From 6786baabd303096927f15a5f27ae4b2561371f43 Mon Sep 17 00:00:00 2001 From: cmayer Date: Sun, 31 Mar 2019 21:46:23 +0200 Subject: [PATCH] added partial support for MIL-STD-110 App D (intermediate) --- lib/adaptive_dfe_impl.cc | 2 +- python/physical_layer/CMakeLists.txt | 1 + python/physical_layer/MIL_STD_188_110A.py | 6 +- python/physical_layer/MIL_STD_188_110C.py | 9 +- python/physical_layer/MIL_STD_188_110D.py | 835 ++++++++++++++++++++++ python/physical_layer/STANAG_4285.py | 4 +- python/physical_layer/common.py | 3 + python/physical_layer_driver.py | 2 +- 8 files changed, 848 insertions(+), 14 deletions(-) create mode 100644 python/physical_layer/MIL_STD_188_110D.py diff --git a/lib/adaptive_dfe_impl.cc b/lib/adaptive_dfe_impl.cc index 782f261..8dcf9ae 100644 --- a/lib/adaptive_dfe_impl.cc +++ b/lib/adaptive_dfe_impl.cc @@ -256,7 +256,7 @@ gr_complex adaptive_dfe_impl::filter(gr_complex const* start, gr_complex const* // (1b) taps_symbols gr_complex dot_symbols(0); gr::digital::constellation_sptr constell = _constellations[_constellation_index]; - bool const update_taps = true; //constell->bits_per_symbol() <= 3; + bool const update_taps = constell->bits_per_symbol() <= 3; if (constell->bits_per_symbol() > 3) _use_symbol_taps = false; if (_use_symbol_taps) { diff --git a/python/physical_layer/CMakeLists.txt b/python/physical_layer/CMakeLists.txt index b144a67..3f35892 100644 --- a/python/physical_layer/CMakeLists.txt +++ b/python/physical_layer/CMakeLists.txt @@ -36,6 +36,7 @@ GR_PYTHON_INSTALL( STANAG_4285.py MIL_STD_188_110A.py MIL_STD_188_110C.py + MIL_STD_188_110D.py DESTINATION ${GR_PYTHON_DIR}/digitalhf/physical_layer ) diff --git a/python/physical_layer/MIL_STD_188_110A.py b/python/physical_layer/MIL_STD_188_110A.py index 8126b77..f145cfb 100644 --- a/python/physical_layer/MIL_STD_188_110A.py +++ b/python/physical_layer/MIL_STD_188_110A.py @@ -184,8 +184,7 @@ class PhysicalLayer(object): for _ in range(self._frame_len)])) a = np.array(zip(scramble_for_frame, scramble_for_frame), - dtype=[('symb', np.complex64), - ('scramble', np.complex64)]) + common.SYMB_SCRAMBLE_DTYPE) n_unknown = self._mode['unknown'] a['symb'][0:n_unknown] = 0 if self._frame_counter >= self._num_frames_per_block-2: @@ -258,8 +257,7 @@ class PhysicalLayer(object): """preamble symbols + scrambler""" return np.array(zip(PRE_SCRAMBLE*PRE_SYMBOLS, PRE_SCRAMBLE), - dtype=[('symb', np.complex64), - ('scramble', np.complex64)]) + common.SYMB_SCRAMBLE_DTYPE) def get_preamble_z(self): """preamble symbols for preamble correlation""" diff --git a/python/physical_layer/MIL_STD_188_110C.py b/python/physical_layer/MIL_STD_188_110C.py index 9e5b3f0..f8c6a53 100644 --- a/python/physical_layer/MIL_STD_188_110C.py +++ b/python/physical_layer/MIL_STD_188_110C.py @@ -242,8 +242,7 @@ class PhysicalLayer(object): print('make_reinserted_preamble', offset, success) a=np.array(zip(REINSERTED_PREAMBLE[offset:], REINSERTED_PREAMBLE[offset:]), - dtype=[('symb', np.complex64), - ('scramble', np.complex64)]) + common.SYMB_SCRAMBLE_DTYPE) a['symb'][-72:-72+3*13] = 0 ## D0,D1,D2 if not success: self._frame_counter = -1 @@ -251,8 +250,7 @@ class PhysicalLayer(object): def make_data_frame(self, success): self._preamble_offset = -72 ## all following reinserted preambles start at index -72 - a = np.zeros(256+31, dtype=[('symb', np.complex64), - ('scramble', np.complex64)]) + a = np.zeros(256+31, common.SYMB_SCRAMBLE_DTYPE) a['scramble'][:256] = self._data_scramble n = (self._frame_counter-2)%72 m = n%18 @@ -274,8 +272,7 @@ class PhysicalLayer(object): """preamble symbols + scrambler""" return np.array(zip(PREAMBLE, PREAMBLE), - dtype=[('symb', np.complex64), - ('scramble', np.complex64)]) + common.SYMB_SCRAMBLE_DTYPE) def get_preamble_z(self): """preamble symbols for preamble correlation""" diff --git a/python/physical_layer/MIL_STD_188_110D.py b/python/physical_layer/MIL_STD_188_110D.py new file mode 100644 index 0000000..c058993 --- /dev/null +++ b/python/physical_layer/MIL_STD_188_110D.py @@ -0,0 +1,835 @@ +## -*- python -*- + +from __future__ import print_function +import numpy as np +import common + +## ---- constellations --------------------------------------------------------- +BPSK=np.array(zip(np.exp(2j*np.pi*np.arange(2)/2), [0,1]), common.CONST_DTYPE) +QPSK=np.array(zip(np.exp(2j*np.pi*np.arange(4)/4), [0,1,3,2]), common.CONST_DTYPE) +PSK8=np.array(zip(np.exp(2j*np.pi*np.arange(8)/8), [1,0,2,3,6,7,5,4]), common.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)), common.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)), common.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)), common.CONST_DTYPE) +QAM256=np.array( + zip([+0.959366+0.056433j, +0.959366+0.169300j, +0.846499+0.507899j, +0.959366+0.282166j, + +0.846499+0.056433j, +0.846499+0.169300j, +0.846499+0.395033j, +0.846499+0.282166j, + +0.959366-0.056433j, +0.959366-0.169300j, +0.846499-0.507899j, +0.959366-0.282166j, + +0.846499-0.056433j, +0.846499-0.169300j, +0.846499-0.395033j, +0.846499-0.282166j, + +0.169300+0.959366j, +0.056433+0.998304j, +0.733632+0.507899j, +0.733632+0.620766j, + +0.733632+0.056433j, +0.733632+0.169300j, +0.733632+0.395033j, +0.733632+0.282166j, + +0.169300-0.959366j, +0.056433-0.998304j, +0.733632-0.507899j, +0.733632-0.620766j, + +0.733632-0.056433j, +0.733632-0.169300j, +0.733632-0.395033j, +0.733632-0.282166j, + +0.507899+0.846499j, +0.507899+0.733632j, +0.507899+0.507899j, +0.507899+0.620766j, + +0.507899+0.056433j, +0.507899+0.169300j, +0.507899+0.395033j, +0.507899+0.282166j, + +0.507899-0.846499j, +0.507899-0.733632j, +0.507899-0.507899j, +0.507899-0.620766j, + +0.507899-0.056433j, +0.507899-0.169300j, +0.507899-0.395033j, +0.507899-0.282166j, + +0.282166+0.959366j, +0.620766+0.733632j, +0.620766+0.507899j, +0.620766+0.620766j, + +0.620766+0.056433j, +0.620766+0.169300j, +0.620766+0.395033j, +0.620766+0.282166j, + +0.282166-0.959366j, +0.620766-0.733632j, +0.620766-0.507899j, +0.620766-0.620766j, + +0.620766-0.056433j, +0.620766-0.169300j, +0.620766-0.395033j, +0.620766-0.282166j, + ## + +0.056433+0.846499j, +0.056433+0.733632j, +0.056433+0.507899j, +0.056433+0.620766j, + +0.056433+0.056433j, +0.056433+0.169300j, +0.056433+0.395033j, +0.056433+0.282166j, + +0.056433-0.846499j, +0.056433-0.733632j, +0.056433-0.507899j, +0.056433-0.620766j, + +0.056433-0.056433j, +0.056433-0.169300j, +0.056433-0.395033j, +0.056433-0.282166j, + +0.169300+0.846499j, +0.169300+0.733632j, +0.169300+0.507899