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parameters.py
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parameters.py
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#!/usr/bin/env python
#
# Copyright 2008 Free Software Foundation, Inc.
#
# This file is part of GNU Radio
#
# GNU Radio 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, or (at your option)
# any later version.
#
# GNU Radio 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 GNU Radio; see the file COPYING. If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street,
# Boston, MA 02110-1301, USA.
#
# Andreas Mueller, 2008
class dab_parameters:
"""
@brief Represents the DAB parameters.
DAB parameters for mode I to IV
as specified in
ETSI EN 300 401 V1.4.1 (2006-06)
"Digital Audio Broadcasting (DAB) to mobile, portable and fixed receivers"
"""
# parameter values for all modes
# OFDM parameters (section 14)
# Table 38, page 145 of the DAB specification
__symbols_per_frame__ = [76, 76, 153, 76] # number of OFDM symbols per DAB frame (incl. pilot, excl. NS)
__num_carriers__ = [1536, 384, 192, 768] # number of carriers -> carrier width = 1536kHz/carriers
__frame_length__ = [196608, 49152, 49152, 98304] # samples per frame; in ms: 96,24,24,48 (incl. NS)
__ns_length__ = [2656, 664, 345, 1328] # length of null symbol in samples
__symbol_length__ = [2552, 638, 319, 1276] # length of an OFDM symbol in samples
__fft_length__ = [2048, 512, 256, 1024] # fft length
__cp_length__ = [504, 126, 63, 252] # length of cyclic prefix
default_sample_rate = 2048000
T = 1./default_sample_rate
# prn calculation data
# tables 39-43 on pages 148 and 149
#format: [mode][index][k_min, k_max, k', i, n]
__prn_kin__ = [[
[-768, -737, -768, 0, 1],
[-736, -705, -736, 1, 2],
[-704, -673, -704, 2, 0],
[-672, -641, -672, 3, 1],
[-640, -609, -640, 0, 3],
[-608, -577, -608, 1, 2],
[-576, -545, -576, 2, 2],
[-544, -513, -544, 3, 3],
[-512, -481, -512, 0, 2],
[-480, -449, -480, 1, 1],
[-448, -417, -448, 2, 2],
[-416, -385, -416, 3, 3],
[-384, -353, -384, 0, 1],
[-352, -321, -352, 1, 2],
[-320, -289, -320, 2, 3],
[-288, -257, -288, 3, 3],
[-256, -225, -256, 0, 2],
[-224, -193, -224, 1, 2],
[-192, -161, -192, 2, 2],
[-160, -129, -160, 3, 1],
[-128, -97, -128, 0, 1],
[-96, -65, -96, 1, 3],
[-64, -33, -64, 2, 1],
[-32, -1, -32, 3, 2],
[1, 32, 1, 0, 3],
[33, 64, 33, 3, 1],
[65, 96, 65, 2, 1],
[97, 128, 97, 1, 1],
