add two missing files (gsm0503.h / conv_gen.py) to previous commit
diff --git a/utils/conv_gen.py b/utils/conv_gen.py
new file mode 100644
index 0000000..1377fca
--- /dev/null
+++ b/utils/conv_gen.py
@@ -0,0 +1,494 @@
+#!/usr/bin/python
+
+mod_license = """
+/*
+ * Copyright (C) 2011-2016 Sylvain Munaut <tnt@246tNt.com>
+ * Copyright (C) 2016 sysmocom s.f.m.c. GmbH
+ *
+ * All Rights Reserved
+ *
+ * This program is free software; you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation; either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * This program is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License along
+ * with this program; if not, write to the Free Software Foundation, Inc.,
+ * 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
+ */
+"""
+
+import sys, os, math
+
+class ConvolutionalCode(object):
+
+ def __init__(self, block_len, polys, name = "call-me", description = "LOL", puncture = []):
+ # Save simple params
+ self.block_len = block_len
+ self.k = 1
+ self.puncture = puncture
+ self.rate_inv = len(polys)
+
+ # Infos
+ self.name = name
+ self.description = description
+
+ # Handle polynoms (and check for recursion)
+ self.polys = [(1, 1) if x[0] == x[1] else x for x in polys]
+
+ # Determine the polynomial degree
+ for (x, y) in polys:
+ self.k = max(self.k, int(math.floor(math.log(max(x, y), 2))))
+ self.k = self.k + 1
+
+ self.poly_divider = 1
+ rp = [x[1] for x in self.polys if x[1] != 1]
+ if rp:
+ if not all([x == rp[0] for x in rp]):
+ raise ValueError("Bad polynoms: Can't have multiple different divider polynoms !")
+ if not all([x[0] == 1 for x in polys if x[1] == 1]):
+ raise ValueError("Bad polynoms: Can't have a '1' divider with a non '1' dividend in a recursive code")
+ self.poly_divider = rp[0]
+
+ @property
+ def recursive(self):
+ return self.poly_divider != 1
+
+ @property
+ def _state_mask(self):
+ return (1 << (self.k - 1)) - 1
+
+ def next_state(self, state, bit):
+ nb = combine(
+ (state << 1) | bit,
+ self.poly_divider,
+ self.k,
+ )
+ return ((state << 1) | nb) & self._state_mask
+
+ def next_term_state(self, state):
+ return (state << 1) & self._state_mask
+
+ def next_output(self, state, bit, ns = None):
+ # Next state bit
+ if ns is None:
+ ns = self.next_state(state, bit)
+
+ src = (ns & 1) | (state << 1)
+
+ # Scan polynoms
+ rv = []
+ for p_n, p_d in self.polys:
+ if self.recursive and p_d == 1:
+ o = bit # No choice ... (systematic output in recursive case)
+ else:
+ o = combine(src, p_n, self.k)
+ rv.append(o)
+
+ return rv
+
