added separate header for viterbi detector
This commit is contained in:
Piotr Krysik
parent
58b281711e
commit
4ebdbec814
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/***************************************************************************
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* Copyright (C) 2008 by Piotr Krysik *
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* pkrysik@stud.elka.pw.edu.pl *
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* *
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* This program is free software; you can redistribute it and/or modify *
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* it under the terms of the GNU General Public License as published by *
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* the Free Software Foundation; either version 2 of the License, or *
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* (at your option) any later version. *
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* *
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* This program is distributed in the hope that it will be useful, *
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* but WITHOUT ANY WARRANTY; without even the implied warranty of *
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the *
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* GNU General Public License for more details. *
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* *
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* You should have received a copy of the GNU General Public License *
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* along with this program; if not, write to the *
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* Free Software Foundation, Inc., *
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* 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. *
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***************************************************************************/
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/*
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** viterbi_detector:
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** This part does the detection of received sequnece.
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** Employed algorithm is viterbi Maximum Likehood Sequence Estimation.
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** At this moment it gives hard decisions on the output, but
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** it was designed with soft decisions in mind.
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**
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** SYNTAX: void viterbi_detector(
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** const gr_complex * input,
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** unsigned int samples_num,
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** gr_complex * rhh,
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** unsigned int start_state,
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** const unsigned int * stop_states,
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** unsigned int stops_num,
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** float * output)
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**
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** INPUT: input: Complex received signal afted matched filtering.
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** samples_num: Number of samples in the input table.
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** rhh: The autocorrelation of the estimated channel
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** impulse response.
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** start_state: Number of the start point. In GSM each burst
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** starts with sequence of three bits (0,0,0) which
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** indicates start point of the algorithm.
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** stop_states: Table with numbers of possible stop states.
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** stops_num: Number of possible stop states
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**
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**
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** OUTPUT: output: Differentially decoded hard output of the algorithm:
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** -1 for logical "0" and 1 for logical "1"
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**
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** SUB_FUNC: none
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**
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** TEST(S): Tested with real world normal burst.
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*/
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#include <gnuradio/gr_complex.h>
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#include <gsm_constants.h>
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#define PATHS_NUM (1 << (CHAN_IMP_RESP_LENGTH-1))
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void viterbi_detector(const gr_complex * input, unsigned int samples_num, gr_complex * rhh, unsigned int start_state, const unsigned int * stop_states, unsigned int stops_num, float * output)
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{
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float increment[8];
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float path_metrics1[16];
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float path_metrics2[16];
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float * new_path_metrics;
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float * old_path_metrics;
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float * tmp;
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float trans_table[BURST_SIZE][16];
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float pm_candidate1, pm_candidate2;
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bool real_imag;
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float input_symbol_real, input_symbol_imag;
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unsigned int i, sample_nr;
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/*
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* Setup first path metrics, so only state pointed by start_state is possible.
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* Start_state metric is equal to zero, the rest is written with some very low value,
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* which makes them practically impossible to occur.
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*/
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for(i=0; i<PATHS_NUM; i++){
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path_metrics1[i]=(-10e30);
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}
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path_metrics1[start_state]=0;
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/*
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* Compute Increment - a table of values which does not change for subsequent input samples.
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* Increment is table of reference levels for computation of branch metrics:
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* branch metric = (+/-)received_sample (+/-) reference_level
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*/
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increment[0] = -rhh[1].imag() -rhh[2].real() -rhh[3].imag() +rhh[4].real();
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increment[1] = rhh[1].imag() -rhh[2].real() -rhh[3].imag() +rhh[4].real();
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increment[2] = -rhh[1].imag() +rhh[2].real() -rhh[3].imag() +rhh[4].real();
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increment[3] = rhh[1].imag() +rhh[2].real() -rhh[3].imag() +rhh[4].real();
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increment[4] = -rhh[1].imag() -rhh[2].real() +rhh[3].imag() +rhh[4].real();
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increment[5] = rhh[1].imag() -rhh[2].real() +rhh[3].imag() +rhh[4].real();
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increment[6] = -rhh[1].imag() +rhh[2].real() +rhh[3].imag() +rhh[4].real();
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increment[7] = rhh[1].imag() +rhh[2].real() +rhh[3].imag() +rhh[4].real();
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/*
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* Computation of path metrics and decisions (Add-Compare-Select).
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* It's composed of two parts: one for odd input samples (imaginary numbers)
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* and one for even samples (real numbers).
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* Each part is composed of independent (parallelisable) statements like
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* this one:
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* pm_candidate1 = old_path_metrics[0] - input_symbol_real - increment[7];
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* pm_candidate2 = old_path_metrics[8] - input_symbol_real + increment[0];
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* if(pm_candidate1 > pm_candidate2){
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* new_path_metrics[0] = pm_candidate1;
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* trans_table[sample_nr][0] = -1.0;
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* }
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* else{
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* new_path_metrics[0] = pm_candidate2;
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* trans_table[sample_nr][0] = 1.0;
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* }
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* This is very good point for optimisations (SIMD or OpenMP) as it's most time
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* consuming part of this function.
