utils/conv_gen.py

#!/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."