[129, 160, 129, 0, 2],
[161, 192, 161, 3, 2],
[193, 224, 193, 2, 1],
[225, 256, 225, 1, 0],
[257, 288, 257, 0, 2],
[289, 320, 289, 3, 2],
[321, 352, 321, 2, 3],
[353, 384, 353, 1, 3],
[385, 416, 385, 0, 0],
[417, 448, 417, 3, 2],
[449, 480, 449, 2, 1],
[481, 512, 481, 1, 3],
[513, 544, 513, 0, 3],
[545, 576, 545, 3, 3],
[577, 608, 577, 2, 3],
[609, 640, 609, 1, 0],
[641, 672, 641, 0, 3],
[673, 704, 673, 3, 0],
[705, 736, 705, 2, 1],
[737, 768, 737, 1, 1]
],[
[-192, -161, -192, 0, 2],
[-160, -129, -160, 1, 3],
[-128, -97, -128, 2, 2],
[-96, -65, -96, 3, 2],
[-64, -33, -64, 0, 1],
[-32, -1, -32, 1, 2],
[1, 32, 1, 2, 0],
[33, 64, 33, 1, 2],
[65, 96, 65, 0, 2],
[97, 128, 97, 3, 1],
[129, 160, 129, 2, 0],
[161, 192, 161, 1, 3]
],[
[-96, -65, -96, 0, 2],
[-64, -33, -64, 1, 3],
[-32, -1, -32, 2, 0],
[1, 32, 1, 3, 2],
[33, 64, 33, 2, 2],
[65, 96, 65, 1, 2]
],[
[-384, -353, -384, 0, 0],
[-352, -321, -352, 1, 1],
[-320, -289, -320, 2, 1],
[-288, -257, -288, 3, 2],
[-256, -225, -256, 0, 2],
[-224, -193, -224, 1, 2],
[-192, -161, -192, 2, 0],
[-160, -129, -160, 3, 3],
[-128, -97, -128, 0, 3],
[-96, -65, -96, 1, 1],
[-64, -33, -64, 2, 3],
[-32, -1, -32, 3, 2],
[1, 32, 1, 0, 0],
[33, 64, 33, 3, 1],
[65, 96, 65, 2, 0],
[97, 128, 97, 1, 2],
[129, 160, 129, 0, 0],
[161, 192, 161, 3, 1],
[193, 224, 193, 2, 2],
[225, 256, 225, 1, 2],
[257, 288, 257, 0, 2],
[289, 320, 289, 3, 1],
[321, 352, 321, 2, 3],
[353, 384, 353, 1, 0]
]]
# h_i,j
# note: values for h_i,j are the same as for h_i,j+16 ...
__prn_h__ = [
[0, 2, 0, 0, 0, 0, 1, 1, 2, 0, 0, 0, 2, 2, 1, 1, 0, 2, 0, 0, 0, 0, 1, 1, 2, 0, 0, 0, 2, 2, 1, 1],
[0, 3, 2, 3, 0, 1, 3, 0, 2, 1, 2, 3, 2, 3, 3, 0, 0, 3, 2, 3, 0, 1, 3, 0, 2, 1, 2, 3, 2, 3, 3, 0],
[0, 0, 0, 2, 0, 2, 1, 3, 2, 2, 0, 2, 2, 0, 1, 3, 0, 0, 0, 2, 0, 2, 1, 3, 2, 2, 0, 2, 2, 0, 1, 3],
[0, 1, 2, 1, 0, 3, 3, 2, 2, 3, 2, 1, 2, 1, 3, 2, 0, 1, 2, 1, 0, 3, 3, 2, 2, 3, 2, 1, 2, 1, 3, 2]
]
__expected_frequency_interleaving__ = [ # these few values are listed in the specs - they are used to verify the sequence
[-513,-14,329,692,-733,13,680,273,-36,43],
[-129,-14,-55,-76,163,141,-88,7,-111,-85],
[-65,-14,52,-29,-58,77,40,71,-38,81],
[-257,-14,73,180,198,-243,168,218,17,299]
]
# transport mechanism parameters
__num_fic_syms__ = [3,3,8,3] # number of OFDM symbols per frame belonging to the FIC
# puncturing
puncturing_vectors = [ # table 29, page 131
[], # "Who are you? How did you get in my house?"