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*/
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sample_nr=0;
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old_path_metrics=path_metrics1;
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new_path_metrics=path_metrics2;
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while(sample_nr<samples_num){
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//Processing imag states
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real_imag=1;
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input_symbol_imag = input[sample_nr].imag();
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pm_candidate1 = old_path_metrics[0] + input_symbol_imag - increment[2];
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pm_candidate2 = old_path_metrics[8] + input_symbol_imag + increment[5];
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if(pm_candidate1 > pm_candidate2){
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new_path_metrics[0] = pm_candidate1;
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trans_table[sample_nr][0] = -1.0;
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}
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else{
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new_path_metrics[0] = pm_candidate2;
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trans_table[sample_nr][0] = 1.0;
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}
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pm_candidate1 = old_path_metrics[0] - input_symbol_imag + increment[2];
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pm_candidate2 = old_path_metrics[8] - input_symbol_imag - increment[5];
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if(pm_candidate1 > pm_candidate2){
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new_path_metrics[1] = pm_candidate1;
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trans_table[sample_nr][1] = -1.0;
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}
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else{
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new_path_metrics[1] = pm_candidate2;
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trans_table[sample_nr][1] = 1.0;
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}
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pm_candidate1 = old_path_metrics[1] + input_symbol_imag - increment[3];
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pm_candidate2 = old_path_metrics[9] + input_symbol_imag + increment[4];
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if(pm_candidate1 > pm_candidate2){
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new_path_metrics[2] = pm_candidate1;
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trans_table[sample_nr][2] = -1.0;
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}
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else{
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new_path_metrics[2] = pm_candidate2;
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trans_table[sample_nr][2] = 1.0;
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}
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pm_candidate1 = old_path_metrics[1] - input_symbol_imag + increment[3];
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pm_candidate2 = old_path_metrics[9] - input_symbol_imag - increment[4];
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if(pm_candidate1 > pm_candidate2){
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new_path_metrics[3] = pm_candidate1;
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trans_table[sample_nr][3] = -1.0;
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}
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else{
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new_path_metrics[3] = pm_candidate2;
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trans_table[sample_nr][3] = 1.0;
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}
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pm_candidate1 = old_path_metrics[2] + input_symbol_imag - increment[0];
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pm_candidate2 = old_path_metrics[10] + input_symbol_imag + increment[7];
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if(pm_candidate1 > pm_candidate2){
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new_path_metrics[4] = pm_candidate1;
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trans_table[sample_nr][4] = -1.0;
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}
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else{
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new_path_metrics[4] = pm_candidate2;
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trans_table[sample_nr][4] = 1.0;
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}
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pm_candidate1 = old_path_metrics[2] - input_symbol_imag + increment[0];
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pm_candidate2 = old_path_metrics[10] - input_symbol_imag - increment[7];
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if(pm_candidate1 > pm_candidate2){
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new_path_metrics[5] = pm_candidate1;
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trans_table[sample_nr][5] = -1.0;
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}
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else{
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new_path_metrics[5] = pm_candidate2;
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trans_table[sample_nr][5] = 1.0;
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}
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pm_candidate1 = old_path_metrics[3] + input_symbol_imag - increment[1];
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pm_candidate2 = old_path_metrics[11] + input_symbol_imag + increment[6];
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if(pm_candidate1 > pm_candidate2){
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new_path_metrics[6] = pm_candidate1;
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trans_table[sample_nr][6] = -1.0;
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}
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else{
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new_path_metrics[6] = pm_candidate2;
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trans_table[sample_nr][6] = 1.0;
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}
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pm_candidate1 = old_path_metrics[3] - input_symbol_imag + increment[1];
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pm_candidate2 = old_path_metrics[11] - input_symbol_imag - increment[6];
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if(pm_candidate1 > pm_candidate2){
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new_path_metrics[7] = pm_candidate1;
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trans_table[sample_nr][7] = -1.0;
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}
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else{
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new_path_metrics[7] = pm_candidate2;
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trans_table[sample_nr][7] = 1.0;
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}
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pm_candidate1 = old_path_metrics[4] + input_symbol_imag - increment[6];
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pm_candidate2 = old_path_metrics[12] + input_symbol_imag + increment[1];
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if(pm_candidate1 > pm_candidate2){
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new_path_metrics[8] = pm_candidate1;
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trans_table[sample_nr][8] = -1.0;
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}
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else{
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new_path_metrics[8] = pm_candidate2;
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trans_table[sample_nr][8] = 1.0;
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}
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pm_candidate1 = old_path_metrics[4] - input_symbol_imag + increment[6];
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pm_candidate2 = old_path_metrics[12] - input_symbol_imag - increment[1];
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if(pm_candidate1 > pm_candidate2){
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new_path_metrics[9] = pm_candidate1;
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trans_table[sample_nr][9] = -1.0;
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}
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else{
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new_path_metrics[9] = pm_candidate2;
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trans_table[sample_nr][9] = 1.0;
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}
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pm_candidate1 = old_path_metrics[5] + input_symbol_imag - increment[7];
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pm_candidate2 = old_path_metrics[13] + input_symbol_imag + increment[0];
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if(pm_candidate1 > pm_candidate2){
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new_path_metrics[10] = pm_candidate1;