j, +0.169300+0.620766j, + +0.169300+0.056433j, +0.169300+0.169300j, +0.169300+0.395033j, +0.169300+0.282166j, + +0.169300-0.846499j, +0.169300-0.733632j, +0.169300-0.507899j, +0.169300-0.620766j, + +0.169300-0.056433j, +0.169300-0.169300j, +0.169300-0.395033j, +0.169300-0.282166j, + +0.395033+0.846499j, +0.395033+0.733632j, +0.395033+0.507899j, +0.395033+0.620766j, + +0.395033+0.056433j, +0.395033+0.169300j, +0.395033+0.395033j, +0.395033+0.282166j, + +0.395033-0.846499j, +0.395033-0.733632j, +0.395033-0.507899j, +0.395033-0.620766j, + +0.395033-0.056433j, +0.395033-0.169300j, +0.395033-0.395033j, +0.395033-0.282166j, + +0.282166+0.846499j, +0.282166+0.733632j, +0.282166+0.507899j, +0.282166+0.620766j, + +0.282166+0.056433j, +0.282166+0.169300j, +0.282166+0.395033j, +0.282166+0.282166j, + +0.282166-0.846499j, +0.282166-0.733632j, +0.282166-0.507899j, +0.282166-0.620766j, + +0.282166-0.056433j, +0.282166-0.169300j, +0.282166-0.395033j, +0.282166-0.282166j, + ## + -0.959366+0.056433j, -0.959366+0.169300j, -0.846499+0.507899j, -0.959366+0.282166j, + -0.846499+0.056433j, -0.846499+0.169300j, -0.846499+0.395033j, -0.846499+0.282166j, + -0.959366-0.056433j, -0.959366-0.169300j, -0.846499-0.507899j, -0.959366-0.282166j, + -0.846499-0.056433j, -0.846499-0.169300j, -0.846499-0.395033j, -0.846499-0.282166j, + -0.169300+0.959366j, -0.056433+0.998304j, -0.733632+0.507899j, -0.733632+0.620766j, + -0.733632+0.056433j, -0.733632+0.169300j, -0.733632+0.395033j, -0.733632+0.282166j, + -0.169300-0.959366j, -0.056433-0.998304j, -0.733632-0.507899j, -0.733632-0.620766j, + -0.733632-0.056433j, -0.733632-0.169300j, -0.733632-0.395033j, -0.733632-0.282166j, + -0.507899+0.846499j, -0.507899+0.733632j, -0.507899+0.507899j, -0.507899+0.620766j, + -0.507899+0.056433j, -0.507899+0.169300j, -0.507899+0.395033j, -0.507899+0.282166j, + -0.507899-0.846499j, -0.507899-0.733632j, -0.507899-0.507899j, -0.507899-0.620766j, + -0.507899-0.056433j, -0.507899-0.169300j, -0.507899-0.395033j, -0.507899-0.282166j, + -0.282166+0.959366j, -0.620766+0.733632j, -0.620766+0.507899j, -0.620766+0.620766j, + -0.620766+0.056433j, -0.620766+0.169300j, -0.620766+0.395033j, -0.620766+0.282166j, + -0.282166-0.959366j, -0.620766-0.733632j, -0.620766-0.507899j, -0.620766-0.620766j, + -0.620766-0.056433j, -0.620766-0.169300j, -0.620766-0.395033j, -0.620766-0.282166j, + ## + -0.056433+0.846499j, -0.056433+0.733632j, -0.056433+0.507899j, -0.056433+0.620766j, + -0.056433+0.056433j, -0.056433+0.169300j, -0.056433+0.395033j, -0.056433+0.282166j, + -0.056433-0.846499j, -0.056433-0.733632j, -0.056433-0.507899j, -0.056433-0.620766j, + -0.056433-0.056433j, -0.056433-0.169300j, -0.056433-0.395033j, -0.056433-0.282166j, + -0.169300+0.846499j, -0.169300+0.733632j, -0.169300+0.507899j, -0.169300+0.620766j, + -0.169300+0.056433j, -0.169300+0.169300j, -0.169300+0.395033j, -0.169300+0.282166j, + -0.169300-0.846499j, -0.169300-0.733632j, -0.169300-0.507899j, -0.169300-0.620766j, + -0.169300-0.056433j, -0.169300-0.169300j, -0.169300-0.395033j, -0.169300-0.282166j, + -0.395033+0.846499j, -0.395033+0.733632j, -0.395033+0.507899j, -0.395033+0.620766j, + -0.395033+0.056433j, -0.395033+0.169300j, -0.395033+0.395033j, -0.395033+0.282166j, + -0.395033-0.846499j, -0.395033-0.733632j, -0.395033-0.507899j, -0.395033-0.620766j, + -0.395033-0.056433j, -0.395033-0.169300j, -0.395033-0.395033j, -0.395033-0.282166j, + -0.282166+0.846499j, -0.282166+0.733632j, -0.282166+0.507899j, -0.282166+0.620766j, + -0.282166+0.056433j, -0.282166+0.169300j, -0.282166+0.395033j, -0.282166+0.282166j, + -0.282166-0.846499j, -0.282166-0.733632j, -0.282166-0.507899j, -0.282166-0.620766j, + -0.282166-0.056433j, -0.282166-0.169300j, -0.282166-0.395033j, -0.282166-0.282166j], + range(256)), common.CONST_DTYPE) + +## ---- constellation indices --------------------------------------------------- +MODE_WALSH = 0 +MODE_BPSK = 0 +MODE_QPSK = 1 +MODE_8PSK = 2 +MODE_16QAM = 3 +MODE_32QAM = 4 +MODE_64QAM = 5 +MODE_256QAM = 6 + +WID_MODE = [MODE_WALSH, # 0 + MODE_BPSK, # 1 + MODE_BPSK, # 2 + MODE_BPSK, # 3 + MODE_BPSK, # 4 + MODE_BPSK, # 5 + MODE_QPSK, # 6 + MODE_8PSK, # 7 + MODE_16QAM, # 8 + MODE_32QAM, # 9 + MODE_64QAM, # 10 + MODE_64QAM, # 11 + MODE_256QAM, # 12 + MODE_QPSK] # 13 + +BW_UNKNOWN = { ## BW -> unknown[WID] + '3 kHz': ['N/A', 48, 48, 96, 96, 256, 256, 256, 256, 256, 256, 360, 360, 256], + '6 kHz': ['N/A', 96, 96, 204, 204, 544, 544, 544, 544, 544, 544, 540, 540 ], + '9 kHz': ['N/A', 288, 288, 288, '-', 768, 768, 768, 768, 768, 768, 1080, 1080 ], + '12 kHz': ['N/A', 192, 192, 384, 384, 1024, 1024, 1024, 1024, 1024, 1024, 1080, 1080 ], + '15 kHz': ['N/A', 288, 288, 288, 288, 1280, 1280, 1280, 1280, 1280, 1280, 1152, 1152 ], + '18 kHz': ['N/A', 448, 448, 448, '-', 1536, 1536, 1536, 1536, 1536, 1536, 1920, 1920 ], + '21 kHz': ['N/A', 320, 320, 320, 320, 1344, 1344, 1344, 1344, 1344, 1344, 2560, 2560 ], + '24 kHz': ['N/A', 272, 272, 816, 816, 2176, 2176, 2176, 2176, 2176, 2176, 1920, 1920 ], + '30 kHz': ['N/A', 576, 576, 576, 576, 2560, 2560, 2560, 2560, 2560, 2560, 2700, 2700 ], + '36 kHz': ['N/A', 1152, 1152, 1152, 3072, 3072, 3072, 3072, 3072, 3072, 3072, 3240, 3240 ], + '42 kHz': ['N/A', 768, 768, 768, 3456, 3456, 3456, 3456, 3456, 3456, 3456, 3840, 3840 ], + '48 kHz': ['N/A', 512, 512, 512, 2560, 2560, 2560, 2560, 2560, 2560, 2560, 2880, 2880 ] +} +BW_KNOWN = { ## BW -> known[WID] + '3 kHz': ['N/A', 48, 48, 32, 32, 32, 32, 32, 32, 32, 32, 24, 24, 32], + '6 kHz': ['N/A', 96, 96, 68, 68, 68, 68, 68, 68, 68, 68, 36, 36 ], + '9 kHz': ['N/A', 144, 144, 144, '-', 96, 96, 96, 96, 96, 96, 72, 72 ], + '12 kHz': ['N/A', 192, 192, 128, 128, 128, 128, 128, 128, 128, 128, 72, 72 ], + '15 kHz': ['N/A', 192, 192, 192, 192, 160, 160, 160, 160, 160, 160, 128, 128 ], + '18 kHz': ['N/A', 224, 224, 224, '-', 192, 192, 192, 192, 192, 192, 128, 128 ], + '21 kHz': ['N/A', 240, 240, 240, 240, 224, 224, 224, 224, 224, 224, 128, 128 ], + '24 kHz': ['N/A', 272, 272, 272, 272, 272, 272, 272, 272, 272, 272, 128, 128 ], + '30 kHz': ['N/A', 384, 384, 384, 384, 320, 320, 320, 320, 320, 320, 180, 180 ], + '36 kHz': ['N/A', 576, 576, 576, 384, 384, 384, 384, 384, 384, 384, 216, 216 ], + '42 kHz': ['N/A', 576, 576, 576, 576, 576, 576, 576, 576, 576, 576, 192, 192 ], + '48 kHz': ['N/A', 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 192, 192 ] +} + +MP_LEN_BASE_SHIFT = { + 24: {'base_len': 13, 'base_shift': 6}, + 32: {'base_len': 16, 'base_shift': 8}, + 36: {'base_len': 19, 'base_shift': 9}, + 48: {'base_len': 25, 'base_shift': 12}, + 64: {'base_len': 36, 'base_shift': 18}, + 68: {'base_len': 36, 'base_shift': 18}, + 72: {'base_len': 36, 'base_shift': 18}, + 96: {'base_len': 49, 'base_shift': 24}, + 128: {'base_len': 64, 'base_shift': 32}, + 144: {'base_len': 81, 'base_shift': 40}, + 160: {'base_len': 81, 'base_shift': 40}, + 180: {'base_len': 100, 'base_shift': 50}, + 192: {'base_len': 100, 'base_shift': 50}, + 216: {'base_len': 121, 'base_shift': 60}, + 224: {'base_len': 121, 'base_shift': 60}, + 240: {'base_len': 121, 'base_shift': 60}, + 272: {'base_len': 144, 'base_shift': 72}, + 320: {'base_len': 169, 'base_shift': 85}, + 384: {'base_len': 196, 'base_shift': 98}, + 512: {'base_len': 256, 'base_shift': 128}, + 576: {'base_len': 289, 'base_shift': 145} +} + +def mp_base(n): + #mp=@(n,q) exp(1i*pi*(2*sqrt(n)*floor([0:n-1]/sqrt(n)) .