[1,1,0,0, 1,0,0,0, 1,0,0,0, 1,0,0,0, 1,0,0,0, 1,0,0,0, 1,0,0,0, 1,0,0,0], # PI=1: code rate: 8/9
[1,1,0,0, 1,0,0,0, 1,0,0,0, 1,0,0,0, 1,1,0,0, 1,0,0,0, 1,0,0,0, 1,0,0,0], # PI=2: code rate: 8/10
[1,1,0,0, 1,0,0,0, 1,1,0,0, 1,0,0,0, 1,1,0,0, 1,0,0,0, 1,0,0,0, 1,0,0,0], # PI=3: code rate: 8/11
[1,1,0,0, 1,0,0,0, 1,1,0,0, 1,0,0,0, 1,1,0,0, 1,0,0,0, 1,1,0,0, 1,0,0,0], # PI=4: code rate: 8/12
[1,1,0,0, 1,1,0,0, 1,1,0,0, 1,0,0,0, 1,1,0,0, 1,0,0,0, 1,1,0,0, 1,0,0,0], # PI=5: code rate: 8/13
[1,1,0,0, 1,1,0,0, 1,1,0,0, 1,0,0,0, 1,1,0,0, 1,1,0,0, 1,1,0,0, 1,0,0,0], # PI=6: code rate: 8/14
[1,1,0,0, 1,1,0,0, 1,1,0,0, 1,1,0,0, 1,1,0,0, 1,1,0,0, 1,1,0,0, 1,0,0,0], # PI=7: code rate: 8/15
[1,1,0,0, 1,1,0,0, 1,1,0,0, 1,1,0,0, 1,1,0,0, 1,1,0,0, 1,1,0,0, 1,1,0,0], # PI=8: code rate: 8/16
[1,1,1,0, 1,1,0,0, 1,1,0,0, 1,1,0,0, 1,1,0,0, 1,1,0,0, 1,1,0,0, 1,1,0,0], # PI=9: code rate: 8/17
[1,1,1,0, 1,1,0,0, 1,1,0,0, 1,1,0,0, 1,1,1,0, 1,1,0,0, 1,1,0,0, 1,1,0,0], # PI=10 code rate: 8/18
[1,1,1,0, 1,1,0,0, 1,1,1,0, 1,1,0,0, 1,1,1,0, 1,1,0,0, 1,1,0,0, 1,1,0,0], # PI=11 code rate: 8/19
[1,1,1,0, 1,1,0,0, 1,1,1,0, 1,1,0,0, 1,1,1,0, 1,1,0,0, 1,1,1,0, 1,1,0,0], # PI=12 code rate: 8/20
[1,1,1,0, 1,1,1,0, 1,1,1,0, 1,1,0,0, 1,1,1,0, 1,1,0,0, 1,1,1,0, 1,1,0,0], # PI=13 code rate: 8/21
[1,1,1,0, 1,1,1,0, 1,1,1,0, 1,1,0,0, 1,1,1,0, 1,1,1,0, 1,1,1,0, 1,1,0,0], # PI=14 code rate: 8/22
[1,1,1,0, 1,1,1,0, 1,1,1,0, 1,1,1,0, 1,1,1,0, 1,1,1,0, 1,1,1,0, 1,1,0,0], # PI=15 code rate: 8/23
[1,1,1,0, 1,1,1,0, 1,1,1,0, 1,1,1,0, 1,1,1,0, 1,1,1,0, 1,1,1,0, 1,1,1,0], # PI=16 code rate: 8/24
[1,1,1,1, 1,1,1,0, 1,1,1,0, 1,1,1,0, 1,1,1,0, 1,1,1,0, 1,1,1,0, 1,1,1,0], # PI=17 code rate: 8/25
[1,1,1,1, 1,1,1,0, 1,1,1,0, 1,1,1,0, 1,1,1,1, 1,1,1,0, 1,1,1,0, 1,1,1,0], # PI=18 code rate: 8/26
[1,1,1,1, 1,1,1,0, 1,1,1,1, 1,1,1,0, 1,1,1,1, 1,1,1,0, 1,1,1,0, 1,1,1,0], # PI=19 code rate: 8/27
[1,1,1,1, 1,1,1,0, 1,1,1,1, 1,1,1,0, 1,1,1,1, 1,1,1,0, 1,1,1,1, 1,1,1,0], # PI=20 code rate: 8/28
[1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,0, 1,1,1,1, 1,1,1,0, 1,1,1,1, 1,1,1,0], # PI=21 code rate: 8/29
[1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,0, 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,0], # PI=22 code rate: 8/30
[1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,0], # PI=23 code rate: 8/31
[1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1], # PI=24 code rate: 8/32
]
puncturing_tail_vector = [1,1,0,0, 1,1,0,0, 1,1,0,0, 1,1,0,0, 1,1,0,0, 1,1,0,0] # V_T
# convolutional coding - 11.1, page 129/130
conv_code_generator_polynomials = [
0133,
0171,
0145,
0133
]
conv_code_initial_state = 0
conv_code_final_state = 0
conv_code_constraint_length = 7
conv_code_in_bits = 1
conv_code_add_bits_input = 6
conv_code_out_bits = 4
__fic_conv_codeword_length__ = [3096, 3096, 4120, 3096] # 4*I + 24
__fic_punctured_codeword_length__ = [2304, 2304, 3072, 2304]
# energy dispersal
__energy_dispersal_fic_fibs_per_vector__ = [3, 3, 4, 3]
__energy_dispersal_fic_vector_length__ = [768, 768, 1024, 768] # I
__prbs_bits__ = [0,0,0,0,0,1,1,1,1,0,1,1,1,1,1,0] # first 16 PRBS bits are given in the standard - can be used for another assert
# transport mechanism parameters
fib_bits = 256