+ def next_term_output(self, state, ns = None):
+ # Next state bit
+ if ns is None:
+ ns = self.next_term_state(state)
+
+ src = (ns & 1) | (state << 1)
+
+ # Scan polynoms
+ rv = []
+ for p_n, p_d in self.polys:
+ if self.recursive and p_d == 1:
+ # Systematic output are replaced when in 'termination' mode
+ o = combine(src, self.poly_divider, self.k)
+ else:
+ o = combine(src, p_n, self.k)
+ rv.append(o)
+
+ return rv
+
+ def next(self, state, bit):
+ ns = self.next_state(state, bit)
+ nb = self.next_output(state, bit, ns = ns)
+ return ns, nb
+
+ def next_term(self, state):
+ ns = self.next_term_state(state)
+ nb = self.next_term_output(state, ns = ns)
+ return ns, nb
+
+ def _print_term(self, fi, num_states, pack = False):
+ d = []
+ for state in range(num_states):
+ x = pack(self.next_term_output(state)) if pack else self.next_term_state(state)
+ d.append("%d, " % x)
+ print >>fi, "\t%s" % ''.join(d)
+
+ def _print_x(self, fi, num_states, pack = False):
+ for state in range(num_states):
+ x0 = pack(self.next_output(state, 0)) if pack else self.next_state(state, 0)
+ x1 = pack(self.next_output(state, 1)) if pack else self.next_state(state, 1)
+ print >>fi, "\t{ %2d, %2d }," % (x0, x1)
+
+ def gen_tables(self, pref, fi):
+ pack = lambda n: sum([x << (self.rate_inv - i - 1) for i, x in enumerate(n)])
+ num_states = 1 << (self.k - 1)
+ print >>fi, "\nstatic const uint8_t %s_state[][2] = {" % self.name
+ self._print_x(fi, num_states)
+ print >>fi, "};\n\nstatic const uint8_t %s_output[][2] = {" % self.name
+ self._print_x(fi, num_states, pack)
+ print >>fi, "};"
+
+ if self.recursive:
+ print >>fi, "\nstatic const uint8_t %s_term_state[] = {" % self.name
+ self._print_term(fi, num_states)
+ print >>fi, "};\n\nstatic const uint8_t %s_term_output[] = {" % self.name
+ self._print_term(fi, num_states, pack)
+ print >>fi, "};"
+
+ if len(self.puncture):
+ print >>fi, "\nstatic const int %s_puncture[] = {" % self.name
+ for p in self.puncture:
+ print >>fi, "\t%d," % p
+ print >>fi, "};"
+
+ print >>fi, "\n/* %s */" % self.description
+ print >>fi, "const struct osmo_conv_code %s_%s = {" % (pref, self.name)
+ print >>fi, "\t.N = %d," % self.rate_inv
+ print >>fi, "\t.K = %d," % self.k
+ print >>fi, "\t.len = %d," % self.block_len
+ print >>fi, "\t.next_output = %s_output," % self.name
+ print >>fi, "\t.next_state = %s_state," % self.name
+ if self.recursive:
+ print >>fi, "\t.next_term_output = %s_term_output," % self.name
+ print >>fi, "\t.next_term_state = %s_term_state," % self.name
+ if len(self.puncture):
