0.866380j, -0.173415-0.984849j,
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-0.520246-0.520246j, -0.520246-0.173415j, -0.173415-0.520246j, -0.173415-0.173415j],
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range(32)), CONST_DTYPE)
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QAM64=np.array(
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zip([+1.000000+0.000000j, 0.822878+0.568218j, 0.821137+0.152996j, 0.932897+0.360142j,
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+0.000000-1.000000j, 0.822878-0.568218j, 0.821137-0.152996j, 0.932897-0.360142j,
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+0.568218+0.822878j, 0.588429+0.588429j, 0.588429+0.117686j, 0.588429+0.353057j,
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+0.568218-0.822878j, 0.588429-0.588429j, 0.588429-0.117686j, 0.588429-0.353057j,
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+0.152996+0.821137j, 0.117686+0.588429j, 0.117686+0.117686j, 0.117686+0.353057j,
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+0.152996-0.821137j, 0.117686-0.588429j, 0.117686-0.117686j, 0.117686-0.353057j,
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+0.360142+0.932897j, 0.353057+0.588429j, 0.353057+0.117686j, 0.353057+0.353057j,
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+0.360142-0.932897j, 0.353057-0.588429j, 0.353057-0.117686j, 0.353057-0.353057j,
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+0.000000+1.000000j, -0.822878+0.568218j, -0.821137+0.152996j, -0.932897+0.360142j,
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-1.000000+0.000000j, -0.822878-0.568218j, -0.821137-0.152996j, -0.932897-0.360142j,
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-0.568218+0.822878j, -0.588429+0.588429j, -0.588429+0.117686j, -0.588429+0.353057j,
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-0.568218-0.822878j, -0.588429-0.588429j, -0.588429-0.117686j, -0.588429-0.353057j,
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-0.152996+0.821137j, -0.117686+0.588429j, -0.117686+0.117686j, -0.117686+0.353057j,
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-0.152996-0.821137j, -0.117686-0.588429j, -0.117686-0.117686j, -0.117686-0.353057j,
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-0.360142+0.932897j, -0.353057+0.588429j, -0.353057+0.117686j, -0.353057+0.353057j,
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-0.360142-0.932897j, -0.353057-0.588429j, -0.353057-0.117686j, -0.353057-0.353057j],
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range(64)), 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_16QAM = 3
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MODE_32QAM = 4
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MODE_64QAM = 5
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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 = 1
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def next(self, num_bits):
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r = self._state & ((1<<num_bits)-1)
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for i in range(num_bits):
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self._advance()
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return r
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def _advance(self):
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lsb = self._state&1
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self._state = (self._state>>1)&511
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if lsb:
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self._state ^= 0x10B
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return self._state
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## ---- preamble definitions ---------------------------------------------------
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## 184 = 8*23
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PREAMBLE=np.array(
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[1,5,1,3,6,1,3,1,1,6,3,7,7,3,5,4,3,6,6,4,5,4,0,
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2,2,2,6,0,7,5,7,4,0,7,5,7,1,6,1,0,5,2,2,6,2,3,
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6,0,0,5,1,4,2,2,2,3,4,0,6,2,7,4,3,3,7,2,0,2,6,
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4,4,1,7,6,2,0,6,2,3,6,7,4,3,6,1,3,7,4,6,5,7,2,
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0,1,1,1,4,4,0,0,5,7,7,4,7,3,5,4,1,6,5,6,6,4,6,
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3,4,3,0,7,1,3,4,7,0,1,4,3,3,3,5,1,1,1,4,6,1,0,
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6,0,1,3,1,4,1,7,7,6,3,0,0,7,2,7,2,0,2,6,1,1,1,
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2,7,7,5,3,3,6,0,5,3,3,1,0,7,1,1,0,3,0,4,0,7,3],
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dtype=np.uint8)
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## 103 = 31 + 1 + 3*13 + 1 + 31
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REINSERTED_PREAMBLE=np.array(
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[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+
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2,
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0,4,0,4,0,0,4,4,0,0,0,0,0, # + D0
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0,4,0,4,0,0,4,4,0,0,0,0,0, # + D1
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0,4,0,4,0,0,4,4,0,0,0,0,0, # + D2
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6,
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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-
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dtype=np.uint8)
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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 D = STANAG 4539"""
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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, QAM16, QAM32, QAM64]
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self._preamble = self.get_preamble()
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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_frame(self):
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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 whethere or not raw IQ samples needed
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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._frame_counter)
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## --- preamble frame ----
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if self._frame_counter == -1:
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return [self._preamble,MODE_BPSK,True,False]
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## ----- data frame ------
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if self._frame_counter == 0:
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self.a = self.make_reinserted_preamble()
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return [self.a, MODE_QPSK,False,True]
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def get_doppler(self, symbols, 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 --------------------',