cif_bits = 55296
__num_fibs__ = [12,3,4,6] # FIC
__num_cifs__ = [4,1,1,2] # MSC -> num cifs = num fib groups
def __init__(self, mode, sample_rate=2048000, verbose=True):
"""
selects the correct parameters for the selected mode and calculates the prn sequence, etc.
@param mode DAB mode (I-IV)
@param sample_rate sampling frequency
"""
if verbose:
print "--> creating DAB parameter object" # should not be seen more than once
assert(mode>=1 and mode <=4)
self.mode = mode
self.sample_rate = sample_rate
self.verbose = verbose
# sanity checks:
for i in range(0,4):
# OFDM parameters
assert(self.__symbols_per_frame__[i]*self.__symbol_length__[i]+self.__ns_length__[i] == self.__frame_length__[i])
assert(self.__symbol_length__[i] == self.__fft_length__[i]+self.__cp_length__[i])
# block partitioning
assert(self.__num_carriers__[i] * 2 * self.__num_fic_syms__[i] / self.__num_cifs__[i] == self.__fic_punctured_codeword_length__[i])
# energy dispersal parameters
assert(self.__energy_dispersal_fic_fibs_per_vector__[i]*self.fib_bits == self.__energy_dispersal_fic_vector_length__[i])
assert(3*self.__energy_dispersal_fic_vector_length__[i] == self.__fic_punctured_codeword_length__[i]) # not sure - according to specification, code rate is only approximately 1/3, but seems to be exact
# sanity checks for PRBS sequence (energy dispersal)
assert(self.prbs(16) == self.__prbs_bits__) # bits from DAB standard
assert(self.prbs(511) == self.prbs(1022)[511:]) # sequence must repeat itself
if verbose:
print "--> DAB parameters self check ok"
self.__update_parameters__()
def set_mode(self, mode):
if self.verbose:
print "--> setting DAB mode to "+str(mode)
self.mode = mode
self.__update_parameters__()
def set_sample_rate(self, sample_rate):
if self.verbose:
print "--> setting sample rate to "+str(sample_rate)
self.sample_rate = sample_rate
self.__update_parameters__()
def __update_parameters__(self):
if self.verbose:
print "--> updating DAB parameters"
mode = self.mode
# OFDM parameters (14)
self.symbols_per_frame = self.__symbols_per_frame__[mode-1]
self.num_carriers = self.__num_carriers__[mode-1]
self.frame_length = self.__frame_length__[mode-1]
self.ns_length = self.__ns_length__[mode-1]
self.symbol_length = self.__symbol_length__[mode-1]
self.fft_length = self.__fft_length__[mode-1]
self.cp_length = self.__cp_length__[mode-1]
# bytes per frame and bytes per symbol
self.bytes_per_frame = (self.symbols_per_frame-1)*self.num_carriers/4
self.bytes_per_symbol = self.num_carriers/4
# prn sequence
self.prn = []
for k in range(-self.num_carriers//2, self.num_carriers//2+1):
if k == 0:
#self.prn.append(0)
pass
else:
[kk,i,n] = self.__get_prn_kk_i_n__(k)
h = self.__prn_h__[i][k-kk]
phi_k = (h+n)%4 # actually phi_k/(pi/2)
if phi_k == 0: # e^(j*pi/2*phi_k) is not exact if calculated by python
self.prn.append(1)
elif phi_k == 1:
self.prn.append(1j)
elif phi_k == 2:
self.prn.append(-1)
elif phi_k == 3:
self.prn.append(-1j)
# frequency (de)interleaving
a = self.fft_length/4-1
b = self.fft_length
A = [0]
for i in range(1,self.fft_length):
A.append((13*A[-1]+a)%b)
D = [d for d in A if d >= self.fft_length/8 and d <= 7*self.fft_length/8 and d != self.fft_length/2]
assert(len(D)==self.num_carriers)
self.frequency_interleaving_sequence = [d - self.fft_length/2 for d in D]
assert(self.frequency_interleaving_sequence[0:len(self.__expected_frequency_interleaving__[mode-1])]==self.__expected_frequency_interleaving__[mode-1])
# sequence for arrays, with indices starting from 0 and central carrier already removed
self.frequency_interleaving_sequence_array = [k+self.num_carriers/2-(k>0) for k in self.frequency_interleaving_sequence]
assert(len(self.frequency_interleaving_sequence_array)==self.num_carriers)
assert(min(self.frequency_interleaving_sequence_array)==0)
assert(max(self.frequency_interleaving_sequence_array)==self.num_carriers-1)
assert(len(set(self.frequency_interleaving_sequence_array))==len(self.frequency_interleaving_sequence_array)) # uniqueness of elements
# frequency deinterleaving sequence
self.frequency_deinterleaving_sequence_array = [self.frequency_interleaving_sequence_array.index(i) for i in range(0,self.num_carriers)]
# adapt for non-standard sample rate - do this at end, frequency interleaving calculation still needs default fft length
if self.sample_rate != self.default_sample_rate:
if self.verbose:
print "--> using non-standard sample rate: "+str(self.sample_rate)
self.T = 1./self.sample_rate
self.ns_length = int(round(self.ns_length*float(self.sample_rate)/float(self.default_sample_rate)))
self.cp_length = int(round(self.cp_length*float(self.sample_rate)/float(self.default_sample_rate)))
self.fft_length = int(round(self.fft_length*float(self.sample_rate)/float(self.default_sample_rate)))
self.symbol_length = self.cp_length + self.fft_length
self.frame_length = self.symbols_per_frame * self.symbol_length + self.ns_length
# block partitioning parameters (14.4)
self.num_fic_syms = self.__num_fic_syms__[mode-1]
# convolutional coding (11)
self.fic_conv_codeword_length = self.__fic_conv_codeword_length__[mode-1] # length after puncturing
# unpuncturing sequence (assembled, such that it can be applied on a complete fib group)
# see 11.2 page 132
self.fic_punctured_codeword_length = self.__fic_punctured_codeword_length__[mode-1]
if mode in [1,2,4]:
self.assembled_fic_puncturing_sequence = 21*4*self.puncturing_vectors[16] + 3*4*self.puncturing_vectors[15] + self.puncturing_tail_vector
else:
self.assembled_fic_puncturing_sequence = 29*4*self.puncturing_vectors[16] + 3*4*self.puncturing_vectors[15] + self.puncturing_tail_vector
assert(len(self.assembled_fic_puncturing_sequence)==self.fic_conv_codeword_length)
assert(len(filter(lambda x: x==1, self.assembled_fic_puncturing_sequence))==self.fic_punctured_codeword_length)
# energy dispersal (10)
self.energy_dispersal_fic_fibs_per_vector =self. __energy_dispersal_fic_fibs_per_vector__[mode-1]
self.energy_dispersal_fic_vector_length = self.__energy_dispersal_fic_vector_length__[mode-1]