+ print >>fi, "\t.puncture = %s_puncture," % self.name
+ print >>fi, "};"
+
+poly = lambda *args: sum([(1 << x) for x in args])
+
+def combine(src, sel, nb):
+ x = src & sel
+ fn_xor = lambda x, y: x ^ y
+ return reduce(fn_xor, [(x >> n) & 1 for n in range(nb)])
+
+# Polynomials according to 3GPP TS 05.03 Annex B
+G0 = poly(0, 3, 4)
+G1 = poly(0, 1, 3, 4)
+G2 = poly(0, 2, 4)
+G3 = poly(0, 1, 2, 3, 4)
+G4 = poly(0, 2, 3, 5, 6)
+G5 = poly(0, 1, 4, 6)
+G6 = poly(0, 1, 2, 3, 4, 6)
+G7 = poly(0, 1, 2, 3, 6)
+
+CCH_poly = [
+ ( G0, 1 ),
+ ( G1, 1 )
+]
+
+xCCH = ConvolutionalCode(
+ 224,
+ CCH_poly,
+ name = "xcch",
+ description =""" *CCH convolutional code:
+ 228 bits blocks, rate 1/2, k = 5
+ G0 = 1 + D3 + D4
+ G1 = 1 + D + D3 + D4
+"""
+)
+
+CS2 = ConvolutionalCode(
+ 290,
+ CCH_poly,
+ puncture = [
+ 15, 19, 23, 27, 31, 35, 43, 47, 51, 55, 59, 63, 67, 71,
+ 75, 79, 83, 91, 95, 99, 103, 107, 111, 115, 119, 123, 127, 131,
+ 139, 143, 147, 151, 155, 159, 163, 167, 171, 175, 179, 187, 191, 195,
+ 199, 203, 207, 211, 215, 219, 223, 227, 235, 239, 243, 247, 251, 255,
+ 259, 263, 267, 271, 275, 283, 287, 291, 295, 299, 303, 307, 311, 315,
+ 319, 323, 331, 335, 339, 343, 347, 351, 355, 359, 363, 367, 371, 379,
+ 383, 387, 391, 395, 399, 403, 407, 411, 415, 419, 427, 431, 435, 439,
+ 443, 447, 451, 455, 459, 463, 467, 475, 479, 483, 487, 491, 495, 499,
+ 503, 507, 511, 515, 523, 527, 531, 535, 539, 543, 547, 551, 555, 559,
+ 563, 571, 575, 579, 583, 587, -1
+ ],
+ name = "cs2",
+ description =""" CS2 convolutional code:
+ G0 = 1 + D3 + D4
+ G1 = 1 + D + D3 + D4
+"""
+)
+
+CS3 = ConvolutionalCode(
+ 334,
+ CCH_poly,
+ puncture = [
+ 15, 17, 21, 23, 27, 29, 33, 35, 39, 41, 45, 47, 51, 53,
+ 57, 59, 63, 65, 69, 71, 75, 77, 81, 83, 87, 89, 93, 95,
+ 99, 101, 105, 107, 111, 113, 117, 119, 123, 125, 129, 131, 135, 137,
+ 141, 143, 147, 149, 153, 155, 159, 161, 165, 167, 171, 173, 177, 179,
+ 183, 185, 189, 191, 195, 197, 201, 203, 207, 209, 213, 215, 219, 221,
+ 225, 227, 231, 233, 237, 239, 243, 245, 249, 251, 255, 257, 261, 263,
+ 267, 269, 273, 275, 279, 281, 285, 287, 291, 293, 297, 299, 303, 305,
+ 309, 311, 315, 317, 321, 323, 327, 329, 333, 335, 339, 341, 345, 347,
+ 351, 353, 357, 359, 363, 365, 369, 371, 375, 377, 381, 383, 387, 389,
+ 393, 395, 399, 401, 405, 407, 411, 413, 417, 419, 423, 425, 429, 431,
+ 435, 437, 441, 443, 447, 449, 453, 455, 459, 461, 465, 467, 471, 473,
+ 477, 479, 483, 485, 489, 491, 495, 497, 501, 503, 507, 509, 513, 515,
+ 519, 521, 525, 527, 531, 533, 537, 539, 543, 545, 549, 551, 555, 557,