* [0:n-1]*q/n)) + if n == 13: ## Barker-13 sequence + return np.array([1,1,1,1,1,-1,-1,1,1,-1,1,-1,1], + dtype=np.complex64) + if n == 19: ## [1 -Legendre(k/19)] + return np.array([1,-1,1,1,-1,-1,-1,-1,1,-1,1,-1,1,1,1,1,-1,-1,1], + dtype=np.complex64) + w = np.arange(n) + m = np.sqrt(n) + return np.exp(-2j*np.pi * m*np.floor(w/m) * w / n, + dtype=np.complex64) + +def make_mp(length, base_len, shift): + z = np.roll(mp_base(base_len), -shift) + while len(z) < length: + n = len(z) + z = np.append(z, z[0:min(n, length-n)]) + return z + +##------------------------------------------------------------------------------ + +INTERLEAVERS = ['US', 'S', 'M', 'L'] + +BW_INTL = { ## [BW][WID][INTL] -> [Number of Frames, Number of bits, Number of Input Bits] + '3 kHz': [ + {'US': ['-', '-', '-'], 'S': [40, 80, 40], 'M': [144, 288, 144], 'L': [576, 1152, 576]}, ## WID 0 + {'US': [ 4, 192, 24], 'S': [16, 768, 96], 'M': [ 64, 3072, 384], 'L': [256, 12288, 1536]}, ## WID 1 + {'US': [ 4, 192, 48], 'S': [16, 768, 192], 'M': [ 64, 3072, 768], 'L': [256, 12288, 3072]}, ## WID 2 + {'US': [ 2, 192, 64], 'S': [ 8, 768, 256], 'M': [ 32, 3072, 1024], 'L': [128, 12288, 4096]}, ## WID 3 + {'US': [ 2, 192, 128], 'S': [ 8, 768, 512], 'M': [ 32, 3072, 2048], 'L': [128, 12288, 8192]}, ## WID 4 + {'US': [ 1, 256, 192], 'S': [ 4, 1024, 768], 'M': [ 16, 4096, 3072], 'L': [ 64, 16384, 12288]}, ## WID 5 + {'US': [ 1, 512, 384], 'S': [ 4, 2048, 1536], 'M': [ 16, 8192, 6144], 'L': [ 64, 32768, 24576]}, ## WID 6 + {'US': [ 1, 768, 576], 'S': [ 4, 3072, 2304], 'M': [ 16, 12288, 9216], 'L': [ 64, 49152, 36864]}, ## WID 7 + {'US': [ 1, 1024, 768], 'S': [ 4, 4096, 3072], 'M': [ 16, 16384, 12288], 'L': [ 64, 65536, 49152]}, ## WID 8 + {'US': [ 1, 1280, 960], 'S': [ 4, 5120, 3840], 'M': [ 16, 20480, 15360], 'L': [ 64, 81920, 61440]}, ## WID 9 + {'US': [ 1, 1536, 1152], 'S': [ 4, 6144, 4608], 'M': [ 16, 24576, 18432], 'L': [ 64, 98304, 73728]}, ## WID 10 + {'US': [ 1, 2160, 1920], 'S': [ 4, 8640, 7680], 'M': [ 16, 34560, 30720], 'L': [ 64, 138240, 122880]}, ## WID 11 + {'US': [ 1, 2880, 2560], 'S': [ 4, 11520, 10240], 'M': [ 16, 46080, 40960], 'L': [ 64, 184320, 163840]}, ## WID 12 + {'US': [ 1, 512, 288], 'S': [ 4, 2048, 1152], 'M': [ 16, 8192, 4608], 'L': [ 64, 32768, 18432]}], ## WID 13 + '6 kHz': [ + {'US': ['-', '-', '-'], 'S': [80, 160, 80], 'M': [288, 576, 288], 'L': [1152, 2304, 1152]}, ## WID 0 + {'US': [ 4, 384, 48], 'S': [16, 1536, 192], 'M': [ 64, 6144, 768], 'L': [ 256, 24576, 3072]}, ## WID 1 + {'US': [ 4, 384, 96], 'S': [16, 1536, 384], 'M': [ 64, 6144, 1536], 'L': [ 256, 24576, 6144]}, ## WID 2 + {'US': [ 2, 408, 136], 'S': [ 8, 1632, 544], 'M': [ 32, 6528, 2176], 'L': [ 128, 26112, 8704]}, ## WID 3 + {'US': [ 2, 408, 272], 'S': [ 8, 1632, 1088], 'M': [ 32, 6528, 4352], 'L': [ 128, 26112, 17408]}, ## WID 4 + {'US': [ 1, 544, 408], 'S': [ 4, 2176, 1632], 'M': [ 16, 8704, 6528], 'L': [ 64, 34816, 26112]}, ## WID 5 + {'US': [ 1, 1088, 816], 'S': [ 4, 4352, 3264], 'M': [ 16, 17408, 13056], 'L': [ 64, 69632, 52224]}, ## WID 6 + {'US': [ 1, 1632, 1224], 'S': [ 4, 6528, 4896], 'M': [ 16, 26112, 19584], 'L': [ 64, 104448, 78336]}, ## WID 7 + {'US': [ 1, 2176, 1632], 'S': [ 4, 8704, 6528], 'M': [ 16, 34816, 26112], 'L': [ 64, 139264, 104448]}, ## WID 8 + {'US': [ 1, 2720, 2040], 'S': [ 4, 10880, 8160], 'M': [ 16, 43520, 32640], 'L': [ 64, 174080, 130560]}, ## WID 9 + {'US': [ 1, 3264, 2448], 'S': [ 4, 13056, 9792], 'M': [ 16, 52224, 39168], 'L': [ 64, 208896, 156672]}, ## WID 10 + {'US': [ 1, 3240, 2880], 'S': [ 4, 12960, 11520], 'M': [ 16, 51840, 46080], 'L': [ 64, 207360, 184320]}, ## WID 11 + {'US': [ 1, 4320, 3840], 'S': [ 4, 17280, 15360], 'M': [ 16, 69120, 61440], 'L': [ 64, 276480, 245760]}], ## WID 12 + '9 kHz': [ + {'US': ['-', '-', '-'], 'S': [120, 240, 160], 'M': [432, 864, 576], 'L': [1728, 3456, 2304]}, ## WID 0 + {'US': [ 2, 576, 72], 'S': [ 8, 2304, 288], 'M': [ 32, 9216, 1152], 'L': [ 128, 36864, 4608]}, ## WID 1 + {'US': [ 2, 576, 144], 'S': [ 8, 2304, 576], 'M': [ 32, 9216, 2304], 'L': [ 128, 36864, 9216]}, ## WID 2 + {'US': [ 2, 576, 288], 'S': [ 8, 2304, 1152], 'M': [ 32, 9216, 4608], 'L': [ 128, 36864, 18432]}, ## WID 3 + {'US': ['-', '-', '-'], 'S': ['-', '-', '-'], 'M': ['-', '-', '-'], 'L': [ '-', '-', '-']}, ## WID 4 + {'US': [ 1, 768, 576], 'S': [ 4, 3072, 2304], 'M': [ 16, 12288, 9216], 'L': [ 64, 49152, 36864]}, ## WID 5 + {'US': [ 1, 1536, 1152], 'S': [ 4, 6144, 4608], 'M': [ 16, 24576, 18432], 'L': [ 64, 98304, 73728]}, ## WID 6 + {'US': [ 1, 2304, 1728], 'S': [ 4, 9216, 6912], 'M': [ 16, 36864, 27648], 'L': [ 64, 147456, 110592]}, ## WID 7 + {'US': [ 1, 3072, 2304], 'S': [ 4, 12288, 9216], 'M': [ 16, 49152, 36864], 'L': [ 64, 196608, 147456]}, ## WID 8 + {'US': [ 1, 3840, 2880], 'S': [ 4, 15360, 11520], 'M': [ 16, 61440, 46080], 'L': [ 64, 245760, 184320]}, ## WID 9 + {'US': [ 1, 4608, 3456], 'S': [ 4, 18432, 13824], 'M': [ 16, 73728, 55296], 'L': [ 64, 294912, 221184]}, ## WID 10 + {'US': [ 1, 6480, 5760], 'S': [ 4, 25920, 23040], 'M': [ 16, 103680, 92160], 'L': [ 64, 414720, 368640]}, ## WID 11 + {'US': [ 1, 8640, 7680], 'S': [ 4, 34560, 30720], 'M': [ 16, 138240, 122880], 'L': [ 64, 552960, 491520]}], ## WID 12 + '12 kHz': [ + {'US': ['-', '-', '-'], 'S': [160, 320, 160], 'M': [576, 1152, 576], 'L': [2304, 4608, 2304]}, ## WID 0 + {'US': [ 3, 576, 72], 'S': [ 12, 2304, 288], 'M': [ 48, 9216, 1152], 'L': [ 192, 36864, 4608]}, ## WID 1 + {'US': [ 3, 576, 144], 'S': [ 12, 2304, 576], 'M': [ 48, 9216, 2304], 'L': [ 192, 36864, 9216]}, ## WID 2 + {'US': [ 2, 768, 256], 'S': [ 8, 3072, 1024], 'M': [ 