# transport mechanism parameters (5)
self.num_fibs = self.__num_fibs__[mode-1]
self.num_cifs = self.__num_cifs__[mode-1]
def __get_prn_kk_i_n__(self,k):
assert(k!=0)
assert(abs(k)<=self.num_carriers//2)
if k<0:
index = (k + self.num_carriers//2) // 32
kk = 32*(int(k)//32)
else:
index = (k + self.num_carriers//2 - 1) // 32
kk = 32*(int(k-1)//32)+1
values = self.__prn_kin__[self.mode-1][index]
assert(k>=values[0] and k<=values[1])
assert(kk==values[2])
i = values[3]
n = values[4]
return [kk, i, n]
def prbs(self, length):
"""
PRBS generated with the polynomial p(x) = x^9 + x^5 + 1
and initial state 111111111
@param length number of bits in the sequence
"""
bits = [1]*9
sequence = []
for i in range(0,length):
newbit = bits[8] ^ bits[4]
bits = [newbit]+bits[0:-1]
sequence.append(newbit)
return sequence
class receiver_parameters:
"""
@brief Parameters for the receiver, independent of the DAB standard
"""
# filter at input
filt_bw = (768+100)*1e3
filt_tb = 50e3
# OFDM stuff
__cp_gap__ = [252, 63, 31, 124] # gap for ofdm_sampler to leave before the start of the next symbol
__symbols_for_ffs_estimation__ = [8,8,16,8] # number of symbols to evaluate for fine frequency error estimation
__symbols_for_magnitude_equalization__ = [6,6,12,6] # how many symbols should be used to estimate magnitude equalizer?
ffs_alpha = 0.5
# phase variance estimation
phase_var_estimate_alpha = 0.01
phase_var_estimate_downsample = 100 # 50 -> uses about 1% of the CPU time
# for USRP
usrp_ffc_retune_frequency = 5 # how often should the USRP be retuned at most?
usrp_ffc_min_deviation = 5 # how far off does the FFE have to be to retune the USRP?
usrp_ffc_adapt_factor = 0.5 # how much to adapt the correction?
def __init__(self, mode, sample_rate=2048000, softbits=False, input_fft_filter=True, autocorrect_sample_rate=False, sample_rate_correction_factor=1, correct_ffe=True, equalize_magnitude=True, verbose=True):
"""
Create new instance.
@param mode DAB mode (I-IV)
@param sample_rate sampling frequency
@param input_fft_filter whether to use an FFT filter at the input
@param autocorrect_sample_rate whether to correct the sample rate dynamically
@param sample_rate_correction_factor static correction factor for sample rate
@parem correct_ffe if False, only estimate fine frequency error - don't correct it
@param verbose be talkative
"""
if verbose:
print "--> creating RX parameter object"
assert(mode>=1 and mode <=4)
self.set_mode(mode)
self.sample_rate = sample_rate
self.softbits = softbits
self.input_fft_filter = input_fft_filter
self.autocorrect_sample_rate = autocorrect_sample_rate
self.sample_rate_correction_factor = sample_rate_correction_factor
self.correct_ffe = correct_ffe
self.equalize_magnitude = equalize_magnitude
self.verbose = verbose
def set_mode(self, mode):
self.mode = mode
self.cp_gap = self.__cp_gap__[mode-1]
self.symbols_for_ffs_estimation = self.__symbols_for_ffs_estimation__[mode-1]
self.symbols_for_magnitude_equalization = self.__symbols_for_magnitude_equalization__[mode-1]