+ 561, 563, 567, 569, 573, 575, 579, 581, 585, 587, 591, 593, 597, 599,
+ 603, 605, 609, 611, 615, 617, 621, 623, 627, 629, 633, 635, 639, 641,
+ 645, 647, 651, 653, 657, 659, 663, 665, 669, 671, -1
+ ],
+ name = "cs3",
+ description =""" CS3 convolutional code:
+ G0 = 1 + D3 + D4
+ G1 = 1 + D + D3 + D4
+"""
+)
+
+TCH_AFS_12_2 = ConvolutionalCode(
+ 250,
+ [
+ ( 1, 1 ),
+ ( G1, G0 ),
+ ],
+ puncture = [
+ 321, 325, 329, 333, 337, 341, 345, 349, 353, 357, 361, 363,
+ 365, 369, 373, 377, 379, 381, 385, 389, 393, 395, 397, 401,
+ 405, 409, 411, 413, 417, 421, 425, 427, 429, 433, 437, 441,
+ 443, 445, 449, 453, 457, 459, 461, 465, 469, 473, 475, 477,
+ 481, 485, 489, 491, 493, 495, 497, 499, 501, 503, 505, 507,
+ -1
+ ],
+ name = 'tch_afs_12_2',
+ description = """TCH/AFS 12.2 convolutional code:
+ 250 bits block, rate 1/2, punctured
+ G0/G0 = 1
+ G1/G0 = 1 + D + D3 + D4 / 1 + D3 + D4
+"""
+)
+
+TCH_AFS_10_2 = ConvolutionalCode(
+ 210,
+ [
+ ( G1, G3 ),
+ ( G2, G3 ),
+ ( 1, 1 ),
+ ],
+ puncture = [
+ 1, 4, 7, 10, 16, 19, 22, 28, 31, 34, 40, 43,
+ 46, 52, 55, 58, 64, 67, 70, 76, 79, 82, 88, 91,
+ 94, 100, 103, 106, 112, 115, 118, 124, 127, 130, 136, 139,
+ 142, 148, 151, 154, 160, 163, 166, 172, 175, 178, 184, 187,
+ 190, 196, 199, 202, 208, 211, 214, 220, 223, 226, 232, 235,
+ 238, 244, 247, 250, 256, 259, 262, 268, 271, 274, 280, 283,
+ 286, 292, 295, 298, 304, 307, 310, 316, 319, 322, 325, 328,
+ 331, 334, 337, 340, 343, 346, 349, 352, 355, 358, 361, 364,
+ 367, 370, 373, 376, 379, 382, 385, 388, 391, 394, 397, 400,
+ 403, 406, 409, 412, 415, 418, 421, 424, 427, 430, 433, 436,
+ 439, 442, 445, 448, 451, 454, 457, 460, 463, 466, 469, 472,
+ 475, 478, 481, 484, 487, 490, 493, 496, 499, 502, 505, 508,
+ 511, 514, 517, 520, 523, 526, 529, 532, 535, 538, 541, 544,
+ 547, 550, 553, 556, 559, 562, 565, 568, 571, 574, 577, 580,
+ 583, 586, 589, 592, 595, 598, 601, 604, 607, 609, 610, 613,
+ 616, 619, 621, 622, 625, 627, 628, 631, 633, 634, 636, 637,
+ 639, 640, -1
+ ],
+ name = 'tch_afs_10_2',
+ description = """TCH/AFS 10.2 kbits convolutional code:
+ G1/G3 = 1 + D + D3 + D4 / 1 + D + D2 + D3 + D4
+ G2/G3 = 1 + D2 + D4 / 1 + D + D2 + D3 + D4
+ G3/G3 = 1
+"""
+)
+
+TCH_AFS_7_95 = ConvolutionalCode(
+ 165,
+ [
+ ( 1, 1 ),
+ ( G5, G4 ),
+ ( G6, G4 ),
+ ],
+ puncture = [
+ 1, 2, 4, 5, 8, 22, 70, 118, 166, 214, 262, 310,
+ 317, 319, 325, 332, 334, 341, 343, 349, 356, 358, 365, 367,
+ 373, 380, 382, 385, 389, 391, 397, 404, 406, 409, 413, 415,
+ 421, 428, 430, 433, 437, 439, 445, 452, 454, 457, 461, 463,