32, 12288, 4096], 'L': [ 128, 49152, 16384]}, ## WID 3 + {'US': [ 2, 768, 512], 'S': [ 8, 3072, 2048], 'M': [ 32, 12288, 8192], 'L': [ 128, 49152, 32768]}, ## WID 4 + {'US': [ 1, 1024, 768], 'S': [ 4, 4096, 3072], 'M': [ 16, 16384, 12288], 'L': [ 64, 65536, 49152]}, ## WID 5 + {'US': [ 1, 2048, 1536], 'S': [ 4, 8192, 6144], 'M': [ 16, 32768, 24576], 'L': [ 64, 131072, 98304]}, ## WID 6 + {'US': [ 1, 3072, 2304], 'S': [ 4, 12288, 9216], 'M': [ 16, 49152, 36864], 'L': [ 64, 196608, 147456]}, ## WID 7 + {'US': [ 1, 4096, 3072], 'S': [ 4, 16384, 12288], 'M': [ 16, 65536, 49152], 'L': [ 64, 262144, 196608]}, ## WID 8 + {'US': [ 1, 5120, 3840], 'S': [ 4, 20480, 15360], 'M': [ 16, 81920, 61440], 'L': [ 64, 327680, 245760]}, ## WID 9 + {'US': [ 1, 6144, 4608], 'S': [ 4, 24576, 18432], 'M': [ 16, 98304, 73728], 'L': [ 64, 393216, 294912]}, ## WID 10 + {'US': [ 1, 6480, 5760], 'S': [ 4, 25920, 23040], 'M': [ 16, 103680, 92160], 'L': [ 64, 414720, 368640]}, ## WID 11 + {'US': [ 1, 8640, 7680], 'S': [ 4, 34560, 30720], 'M': [ 16, 138240, 122880], 'L': [ 64, 552960, 491520]}], ## WID 12 + '15 kHz': [ + {'US': ['-', '-', '-'], 'S': [200, 400, 160], 'M': [720, 1440, 576], 'L': [2880, 5760, 2304]}, ## WID 0 + {'US': [ 3, 864, 72], 'S': [ 12, 3456, 288], 'M': [ 48, 13824, 1152], 'L': [ 192, 55296, 4608]}, ## WID 1 + {'US': [ 3, 864, 144], 'S': [ 12, 3456, 576], 'M': [ 48, 13824, 2304], 'L': [ 192, 55296, 9216]}, ## WID 2 + {'US': [ 3, 864, 288], 'S': [ 12, 3456, 1152], 'M': [ 48, 13824, 4608], 'L': [ 192, 55296, 18432]}, ## WID 3 + {'US': [ 3, 864, 576], 'S': [ 12, 3456, 2304], 'M': [ 48, 13824, 9216], 'L': [ 192, 55296, 36864]}, ## WID 4 + {'US': [ 1, 1280, 960], 'S': [ 4, 5120, 3840], 'M': [ 16, 20480, 15360], 'L': [ 64, 81920, 61440]}, ## WID 5 + {'US': [ 1, 2560, 1920], 'S': [ 4, 10240, 7680], 'M': [ 16, 40960, 30720], 'L': [ 64, 163840, 122880]}, ## WID 6 + {'US': [ 1, 3840, 2880], 'S': [ 4, 15360, 11520], 'M': [ 16, 61440, 46080], 'L': [ 64, 245760, 184320]}, ## WID 7 + {'US': [ 1, 5120, 3840], 'S': [ 4, 20480, 15360], 'M': [ 16, 81920, 61440], 'L': [ 64, 327680, 245760]}, ## WID 8 + {'US': [ 1, 6400, 4800], 'S': [ 4, 25600, 19200], 'M': [ 16, 102400, 76800], 'L': [ 64, 409600, 307200]}, ## WID 9 + {'US': [ 1, 7680, 5760], 'S': [ 4, 30720, 23040], 'M': [ 16, 122880, 92160], 'L': [ 64, 491520, 368640]}, ## WID 10 + {'US': [ 1, 6912, 6144], 'S': [ 4, 27648, 24576], 'M': [ 16, 110592, 98304], 'L': [ 64, 442368, 393216]}, ## WID 11 + {'US': [ 1, 9216, 8192], 'S': [ 4, 36864, 32768], 'M': [ 16, 147456, 131072], 'L': [ 64, 589824, 524288]}], ## WID 12 + '18 kHz': [ + {'US': ['-', '-', '-'], 'S': [240, 480, 320], 'M': [864, 1728, 1152], 'L': [3456, 6912, 4608]}, ## WID 0 + {'US': [ 3, 1344, 168], 'S': [ 12, 5376, 672], 'M': [ 48, 21504, 2688], 'L': [ 192, 86016, 10752]}, ## WID 1 + {'US': [ 3, 1344, 336], 'S': [ 12, 5376, 1344], 'M': [ 48, 21504, 5376], 'L': [ 192, 86016, 21504]}, ## WID 2 + {'US': [ 3, 1344, 672], 'S': [ 12, 5376, 2688], 'M': [ 48, 21504, 10752], 'L': [ 192, 86016, 43008]}, ## WID 3 + {'US': ['-', '-', '-'], 'S': ['-', '-', '-'], 'M': ['-', '-', '-'], 'L': [ '-', '-', '-']}, ## WID 4 + {'US': [ 1, 1536, 1152], 'S': [ 4, 6144, 4608], 'M': [ 16, 24576, 18432], 'L': [ 64, 98304, 73728]}, ## WID 5 + {'US': [ 1, 3072, 2304], 'S': [ 4, 12288, 9216], 'M': [ 16, 49152, 36864], 'L': [ 64, 196608, 147456]}, ## WID 6 + {'US': [ 1, 4608, 3456], 'S': [ 4, 18432, 13824], 'M': [ 16, 73728, 55296], 'L': [ 64, 294912, 221184]}, ## WID 7 + {'US': [ 1, 6144, 4608], 'S': [ 4, 24576, 18432], 'M': [ 16, 98304, 73728], 'L': [ 64, 393216, 294912]}, ## WID 8 + {'US': [ 1, 7680, 5760], 'S': [ 4, 30720, 23040], 'M': [ 16, 122880, 92160], 'L': [ 64, 491520, 368640]}, ## WID 9 + {'US': [ 1, 9216, 6912], 'S': [ 4, 36864, 27648], 'M': [ 16, 147456, 110592], 'L': [ 64, 589824, 442368]}, ## WID 10 + {'US': [ 1, 11520, 10240], 'S': [ 4, 46080, 40960], 'M': [ 16, 184320, 163840], 'L': [ 64, 737280, 655360]}, ## WID 11 + {'US': [ 1, 15360, 12800], 'S': [ 4, 61440, 51200], 'M': [ 16, 245760, 204800], 'L': [ 64, 983040, 819200]}], ## WID 12 + '21 kHz': [ + {'US': ['-', '-', '-'], 'S': [280, 560, 160], 'M': [1008, 2016, 576], 'L': [4032, 8064, 2304]}, ## WID 0 + {'US': [ 4, 1280, 80], 'S': [ 16, 5120, 320], 'M': [ 64, 20480, 1280], 'L': [ 256, 81920, 5120]}, ## WID 1 + {'US': [ 4, 1280, 160], 'S': [ 16, 5120, 640], 'M': [ 64, 20480, 2560], 'L': [ 256, 81920, 10240]}, ## WID 2 + {'US': [ 4, 1280, 320], 'S': [ 16, 5120, 1280], 'M': [ 64, 20480, 5120], 'L': [ 256, 81920, 20480]}, ## WID 3 + {'US': [ 4, 1280, 640], 'S': [ 16, 5120, 2560], 'M': [ 64, 20480, 10240], 'L': [ 256, 81920, 40960]}, ## WID 4 + {'US': [ 1, 1344, 896], 'S': [ 4, 5376, 3584], 'M': [ 16, 21504, 14336], 'L': [ 64, 86016, 57344]}, ## WID 5 + {'US': [ 1, 2688, 1792], 'S': [ 4, 10752, 7168], 'M': [ 16, 43008, 28672], 'L': [ 64, 172032, 114688]}, ## WID 6 + {'US': [ 1, 4032, 2688], 'S': [ 4, 16128, 10752], 'M': [ 16, 64512, 43008], 'L': [ 64, 258048, 172032]}, ## WID 7 + {'US': [ 1, 5376, 3584], 'S': [ 4, 21504, 14336], 'M': [ 16, 86016, 57344], 'L': [ 64, 344064, 229376]}, ## WID 8 + {'US': [ 1, 6720, 4480], 'S': [ 4, 26880, 17920], 'M': [ 16, 107520, 71680], 'L': [ 64, 430080, 286720]}, ## WID 9 + {'US': [ 1, 8064, 5376], 'S': [ 4, 32256, 21504], 'M': [ 16, 129024, 86016], 'L': [ 64, 516096, 344064]}, ## WID 10 + {'US': [ 1, 15360, 12288], 'S': [ 4, 61440, 49152], 'M': [ 16, 245760, 196608], 'L': [ 64, 983040, 786432]}, ## WID 11 + {'US': [ 1, 20480, 18432], 'S': [ 4, 81920, 73728], 'M': [ 16, 327680, 294912], 'L': [ 64, 1310720, 1179648]}], ## WID 12 + '24 kHz': [ + {'US': ['-', '-', '-'], 'S': [320, 640, 320], 'M': [1152, 2304, 1152], 'L': [4608, 9216, 4608]}, ## WID 0 + {'US': [ 4, 1088, 136], 'S': [ 16, 4352, 544], 'M': [ 64, 17408, 2176], 'L': [ 256, 69632, 8704]}, ## WID 1 + {'US': [ 4, 1088, 272], 'S': [ 16, 4352, 1088], 'M': [ 64, 17408, 4352], 'L': [ 256, 69632, 17408]}, ## WID 2 + {'US': [ 2, 1632, 544], 'S': [ 8, 6528, 2176], 'M': [ 32, 26112, 8704], 'L': [ 128, 104448, 34816]}, ## WID 3 + {'US': [ 2, 1632, 1088], 'S': [ 8, 6528, 4352], 'M': [ 32, 26112, 17408], 'L': [ 128, 104448, 69632]}, ## WID 4 + {'US': [ 1, 2176, 1632], 'S': [ 4, 8704, 6528], 'M': [ 16, 34816, 26112], 'L': [ 64, 139264, 104448]}, ## WID 5 + {'US': [ 1, 4352, 3264], 'S': [ 4, 17408, 13056], 'M': [ 16, 69632, 52224], 'L': [ 64, 278528, 208896]}, ## WID 6 + {'US': [ 1, 6528, 4896], 'S': [ 4, 26112, 19584], 'M': [ 16, 104448, 78336], 