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self._frame_counter,len(symbols),len(iq_samples))
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#if len(symbols)!=0:
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# print('symb=', symbols)
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success = False
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doppler = 0
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if self._frame_counter == -1: ## -- preamble ----
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success,doppler = self.get_doppler_from_preamble(symbols, iq_samples)
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if len(symbols) != 0:
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for s in symbols:
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print(s)
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self._frame_counter = 0
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else: ## ------------------------ data frame ----
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if len(symbols) != 0:
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for s in symbols:
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print(s)
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success = False
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self._frame_counter = -1
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return success,doppler
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def get_doppler_from_preamble(self, symbols, iq_samples):
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"""quality check and doppler estimation for preamble"""
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success = True
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doppler = 0
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shift=9
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if len(iq_samples) != 0:
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zp = np.conj(self.get_preamble_z(self._sps)[shift*self._sps:])
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cc = np.array([np.sum(iq_samples[i:i+23*self._sps] *
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zp[0:23*self._sps])
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for i in range(23*3*self._sps)])
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acc = np.abs(cc)
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for i in range(0,len(cc),23*self._sps):
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print('i=%3d: '%i,end='')
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for j in range(23*self._sps):
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print('%3.0f ' % acc[i+j], end='')
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print()
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imax = np.argmax(np.abs(cc[0:3*23*self._sps]))
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print(imax)
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pks = np.array([np.sum(iq_samples[(imax+23*i*self._sps):
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(imax+23*i*self._sps+23*self._sps)] *
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zp[(23*i*self._sps):
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(23*i*self._sps+23*self._sps)])
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for i in range(1,5)])
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print('doppler apks', np.abs(pks))
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print('doppler ppks', np.angle(pks), np.diff(np.unwrap(np.angle(pks)))/23)
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doppler = np.mean(np.diff(np.unwrap(np.angle(pks))))/23
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success = True
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print('success=', success, 'doppler=', doppler)
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return success,doppler
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def make_reinserted_preamble(self):
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a=np.zeros(len(REINSERTED_PREAMBLE), dtype=[('symb', np.complex64),
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('scramble', np.complex64)])
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a['symb'] = n_psk(8, REINSERTED_PREAMBLE)
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a['scramble'] = n_psk(8, REINSERTED_PREAMBLE)
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a['symb'][32:32+3*13] = 0 ## D0,D1,D2
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return a
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@staticmethod
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def get_preamble():
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"""preamble symbols + scrambler"""
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a=np.zeros(len(PREAMBLE), dtype=[('symb', np.complex64),
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('scramble', np.complex64)])
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a['symb'] = n_psk(8, PREAMBLE)
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a['scramble'] = n_psk(8, PREAMBLE)
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##a['symb'][-30:] = 0
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return a
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@staticmethod
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def get_preamble_z(sps):
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"""preamble symbols for preamble correlation"""
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a = PhysicalLayer.get_preamble()
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return np.array([z for z in a['symb'] for i in range(sps)])
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if __name__ == '__main__':
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print(PREAMBLE)
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z = n_psk(8,PREAMBLE)
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cc = [np.sum(z[0:23]*np.conj(z[23*i:23*i+23])) for i in range(6)]
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print(np.abs(cc))
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print(np.angle(cc)/np.pi*4)
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print(all(z==PhysicalLayer.get_preamble()['symb']))
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print(len(PhysicalLayer.get_preamble()['symb']))
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s = ScrambleData()
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print([s.next(1) for _ in range(511)])
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print([s.next(1) for _ in range(511)] ==
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[s.next(1) for _ in range(511)])
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print(QAM64)
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print(QAM32)
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print(QAM16)
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print(PSK8)
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print(QPSK)
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print(BPSK)
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