+ 469, 476, 478, 481, 485, 487, 490, 493, 500, 502, 503, 505,
+ 506, 508, 509, 511, 512, -1
+ ],
+ name = 'tch_afs_7_95',
+ description = """TCH/AFS 7.95 kbits convolutional code:
+ G4/G4 = 1
+ G5/G4 = 1 + D + D4 + D6 / 1 + D2 + D3 + D5 + D6
+ G6/G4 = 1 + D + D2 + D3 + D4 + D6 / 1 + D2 + D3 + D5 + D6
+"""
+)
+
+TCH_AFS_7_4 = ConvolutionalCode(
+ 154,
+ [
+ ( G1, G3 ),
+ ( G2, G3 ),
+ ( 1, 1 ),
+ ],
+ puncture = [
+ 0, 355, 361, 367, 373, 379, 385, 391, 397, 403, 409, 415,
+ 421, 427, 433, 439, 445, 451, 457, 460, 463, 466, 468, 469,
+ 471, 472, -1
+ ],
+ name = 'tch_afs_7_4',
+ description = """TCH/AFS 7.4 kbits convolutional code:
+ G1/G3 = 1 + D + D3 + D4 / 1 + D + D2 + D3 + D4
+ G2/G3 = 1 + D2 + D4 / 1 + D + D2 + D3 + D4
+ G3/G3 = 1
+"""
+)
+
+TCH_AFS_6_7 = ConvolutionalCode(
+ 140,
+ [
+ ( G1, G3 ),
+ ( G2, G3 ),
+ ( 1, 1 ),
+ ( 1, 1 ),
+ ],
+ puncture = [
+ 1, 3, 7, 11, 15, 27, 39, 55, 67, 79, 95, 107,
+ 119, 135, 147, 159, 175, 187, 199, 215, 227, 239, 255, 267,
+ 279, 287, 291, 295, 299, 303, 307, 311, 315, 319, 323, 327,
+ 331, 335, 339, 343, 347, 351, 355, 359, 363, 367, 369, 371,
+ 375, 377, 379, 383, 385, 387, 391, 393, 395, 399, 401, 403,
+ 407, 409, 411, 415, 417, 419, 423, 425, 427, 431, 433, 435,
+ 439, 441, 443, 447, 449, 451, 455, 457, 459, 463, 465, 467,
+ 471, 473, 475, 479, 481, 483, 487, 489, 491, 495, 497, 499,
+ 503, 505, 507, 511, 513, 515, 519, 521, 523, 527, 529, 531,
+ 535, 537, 539, 543, 545, 547, 549, 551, 553, 555, 557, 559,
+ 561, 563, 565, 567, 569, 571, 573, 575, -1
+ ],
+ name = 'tch_afs_6_7',
+ description = """TCH/AFS 6.7 kbits convolutional code:
+ G1/G3 = 1 + D + D3 + D4 / 1 + D + D2 + D3 + D4
+ G2/G3 = 1 + D2 + D4 / 1 + D + D2 + D3 + D4
+ G3/G3 = 1
+ G3/G3 = 1
+"""
+)
+
+TCH_AFS_5_9 = ConvolutionalCode(
+ 124,
+ [
+ ( G4, G6 ),
+ ( G5, G6 ),
+ ( 1, 1),
+ ( 1, 1),
+ ],
+ puncture = [
+ 0, 1, 3, 5, 7, 11, 15, 31, 47, 63, 79, 95,
+ 111, 127, 143, 159, 175, 191, 207, 223, 239, 255, 271, 287,
+ 303, 319, 327, 331, 335, 343, 347, 351, 359, 363, 367, 375,
+ 379, 383, 391, 395, 399, 407, 411, 415, 423, 427, 431, 439,
+ 443, 447, 455, 459, 463, 467, 471, 475, 479, 483, 487, 491,
+ 495, 499, 503, 507, 509, 511, 512, 513, 515, 516, 517, 519,
+ -1
+ ],
+ name = 'tch_afs_5_9',
+ description = """TCH/AFS 5.9 kbits convolutional code:
+ 124 bits
+ G4/G6 = 1 + D2 + D3 + D5 + D6 / 1 + D + D2 + D3 + D4 + D6
+ G5/G6 = 1 + D + D4 + D6 / 1 + D + D2 + D3 + D4 + D6
+ G6/G6 = 1
+ G6/G6 = 1
+"""
+)
+
+TCH_AFS_5_15 = ConvolutionalCode(
+ 109,
+ [
+ ( G1, G3 ),
+ ( G1, G3 ),
+ ( G2, G3 ),
+ ( 1, 1 ),