'L': [ 64, 417792, 313344]}, ## WID 7 + {'US': [ 1, 8704, 6528], 'S': [ 4, 34816, 26112], 'M': [ 16, 139264, 104448], 'L': [ 64, 557056, 417792]}, ## WID 8 + {'US': [ 1, 10880, 8160], 'S': [ 4, 43520, 32640], 'M': [ 16, 174080, 130560], 'L': [ 64, 696320, 522240]}, ## WID 9 + {'US': [ 1, 13056, 9792], 'S': [ 4, 52224, 39168], 'M': [ 16, 208896, 156672], 'L': [ 64, 835584, 626688]}, ## WID 10 + {'US': [ 1, 11520, 10240], 'S': [ 4, 46080, 40960], 'M': [ 16, 184320, 163840], 'L': [ 64, 737280, 655360]}, ## WID 11 + {'US': [ 1, 15360, 12800], 'S': [ 4, 61440, 51200], 'M': [ 16, 245760, 204800], 'L': [ 64, 983040, 819200]}], ## WID 12 + '30 kHz': [ + {'US': ['-', '-', '-'], 'S': [400, 800, 320], 'M': [1440, 2880, 1152], 'L': [5760, 11520, 4608]}, ## WID 0 + {'US': [ 3, 1728, 144], 'S': [ 12, 6912, 576], 'M': [ 48, 27648, 2304], 'L': [ 192, 110592, 9216]}, ## WID 1 + {'US': [ 3, 1728, 288], 'S': [ 12, 6912, 1152], 'M': [ 48, 27648, 4608], 'L': [ 192, 110592, 18432]}, ## WID 2 + {'US': [ 3, 1728, 576], 'S': [ 12, 6912, 2304], 'M': [ 48, 27648, 9216], 'L': [ 192, 110592, 36864]}, ## WID 3 + {'US': [ 3, 1728, 1152], 'S': [ 12, 6912, 4608], 'M': [ 48, 27648, 18432], 'L': [ 192, 110592, 73728]}, ## WID 4 + {'US': [ 1, 2560, 1920], 'S': [ 4, 10240, 7680], 'M': [ 16, 40960, 30720], 'L': [ 64, 163840, 122880]}, ## WID 5 + {'US': [ 1, 5120, 3840], 'S': [ 4, 20480, 15360], 'M': [ 16, 81920, 61440], 'L': [ 64, 327680, 245760]}, ## WID 6 + {'US': [ 1, 7680, 5760], 'S': [ 4, 30720, 23040], 'M': [ 16, 122880, 92160], 'L': [ 64, 491520, 368640]}, ## WID 7 + {'US': [ 1, 10240, 7680], 'S': [ 4, 40960, 30720], 'M': [ 16, 163840, 122880], 'L': [ 64, 655360, 491520]}, ## WID 8 + {'US': [ 1, 12800, 9600], 'S': [ 4, 51200, 38400], 'M': [ 16, 204800, 153600], 'L': [ 64, 819200, 614400]}, ## WID 9 + {'US': [ 1, 15360, 11520], 'S': [ 4, 61440, 46080], 'M': [ 16, 245760, 184320], 'L': [ 64, 983040, 737280]}, ## WID 10 + {'US': [ 1, 16200, 14400], 'S': [ 4, 64800, 57600], 'M': [ 16, 259200, 230400], 'L': [ 64, 1036800, 921600]}, ## WID 11 + {'US': [ 1, 21600, 19200], 'S': [ 4, 86400, 76800], 'M': [ 16, 345600, 307200], 'L': [ 64, 1382400, 1228800]}], ## WID 12 + '36 kHz': [ + {'US': ['-', '-', '-'], 'S': [480, 960, 640], 'M': [1728, 3456, 2304], 'L': [6912, 13824, 9216]}, ## WID 0 + {'US': [ 2, 2304, 288], 'S': [ 8, 9216, 1152], 'M': [ 32, 36864, 4608], 'L': [ 128, 147456, 18432]}, ## WID 1 + {'US': [ 2, 2304, 576], 'S': [ 8, 9216, 2304], 'M': [ 32, 36864, 9216], 'L': [ 128, 147456, 36864]}, ## WID 2 + {'US': [ 2, 2304, 1152], 'S': [ 8, 9216, 4608], 'M': [ 32, 36864, 18432], 'L': [ 128, 147456, 73728]}, ## WID 3 + {'US': [ 1, 3072, 1536], 'S': [ 4, 12288, 6144], 'M': [ 16, 49152, 24576], 'L': [ 64, 196608, 98304]}, ## WID 4 + {'US': [ 1, 3072, 2304], 'S': [ 4, 12288, 9216], 'M': [ 16, 49152, 36864], 'L': [ 64, 196608, 147456]}, ## WID 5 + {'US': [ 1, 6144, 4608], 'S': [ 4, 24576, 18432], 'M': [ 16, 98304, 73728], 'L': [ 64, 393216, 294912]}, ## WID 6 + {'US': [ 1, 9216, 6912], 'S': [ 4, 36864, 27648], 'M': [ 16, 147456, 110592], 'L': [ 64, 589824, 442368]}, ## WID 7 + {'US': [ 1, 12288, 9216], 'S': [ 4, 49152, 36864], 'M': [ 16, 196608, 147456], 'L': [ 64, 786432, 589824]}, ## WID 8 + {'US': [ 1, 15360, 11520], 'S': [ 4, 61440, 46080], 'M': [ 16, 245760, 184320], 'L': [ 64, 983040, 737280]}, ## WID 9 + {'US': [ 1, 18432, 13824], 'S': [ 4, 73728, 55296], 'M': [ 16, 294912, 221184], 'L': [ 64, 1179648, 884736]}, ## WID 10 + {'US': [ 1, 19440, 17280], 'S': [ 4, 77760, 69120], 'M': [ 16, 311040, 276480], 'L': [ 64, 1244160, 1105920]}, ## WID 11 + {'US': [ 1, 25920, 23040], 'S': [ 4, 103680, 92160], 'M': [ 16, 414720, 368640], 'L': [ 64, 1658880, 1474560]}], ## WID 12 + '42 kHz': [ + {'US': ['-', '-', '-'], 'S': [560, 1120, 640], 'M': [2016, 4032, 2304], 'L': [8064, 16128, 9216]}, ## WID 0 + {'US': [ 3, 2304, 288], 'S': [ 12, 9216, 1152], 'M': [ 48, 36864, 4608], 'L': [ 192, 147456, 18432]}, ## WID 1 + {'US': [ 3, 2304, 576], 'S': [ 12, 9216, 2304], 'M': [ 48, 36864, 9216], 'L': [ 192, 147456, 36864]}, ## WID 2 + {'US': [ 3, 2304, 1152], 'S': [ 12, 9216, 4608], 'M': [ 48, 36864, 18432], 'L': [ 192, 147456, 73728]}, ## WID 3 + {'US': [ 1, 3456, 1728], 'S': [ 4, 13824, 6912], 'M': [ 16, 55296, 27648], 'L': [ 64, 221184, 110592]}, ## WID 4 + {'US': [ 1, 3456, 2304], 'S': [ 4, 13824, 9216], 'M': [ 16, 55296, 36864], 'L': [ 64, 221184, 147456]}, ## WID 5 + {'US': [ 1, 6912, 4608], 'S': [ 4, 27648, 18432], 'M': [ 16, 110592, 73728], 'L': [ 64, 442368, 294912]}, ## WID 6 + {'US': [ 1, 10368, 6912], 'S': [ 4, 41472, 27648], 'M': [ 16, 165888, 110592], 'L': [ 64, 663552, 442368]}, ## WID 7 + {'US': [ 1, 13824, 9216], 'S': [ 4, 55296, 36864], 'M': [ 16, 221184, 147456], 'L': [ 64, 884736, 589824]}, ## WID 8 + {'US': [ 1, 17280, 11520], 'S': [ 4, 69120, 46080], 'M': [ 16, 276480, 184320], 'L': [ 64, 1105920, 737280]}, ## WID 9 + {'US': [ 1, 20736, 13824], 'S': [ 4, 82944, 55296], 'M': [ 16, 331776, 221184], 'L': [ 64, 1327104, 884736]}, ## WID 10 + {'US': [ 1, 23040, 19200], 'S': [ 4, 92160, 76800], 'M': [ 16, 368640, 307200], 'L': [ 64, 1474560, 1228800]}, ## WID 11 + {'US': [ 1, 30720, 23040], 'S': [ 4, 122880, 92160], 'M': [ 16, 491520, 368640], 'L': [ 64, 1966080, 1474560]}], ## WID 12 + '48 kHz': [ + {'US': ['-', '-', '-'], 'S': [640, 1280, 640], 'M': [2304, 4608, 2304], 'L': [9216, 18432, 9216]}, ## WID 0 + {'US': [ 3, 1536, 192], 'S': [ 18, 9216, 1152], 'M': [ 72, 36864, 4608], 'L': [ 288, 147456, 18432]}, ## WID 1 + {'US': [ 3, 1536, 384], 'S': [ 18, 9216, 2304], 'M': [ 72, 36864, 9216], 'L': [ 288, 147456, 36864]}, ## WID 2 + {'US': [ 3, 1536, 768], 'S': [ 18, 9216, 4608], 'M': [ 72, 36864, 18432], 'L': [ 288, 147456, 73728]}, ## WID 3 + {'US': [ 1, 2560, 1280], 'S': [ 6, 15360, 7680], 'M': [ 24, 61440, 30720], 'L': [ 96, 245760, 122880]}, ## WID 4 + {'US': [ 1, 2560, 1920], 'S': [ 6, 15360, 11520], 'M': [ 24, 61440, 46080], 'L': [ 96, 245760, 184320]}, ## WID 5 + {'US': [ 1, 5120, 3840], 'S': [ 6, 30720, 23040], 'M': [ 24, 122880, 92160], 'L': [ 96, 491520, 368640]}, ## WID 6 + {'US': [ 1, 7680, 5760], 'S': [ 6, 46080, 34560], 'M': [ 24, 184320, 138240], 'L': [ 96, 737280, 552960]}, ## WID 7 + {'US': [ 1, 10240, 7680], 'S': [ 6, 61440, 46080], 'M': [ 24, 245760, 184320], 'L': [ 96, 983040, 737280]}, ## WID 8 + {'US': [ 1, 12800, 9600], 'S': [ 6, 76800, 57600], 'M': [ 24, 307200, 230400], 'L': [ 96, 1228800, 921600]}, ## WID 9 + {'US': [ 1, 15360, 11520], 'S': [ 6, 92160, 69120], 'M': [ 24, 368640, 276480], 'L': [ 96, 1474560, 1105920]}, ## WID 10 + {'US': [ 1, 17280, 15360], 'S': [ 6, 103680, 92160], 'M': [ 24, 414720, 368640], 'L': [ 96, 1658880, 1474560]}, ## WID 11 + {'US': [ 1, 23040, 19200], 'S': [ 6, 138240, 115200], 'M': [ 24, 552960, 460800], 'L': [ 96, 2211840, 1843200]}] ## WID 12 +} + +BW_INTL_INCR = { ## [BW][WID][INTL] -> interleaver increment + '3 kHz': [ + {'US': '-', 'S': 11, 'M': 37, 'L': 145}, ## WID 0 + {'US': 25, 'S': 97, 'M': 385, 'L': 1543}, ## WID 1 + {'US': 25, 'S': 97, 'M': 385, 'L': 1543}, ## WID 2 + {'US': 25, 'S': 97, 'M': 385, 'L': 1549}, ## WID 3 + {'US': 25, 'S': 97, 'M': 385, 'L': 1549}, ## WID 4 + {'US': 33, 'S': 129, 'M': 513, 'L': 2081}, ## WID 5 + {'US': 65, 'S': 257, 'M': 1025, 'L': 4161}, ## WID 6 + {'US': 97, 'S': 385, 'M': 1537, 'L': 6241}, ## WID 7 + {'US': 129, 'S': 513, 'M': 2049, 'L': 8321}, ## WID 8 + {'US': 161, 'S': 641, 'M': 2561, 'L': 10403}, ## WID 9 + {'US': 193, 'S': 769, 'M': 3073, 'L': 12481}, ## WID 10 + {'US': 271, 'S': 1081, 'M': 4321, 'L': 17551}, ## WID 11 + {'US': 361, 'S': 1441, 'M': 5761, 'L': 23401}, ## WID 12 + {'US': 65, 'S': 257, 'M': 1025, 'L': 4161}], ## WID 13 + '6 kHz': [ + {'US': '-', 'S': 21, 'M': 73, 'L': 289}, ## WID 0 + {'US': 49, 'S': 193, 'M': 769, 'L': 3085}, ## WID 1 + {'US': 49, 'S': 193, 'M': 769, 'L': 3085}, ## WID 2 + {'US': 53, 'S': 205, 'M': 817, 'L': 3293}, ## WID 3 + {'US': 53, 'S': 205, 'M': 817, 'L': 3293}, ## WID 4 + {'US': 69, 'S': 273, 'M': 1089, 'L': 4421}, ## WID 5 + {'US': 137, 'S': 545, 'M': 2177, 'L': 8841}, ## WID 6 + {'US': 205, 'S': 817, 'M': 3265, 'L': 13261}, ## WID 7 + {'US': 273, 'S': 1089, 'M': 4353, 'L': 17681}, ## WID 8 + {'US': 341, 'S': 1361, 'M': 5441, 'L': 22103}, ## WID 9 + {'US': 409, 'S': 1633, 'M': 6529, 'L': 26521}, ## WID 10 + {'US': 409, 'S': 1621, 'M': 6481, 'L': 26329}, ## WID 11 + {'US': 553, 'S': 2161, 'M': 8641, 'L': 35113}], ## WID 12 + '9 kHz': [ + {'US': '-', 'S': 31, 'M': 109, 'L': 433}, ## WID 0 + {'US': 73, 'S': 289, 'M': 1153, 'L': 4645}, ## WID 1 + {'US': 73, 'S': 289, 'M': 1153, 'L': 4645}, ## WID 2 + {'US': 73, 'S': 289, 'M': 1153, 'L': 4645}, ## WID 3 + {'US': '-', 'S': '-', 'M': '-', 'L': '-'}, ## WID 4 + {'US': 97, 'S': 385, 'M': 1537, 'L': 6241}, ## WID 5 + {'US': 193, 'S': 769, 'M': 3073, 'L': 12481}, ## WID 6 + {'US': 289, 'S': 1153, 'M': 4609, 'L': 18721}, ## WID 7 + {'US': 385, 'S': 1537, 'M': 6145, 'L': 24961}, ## WID 8 + {'US': 481, 'S': 1921, 'M': 7681, 'L': 31207}, ## WID 9 + {'US': 577, 'S': 2305, 'M': 9217, 'L': 37441}, ## WID 10 + {'US': 811, 'S': 3241, 'M': 13771, 'L': 52651}, ## WID 11 + {'US': 1081, 'S': 4321, 'M': 18361, 'L': 70201}], ## WID 12 + '12 kHz': [ + {'US': '-', 'S': 41, 'M': 145, 'L': 577}, ## WID 0 + {'US': 73, 'S': 289, 'M': 1153, 'L': 4633}, ## WID 1 + {'US': 73, 'S': 289, 'M': 1153, 'L': 4633}, ## WID 2 + {'US': 97, 'S': 385, 'M': 1537, 'L': 6193}, ## WID 3 + {'US': 97, 'S': 385, 'M': 1537, 'L': 6193}, ## WID 4 + {'US': 129, 'S': 513, 'M': 2049, 'L': 8321}, ## WID 5 + {'US': 257, 'S': 1025, 'M': 4097, 'L': 16641}, ## WID 6 + {'US': 385, 'S': 1537, 'M': 6145, 'L': 24961}, ## WID 7 + {'US': 513, 'S': 2049, 'M': 8193, 'L': 33281}, ## WID 8 + {'US': 641, 'S': 2561, 'M': 10241, 'L': 41603}, ## WID 9 + {'US': 769, 'S': 3073, 'M': 13057, 'L': 49921}, ## WID 10 + {'US': 811, 'S': 3241, 'M': 13771, 'L': 52651}, ## WID 11 + {'US': 1081, 'S': 4321, 'M': 18361, 'L': 70201}], ## WID 12 + '15 kHz': [ + {'US': '-', 'S': 51, 'M': 181, 'L': 721}, ## WID 0 + {'US': 109, 'S': 433, 'M': 1729, 'L': 6949}, ## WID 1 + {'US': 109, 'S': 433, 'M': 1729, 'L': 6949}, ## WID 2 + {'US': 109, 'S': 433, 'M': 1729, 'L': 6949}, ## WID 3 + {'US': 109, 'S': 433, 'M': 1729, 'L': 6949}, ## WID 4 + {'US': 161, 'S': 641, 'M': 2561, 'L': 10401}, ## WID 5 + {'US': 321, 'S': 1281, 'M': 5121, 'L': 20801}, ## WID 6 + {'US': 481, 'S': 1921, 'M': 7681, 'L': 31201}, ## WID 7 + {'US': 641, 'S': 2561, 'M': 10241, 'L': 41601}, ## WID 8 + {'US': 801, 'S': 3201, 'M': 13603, 'L': 52003}, ## WID 9 + {'US': 961, 'S': 3841, 'M': 16321, 'L': 62401}, ## WID 10 + {'US': 865, 'S': 3457, 'M': 14689, 'L': 56161}, ## WID 11 + {'US': 1153, 'S': 4609, 'M': 19585, 'L': 74881}], ## WID 12 + '18 kHz': [ + {'US': '-', 'S': 61, 'M': 217, 'L': 865}, ## WID 0 + {'US': 169, 'S': 673, 'M': 2689, 'L': 10811}, ## WID 1 + {'US': 169, 'S': 673, 'M': 2689, 'L': 10811}, ## WID 2 + {'US': 169, 'S': 673, 'M': 2689, 'L': 10811}, ## WID 3 + {'US': '-', 'S': '-', 'M': '-', 'L': '-'}, ## WID 4 + {'US': 193, 'S': 769, 'M': 3073, 'L': 12481}, ## WID 5 + {'US': 385, 'S': 1537, 'M': 6145, 'L': 24961}, ## WID 6 + {'US': 577, 'S': 2305, 'M': 9217, 'L': 37441}, ## WID 7 + {'US': 769, 'S': 3073, 'M': 13057, 'L': 49921}, ## WID 8 + {'US': 961, 'S': 3841, 'M': 16327, 'L': 62407}, ## WID 9 + {'US': 1153, 'S': 4609, 'M': 19585, 'L': 74881}, ## WID 10 + {'US': 1441, 'S': 5761, 'M': 24481, 'L': 93601}, ## WID 11 + {'US': 1921, 'S': 7681, 'M': 32641, 'L': 124801}], ## WID 12 + '21 kHz': [ + {'US': '-', 'S': 71, 'M': 253, 'L': 1009}, ## WID 0 + {'US': 161, 'S': 641, 'M': 2561, 'L': 10281}, ## WID 1 + {'US': 161, 'S': 641, 'M': 2561, 'L': 10281}, ## WID 2 + {'US': 161, 'S': 641, 'M': 2561, 'L': 10281}, ## WID 3 + {'US': 161, 'S': 641, 'M': 2561, 'L': 10281}, ## WID 4 + {'US': 169, 'S': 673, 'M': 2689, 'L': 10921}, ## WID 5 + {'US': 337, 'S': 1345, 'M': 5377, 'L': 21841}, ## WID 6 + {'US': 505, 'S': 2017, 'M': 8065, 'L': 32761}, ## WID 7 + {'US': 673, 'S': 2689, 'M': 11425, 'L': 43681}, ## WID 8 + {'US': 841, 'S': 3361, 'M': 14293, 'L': 54613}, ## WID 9 + {'US': 1009, 'S': 4033, 'M': 17137, 'L': 65521}, ## WID 10 + {'US': 1921, 'S': 7681, 'M': 32641, 'L': 124801}, ## WID 11 + {'US': 2561, 'S': 10241, 'M': 43521, 'L': 166401}], ## WID 12 + '24 kHz': [ + {'US': '-', 'S': 81, 'M': 289, 'L': 1153}, ## WID 0 + {'US': 137, 'S': 545, 'M': 2177, 'L': 8739}, ## WID 1 + {'US': 137, 'S': 545, 'M': 2177, 'L': 8739}, ## WID 2 + {'US': 205, 'S': 817, 'M': 3265, 'L': 13159}, ## WID 3 + {'US': 205, 'S': 817, 'M': 3265, 'L': 13159}, ## WID 4 + {'US': 273, 'S': 1089, 'M': 4353, 'L': 17681}, ## WID 5 + {'US': 545, 'S': 2177, 'M': 8705, 'L': 35361}, ## WID 6 + {'US': 817, 'S': 3265, 'M': 13873, 'L': 53041}, ## WID 7 + {'US': 1089, 'S': 4353, 'M': 18497, 'L': 70721}, ## WID 8 + {'US': 1361, 'S': 5441, 'M': 23123, 'L': 88403}, ## WID 9 + {'US': 1633, 'S': 6529, 'M': 27745, 'L': 106081}, ## WID 10 + {'US': 1441, 'S': 5761, 'M': 24481, 'L': 93601}, ## WID 11 + {'US': 1921, 'S': 7681, 