+ ( 1, 1 ),
+ ],
+ puncture = [
+ 0, 4, 5, 9, 10, 14, 15, 20, 25, 30, 35, 40,
+ 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160,
+ 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280,
+ 290, 300, 310, 315, 320, 325, 330, 334, 335, 340, 344, 345,
+ 350, 354, 355, 360, 364, 365, 370, 374, 375, 380, 384, 385,
+ 390, 394, 395, 400, 404, 405, 410, 414, 415, 420, 424, 425,
+ 430, 434, 435, 440, 444, 445, 450, 454, 455, 460, 464, 465,
+ 470, 474, 475, 480, 484, 485, 490, 494, 495, 500, 504, 505,
+ 510, 514, 515, 520, 524, 525, 529, 530, 534, 535, 539, 540,
+ 544, 545, 549, 550, 554, 555, 559, 560, 564, -1
+ ],
+ name = 'tch_afs_5_15',
+ description = """TCH/AFS 5.15 kbits convolutional code:
+ G1/G3 = 1 + D + D3 + D4 / 1 + D + D2 + D3 + D4
+ G1/G3 = 1 + D + D3 + D4 / 1 + D + D2 + D3 + D4
+ G2/G3 = 1 + D2 + D4 / 1 + D + D2 + D3 + D4
+ G3/G3 = 1
+ G3/G3 = 1
+"""
+)
+
+TCH_AFS_4_75 = ConvolutionalCode(
+ 101,
+ [
+ ( G4, G6 ),
+ ( G4, G6 ),
+ ( G5, G6 ),
+ ( 1, 1 ),
+ ( 1, 1 ),
+ ],
+ puncture = [
+ 0, 1, 2, 4, 5, 7, 9, 15, 25, 35, 45, 55,
+ 65, 75, 85, 95, 105, 115, 125, 135, 145, 155, 165, 175,
+ 185, 195, 205, 215, 225, 235, 245, 255, 265, 275, 285, 295,
+ 305, 315, 325, 335, 345, 355, 365, 375, 385, 395, 400, 405,
+ 410, 415, 420, 425, 430, 435, 440, 445, 450, 455, 459, 460,
+ 465, 470, 475, 479, 480, 485, 490, 495, 499, 500, 505, 509,
+ 510, 515, 517, 519, 520, 522, 524, 525, 526, 527, 529, 530,
+ 531, 532, 534, -1
+ ],
+ name = 'tch_afs_4_75',
+ description = """TCH/AFS 4.75 kbits convolutional code:
+ G4/G6 = 1 + D2 + D3 + D5 + D6 / 1 + D + D2 + D3 + D4 + D6
+ G4/G6 = 1 + D2 + D3 + D5 + D6 / 1 + D + D2 + D3 + D4 + D6
+ G5/G6 = 1 + D + D4 + D6 / 1 + D + D2 + D3 + D4 + D6
+ G6/G6 = 1
+ G6/G6 = 1
+"""
+)
+
+def gen_c(dest, pref, code):
+ f = open(os.path.join(dest, 'conv_' + code.name + '_gen.c'), 'w')
+ print >>f, mod_license
+ print >>f, "#include <stdint.h>"
+ print >>f, "#include <osmocom/core/conv.h>"
+ code.gen_tables(pref, f)
+
+if __name__ == '__main__':
+ print >>sys.stderr, "Generating convolutional codes..."
+ prefix = "gsm0503"
+ path = sys.argv[1] if len(sys.argv) > 1 else os.getcwd()
+ gen_c(path, prefix, xCCH)
+ gen_c(path, prefix, CS2)
+ gen_c(path, prefix, CS3)
+ gen_c(path, prefix, TCH_AFS_12_2)
+ gen_c(path, prefix, TCH_AFS_10_2)
+ gen_c(path, prefix, TCH_AFS_7_95)
+ gen_c(path, prefix, TCH_AFS_7_4)
+ gen_c(path, prefix, TCH_AFS_6_7)
+ gen_c(path, prefix, TCH_AFS_5_9)
+ gen_c(path, prefix, TCH_AFS_5_15)
+ gen_c(path, prefix, TCH_AFS_4_75)
+ print >>sys.stderr, "\tdone."