'M': 32641, 'L': 124801}], ## WID 12 + '30 kHz': [ + {'US': '-', 'S': 119, 'M': 431, 'L': 1673}, ## WID 0 + {'US': 275, 'S': 1259, 'M': 4207, 'L': 15445}, ## WID 1 + {'US': 275, 'S': 1259, 'M': 4207, 'L': 15445}, ## WID 2 + {'US': 275, 'S': 1259, 'M': 4207, 'L': 15445}, ## WID 3 + {'US': 275, 'S': 1259, 'M': 4207, 'L': 15445}, ## WID 4 + {'US': 381, 'S': 2951, 'M': 8729, 'L': 24989}, ## WID 5 + {'US': 781, 'S': 5893, 'M': 29161, 'L': 49991}, ## WID 6 + {'US': 1207, 'S': 8821, 'M': 26221, 'L': 71341}, ## WID 7 + {'US': 1529, 'S': 11837, 'M': 43733, 'L': 142017}, ## WID 8 + {'US': 1941, 'S': 14711, 'M': 54541, 'L': 221051}, ## WID 9 + {'US': 2257, 'S': 17623, 'M': 65557, 'L': 288127}, ## WID 10 + {'US': 2473, 'S': 18643, 'M': 55669, 'L': 281233}, ## WID 11 + {'US': 3281, 'S': 25097, 'M': 74329, 'L': 374993}], ## WID 12 + '36 kHz': [ + {'US': '-', 'S': 143, 'M': 511, 'L': 2011}, ## WID 0 + {'US': 901, 'S': 1577, 'M': 11843, 'L': 22595}, ## WID 1 + {'US': 901, 'S': 1577, 'M': 11843, 'L': 22595}, ## WID 2 + {'US': 901, 'S': 1577, 'M': 11843, 'L': 22595}, ## WID 3 + {'US': 455, 'S': 3557, 'M': 17575, 'L': 26663}, ## WID 4 + {'US': 455, 'S': 3557, 'M': 17575, 'L': 26663}, ## WID 5 + {'US': 911, 'S': 7027, 'M': 35179, 'L': 69763}, ## WID 6 + {'US': 1633, 'S': 10867, 'M': 52741, 'L': 90013}, ## WID 7 + {'US': 1913, 'S': 14245, 'M': 70337, 'L': 119977}, ## WID 8 + {'US': 2411, 'S': 17831, 'M': 87931, 'L': 133351}, ## WID 9 + {'US': 2719, 'S': 22267, 'M': 105163, 'L': 174025}, ## WID 10 + {'US': 2851, 'S': 22381, 'M': 110509, 'L': 197491}, ## WID 11 + {'US': 4049, 'S': 29977, 'M': 148361, 'L': 281057}], ## WID 12 + '40 kHz': [ + {'US': '-', 'S': 167, 'M': 601, 'L': 2371}, ## WID 0 + {'US': 1001, 'S': 1687, 'M': 5867, 'L': 30619}, ## WID 1 + {'US': 1001, 'S': 1687, 'M': 5867, 'L': 30619}, ## WID 2 + {'US': 1001, 'S': 1687, 'M': 5867, 'L': 30619}, ## WID 3 + {'US': 593, 'S': 4007, 'M': 11789, 'L': 33757}, ## WID 4 + {'US': 593, 'S': 4007, 'M': 11789, 'L': 33757}, ## WID 5 + {'US': 1019, 'S': 7993, 'M': 23641, 'L': 67505}, ## WID 6 + {'US': 1531, 'S': 11995, 'M': 35335, 'L': 101203}, ## WID 7 + {'US': 2029, 'S': 15965, 'M': 47137, 'L': 134965}, ## WID 8 + {'US': 2581, 'S': 19901, 'M': 58871, 'L': 168701}, ## WID 9 + {'US': 3079, 'S': 23995, 'M': 70669, 'L': 202411}, ## WID 10 + {'US': 3499, 'S': 26539, 'M': 78571, 'L': 249943}, ## WID 11 + {'US': 4673, 'S': 35281, 'M': 104857, 'L': 333113}], ## WID 12 + '48 kHz': [ + {'US': '-', 'S': 191, 'M': 697, 'L': 2693}, ## WID 0 + {'US': 943, 'S': 1735, 'M': 6805, 'L': 14425}, ## WID 1 + {'US': 943, 'S': 1735, 'M': 6805, 'L': 14425}, ## WID 2 + {'US': 943, 'S': 1735, 'M': 6805, 'L': 14425}, ## WID 3 + {'US': 397, 'S': 2941, 'M': 8393, 'L': 39551}, ## WID 4 + {'US': 397, 'S': 2941, 'M': 8393, 'L': 39551}, ## WID 5 + {'US': 797, 'S': 5881, 'M': 16739, 'L': 72607}, ## WID 6 + {'US': 1231, 'S': 8827, 'M': 25099, 'L': 104197}, ## WID 7 + {'US': 1509, 'S': 11729, 'M': 46729, 'L': 137413}, ## WID 8 + {'US': 1911, 'S': 14701, 'M': 41981, 'L': 181541}, ## WID 9 + {'US': 2281, 'S': 17623, 'M': 70183, 'L': 217873}, ## WID 10 + {'US': 2569, 'S': 19831, 'M': 56599, 'L': 247963}, ## WID 11 + {'US': 3401, 'S': 26449, 'M': 105209, 'L': 326729}] ## WID 12 +} + +## ---- Walsh-4 codes ---------------------------------------------------------- +WALSH = np.array([[0,0,0,0], # 0 - 00 + [0,1,0,1], # 1 - 01 + [0,0,1,1], # 2 - 10 + [0,1,1,0]], # 3 - 11 + dtype=np.uint8) + +FROM_WALSH = -np.ones(256, dtype=np.int8) +for i in range(4): + FROM_WALSH[np.packbits(WALSH[i][:])[0]] = i + +## ---- band width - Walsh lengths table --------------------------------------- +WALSH_BW_LENGTHS = { + '3 kHz': 32, + '6 kHz': 64, + '9 kHz': 96, + '12 kHz': 128, + '15 kHz': 160, + '18 kHz': 192, + '21 kHz': 224, + '24 kHz': 256, + '30 kHz': 320, ## by extrapolation using 32 * BW / 3kHz + '36 kHz': 384, + '42 kHz': 448, + '48 kHz': 512 +} + +## ---- di-bit scramble sequences for preamble --------------------------------- +FIXED_PN = np.array( + [2,4,0,0,6,2,1,4,6,1,0,5,7,3,4,1,2,6,1,7,0,7,3,2,2,2,3,2,4,6,3,6, + 6,3,7,5,4,7,5,6,7,4,0,2,6,1,5,3,0,4,2,4,6,4,5,2,5,4,5,3,1,5,4,5, + 6,5,1,0,7,1,0,1,0,5,3,5,2,2,4,5,4,0,6,4,1,4,0,3,3,0,0,3,3,7,3,4, + 2,7,4,4,4,0,3,4,7,6,4,2,6,2,0,3,5,3,2,2,4,5,2,0,0,3,5,0,3,2,6,6, + 1,4,2,3,6,1,3,0,3,3,2,4,2,2,6,5,5,3,6,7,6,5,6,6,5,2,5,4,2,3,3,3, + 5,7,5,5,3,7,0,4,7,0,4,1,6,2,3,5,5,6,2,6,4,6,3,4,0,7,0,0,5,2,1,5, + 4,3,4,5,7,0,5,3,7,6,6,6,4,5,6,0,2,0,4,2,3,4,4,0,7,6,6,2,0,0,3,3, + 0,5,2,4,2,2,4,5,4,6,6,6,3,2,1,0,3,2,6,0,6,2,4,0,6,4,1,3,3,5,3,6], + dtype=np.uint8) +CNT_PN = np.array( + [5,5,2,2,0,2,5,6,7,1,3,5,1,5,6,5,3,7,0,4,0,3,3,2,1,3,0,3,1,6,2,6, + 0,6,4,1,2,5,6,3,5,3,7,4,2,6,7,3,0,2,0,1,7,5,0,6,1,5,0,3,2,2,5,2, + 5,2,3,4,2,7,6,1,1,5,2,1,5,4,0,3,5,5,0,3,1,4,0,5,0,3,0,6,0,0,3,1, + 6,1,4,4,7,7,0,5,7,0,1,5,1,0,1,3,1,5,0,7,1,2,2,2,7,1,2,5,0,3,3,2, + 2,0,4,5,1,3,1,3,5,3,1,7,5,2,7,1,3,1,5,6,2,4,6,0,6,1,0,0,3,6,2,7, + 3,2,4,7,6,4,1,3,6,6,0,3,0,0,7,5,4,5,1,2,1,5,0,3,1,0,4,6,6,1,0,5, + 2,6,3,2,7,4,2,4,0,1,7,0,7,0,5,1,4,5,7,2,0,4,4,3,5,2,7,7,4,5,1,4, + 4,6,3,3,0,5,1,5,5,4,3,2,0,3,0,4,7,4,5,1,5,5,7,7,6,2,4,3,5,2,2,4], + dtype=np.uint8) +WID_PN = np.array( + [2,3,0,3,7,3,3,0,1,4,4,6,5,5,4,5,6,2,0,5,6,6,5,3,5,5,2,2,1,2,3,6, + 1,1,4,3,1,0,5,1,0,3,3,0,3,0,4,4,6,2,5,6,1,7,2,6,2,0,0,4,7,2,3,5, + 2,7,1,6,5,0,4,1,6,2,1,5,4,3,5,0,3,4,1,3,2,1,6,1,5,7,0,4,7,6,6,0, + 4,7,6,6,6,6,2,3,5,0,7,0,3,1,5,1,2,0,5,3,2,4,5,6,6,7,7,3,5,1,6,0, + 1,4,4,5,6,0,6,7,2,4,4,0,3,7,2,0,0,1,4,0,7,1,7,4,5,4,5,5,5,3,3,2, + 0,5,1,3,1,5,3,4,1,5,4,1,4,4,2,2,4,3,0,7,4,1,5,7,1,4,7,2,5,5,6,6, + 1,6,5,6,3,0,2,5,7,7,4,4,3,4,4,6,0,7,2,2,0,0,2,1,0,0,3,6,6,4,0,2, + 4,3,4,5,2,6,3,7,7,5,7,3,0,7,0,0,7,2,6,2,2,6,1,4,3,7,6,5,0,6,5,4], + dtype=np.uint8); + +FIXED_WALSH_SYMBOLS = [0,0,2, 1,2,1, 0,2,3] + +## ---- data scrambler ----------------------------------------------------------- +class ScrambleData(object): + """data scrambling sequence generator""" + def __init__(self, nbits): + self._nbits = nbits + self.reset() + + def reset(self): + self._state = [0,0,0,0,0,0,0,0,1] + + def next(self): + val = 0 + for i in range(self._nbits): + val += (1< 0.7 if self._state == 'MP' else True + return [a,mode,success,success] + + def get_next_data_frame(self, success): + ## TODO + if self._state == 'MP': + self._state = 'DATA' + a = np.zeros(self._known, common.SYMB_SCRAMBLE_DTYPE) + if (self._frame_counter % self._intl_frames) == self._intl_frames-1: + a['symb'][:] = self._mp_shifted + a['scramble'][:] = self._mp_shifted + else: + a['symb'][:] = self._mp + a['scramble'][:] = self._mp + return MODE_BPSK,a + else: + self._state = 'MP' + a = np.zeros(self._unknown, common.SYMB_SCRAMBLE_DTYPE) + self._scr_data.reset() + for i in range(self._unknown): ## TODO: handle QAM modes + a['scramble'][i] = np.exp(2j*np.pi*self._scr_data.next()/8) + return self._data_mode,a + + def get_doppler(self, iq_samples): + """quality check and doppler estimation for preamble""" + success,doppler = True,0 + ## TODO + return success,doppler + + def decode_walsh(self, symbols): + wlen = self._wlen + return np.array([FROM_WALSH[np.packbits + (np.real + (np.sum + (symbols[wlen*i:wlen*(i+1)].reshape((wlen//4,4)),0))<0)[0]] + for i in range(len(symbols)//wlen)], + dtype=np.uint8) + + def decode_fixed(self, symbols): + print('decode_fixed: ', len(symbols)) + data = self.decode_walsh(symbols) + success = np.all(data == FIXED_WALSH_SYMBOLS) + print('data=', data, success) + return success + + def decode_cnt(self, symbols): + print('decode_cnt: ', len(symbols)) + data = self.decode_walsh(symbols) + print('data=', data) + b = np.unpackbits(data.reshape(4,1), axis=1) + b = np.concatenate([b[i][6:8] for i in range(4)]) + b = np.flip(b) + b[7] ^= b[1] ^ b[2] ^ b[3] + b[6] ^= b[2] ^ b[3] ^ b[4] + b[5] ^= b[0] ^ b[1] ^ b[2] + success = np.all(b[5:8] == 0) + b = np.flip(b) + self._superframe_counter = np.packbits(b)[0] + print('b=', b, success, self._superframe_counter) + return success + + def decode_wid(self, symbols): + print('decode_wid: ', len(symbols)) + data = self.decode_walsh(symbols) + print('data=', data) + b = np.unpackbits(data.reshape(5,1), axis=1) + b = np.concatenate([b[i][6:8] for i in range(5)]) + b = np.flip(b) + b[2] ^= b[9] ^ b[8] ^ b[7] + b[1] ^= b[7] ^ b[6] ^ b[5] + b[0] ^= b[5] ^ b[4] ^ b[3] + success = np.all(b[0:3] == 0) + b = np.flip(b) + self._wid = np.packbits(b[0:4])[0]>>4 + self._intl_type = INTERLEAVERS[np.packbits(b[4:6])[0]>>6] + self._constraint_length = b[6] + self._data_mode = WID_MODE[self._wid] + self._unknown = BW_UNKNOWN[self._bw][self._wid] + self._known = BW_KNOWN[self._bw][self._wid] + mp_info = MP_LEN_BASE_SHIFT[self._known] + self._mp = make_mp(self._known, mp_info['base_len'], 0) + self._mp_shifted = make_mp(self._known, mp_info['base_len'], mp_info['base_shift']) + self._intl_info = BW_INTL[self._bw][self._wid][self._intl_type] + self._intl_frames = self._intl_info[0] + print('b=', b, success, self._wid, self._intl_type, self._intl_frames, self._constraint_length, + self._known, self._unknown) + return success + + def set_mode(self, bw): + self._bw = bw + ## ---- di-bit codes ----------------------------------------------------------- + self._wlen = wlen = WALSH_BW_LENGTHS[bw] + DIBIT = np.zeros((4,wlen), dtype=np.uint8) + for i in range(4): + DIBIT[i][:] = np.concatenate([WALSH[i][:] for _ in range(wlen//4)]) + ## ---- preamble symbols --------------------------------------------------------- + SYNC_SYMB = common.n_psk(2, np.concatenate([DIBIT[i][:] + for i in FIXED_WALSH_SYMBOLS])) + CNT_SYMB = np.zeros(4*wlen, dtype=np.complex64) + WID_SYMB = np.zeros(5*wlen, dtype=np.complex64) + ## ---- preamble scramble symbols ------------------------------------------------ + SYNC_SCR = common.n_psk(8, np.concatenate([FIXED_PN for _ in range(9)])) + CNT_SCR = common.n_psk(8, np.concatenate([CNT_PN for _ in range(4)])) + WID_SCR = common.n_psk(8, np.concatenate([WID_PN for _ in range(5)])) + + self._fixed_s = np.array(zip(SYNC_SCR*SYNC_SYMB, + SYNC_SCR), + common.SYMB_SCRAMBLE_DTYPE) + self._cnt_s = np.array(zip(CNT_SCR*CNT_SYMB, + CNT_SCR), + common.SYMB_SCRAMBLE_DTYPE) + self._wid_s = np.array(zip(WID_SCR*WID_SYMB, + WID_SCR), + common.SYMB_SCRAMBLE_DTYPE) + + + def decode_soft_dec(self, soft_dec): + print('decode_soft_dec', len(soft_dec), soft_dec.dtype) + return soft_dec ## TODO + + def get_preamble(): + """fixed symbols + scrambler""" + return self._fixed_s + + def get_preamble_z(self): + """preamble symbols for preamble correlation""" + return 2,np.array([z for z in self._fixed_s['symb'][0:3*self._wlen] + for _ in range(self._sps)]) + +if __name__ == '__main__': + p = PhysicalLayer(5) + p.set_mode('24 kHz') + s = ScrambleData(3) + for i in range(10): + print(i, s.next()) + print(np.real(make_mp(24,13,0))) + print(np.real(make_mp(24,13,6))) + #print(make_mp(72,36,0)) diff --git a/python/physical_layer/STANAG_4285.py b/python/physical_layer/STANAG_4285.py index 86f51cd..9ea87cd 100644 --- a/python/physical_layer/STANAG_4285.py +++ b/python/physical_layer/STANAG_4285.py @@ -123,7 +123,7 @@ class PhysicalLayer(object): 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)]) + a = np.zeros(80, common.SYMB_SCRAMBLE_DTYPE) ## BPSK modulation constellation = PhysicalLayer.make_psk(2,range(2))['points'] a['symb'] = constellation[p,] @@ -140,7 +140,7 @@ class PhysicalLayer(object): p[i] = np.sum(state[-3:]*[4,2,1]) for _ in range(3): state = np.concatenate(([np.sum(state&taps)&1], state[0:-1])) - a=np.zeros(176, dtype=[('symb',np.complex64), ('scramble', np.complex64)]) + a = np.zeros(176, common.SYMB_SCRAMBLE_DTYPE) ## 8PSK modulation constellation = PhysicalLayer.make_psk(8,range(8))['points'] a['scramble'] = constellation[p,] diff --git a/python/physical_layer/common.py b/python/physical_layer/common.py index 375aacb..93e5dad 100644 --- a/python/physical_layer/common.py +++ b/python/physical_layer/common.py @@ -5,6 +5,9 @@ import numpy as np CONST_DTYPE=np.dtype([('points', np.complex64), ('symbols', np.int32)]) +SYMB_SCRAMBLE_DTYPE=np.dtype([('symb', np.complex64), + ('scramble', np.complex64)]) + def n_psk(n,x): """n-ary PSK constellation""" return np.complex64(np.exp(2j*np.pi*x/n)) diff --git a/python/physical_layer_driver.py b/python/physical_layer_driver.py index 7060933..4180338 100644 --- a/python/physical_layer_driver.py +++ b/python/physical_layer_driver.py @@ -55,7 +55,7 @@ class physical_layer_driver(gr.hier_block2): ## TODO: get rrc tap information from physical layer description self._rrc_taps = filter.firdes.root_raised_cosine(1.0, samp_rate, samp_rate/sps, 0.35, 11*sps) preamble_offset,preamble_samples = self._physical_layer_driver_description.get_preamble_z() - preamble_length = sps*len(self._physical_layer_driver_description.get_preamble()) + preamble_length = len(preamble_samples) self._rrc_filter = filter.fir_filter_ccc(1, (self._rrc_taps)) self._corr_est = digital.corr_est_cc(symbols = (preamble_samples.tolist()), sps = sps,