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trans_table[sample_nr][10] = -1.0;
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}
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else{
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new_path_metrics[10] = pm_candidate2;
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trans_table[sample_nr][10] = 1.0;
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}
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pm_candidate1 = old_path_metrics[5] - input_symbol_imag + increment[7];
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pm_candidate2 = old_path_metrics[13] - input_symbol_imag - increment[0];
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if(pm_candidate1 > pm_candidate2){
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new_path_metrics[11] = pm_candidate1;
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trans_table[sample_nr][11] = -1.0;
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}
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else{
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new_path_metrics[11] = pm_candidate2;
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trans_table[sample_nr][11] = 1.0;
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}
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pm_candidate1 = old_path_metrics[6] + input_symbol_imag - increment[4];
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pm_candidate2 = old_path_metrics[14] + input_symbol_imag + increment[3];
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if(pm_candidate1 > pm_candidate2){
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new_path_metrics[12] = pm_candidate1;
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trans_table[sample_nr][12] = -1.0;
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}
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else{
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new_path_metrics[12] = pm_candidate2;
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trans_table[sample_nr][12] = 1.0;
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}
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pm_candidate1 = old_path_metrics[6] - input_symbol_imag + increment[4];
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pm_candidate2 = old_path_metrics[14] - input_symbol_imag - increment[3];
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if(pm_candidate1 > pm_candidate2){
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new_path_metrics[13] = pm_candidate1;
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trans_table[sample_nr][13] = -1.0;
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}
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else{
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new_path_metrics[13] = pm_candidate2;
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trans_table[sample_nr][13] = 1.0;
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}
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pm_candidate1 = old_path_metrics[7] + input_symbol_imag - increment[5];
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pm_candidate2 = old_path_metrics[15] + input_symbol_imag + increment[2];
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if(pm_candidate1 > pm_candidate2){
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new_path_metrics[14] = pm_candidate1;
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trans_table[sample_nr][14] = -1.0;
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}
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else{
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new_path_metrics[14] = pm_candidate2;
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trans_table[sample_nr][14] = 1.0;
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}
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pm_candidate1 = old_path_metrics[7] - input_symbol_imag + increment[5];
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pm_candidate2 = old_path_metrics[15] - input_symbol_imag - increment[2];
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if(pm_candidate1 > pm_candidate2){
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new_path_metrics[15] = pm_candidate1;
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trans_table[sample_nr][15] = -1.0;
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}
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else{
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new_path_metrics[15] = pm_candidate2;
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trans_table[sample_nr][15] = 1.0;
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}
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tmp=old_path_metrics;
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old_path_metrics=new_path_metrics;
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new_path_metrics=tmp;
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sample_nr++;
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if(sample_nr==samples_num)
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break;
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//Processing real states
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real_imag=0;
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input_symbol_real = input[sample_nr].real();
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pm_candidate1 = old_path_metrics[0] - input_symbol_real - increment[7];
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pm_candidate2 = old_path_metrics[8] - input_symbol_real + increment[0];
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if(pm_candidate1 > pm_candidate2){
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new_path_metrics[0] = pm_candidate1;
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trans_table[sample_nr][0] = -1.0;
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}
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else{
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new_path_metrics[0] = pm_candidate2;
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trans_table[sample_nr][0] = 1.0;
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}
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pm_candidate1 = old_path_metrics[0] + input_symbol_real + increment[7];
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pm_candidate2 = old_path_metrics[8] + input_symbol_real - increment[0];
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if(pm_candidate1 > pm_candidate2){
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new_path_metrics[1] = pm_candidate1;
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trans_table[sample_nr][1] = -1.0;
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}
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else{
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new_path_metrics[1] = pm_candidate2;
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trans_table[sample_nr][1] = 1.0;
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}
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pm_candidate1 = old_path_metrics[1] - input_symbol_real - increment[6];
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pm_candidate2 = old_path_metrics[9] - input_symbol_real + increment[1];
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if(pm_candidate1 > pm_candidate2){
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new_path_metrics[2] = pm_candidate1;
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trans_table[sample_nr][2] = -1.0;
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}
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else{
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new_path_metrics[2] = pm_candidate2;
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trans_table[sample_nr][2] = 1.0;
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}
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pm_candidate1 = old_path_metrics[1] + input_symbol_real + increment[6];
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pm_candidate2 = old_path_metrics[9] + input_symbol_real - increment[1];
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if(pm_candidate1 > pm_candidate2){
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new_path_metrics[3] = pm_candidate1;
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trans_table[sample_nr][3] = -1.0;
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}
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else{
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new_path_metrics[3] = pm_candidate2;
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trans_table[sample_nr][3] = 1.0;
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}
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pm_candidate1 = old_path_metrics[2] - input_symbol_real - increment[5];
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pm_candidate2 = old_path_metrics[10] - input_symbol_real + increment[2];
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if(pm_candidate1 > pm_candidate2){
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new_path_metrics[4] = pm_candidate1;
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trans_table[sample_nr][4] = -1.0;
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}
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else{
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new_path_metrics[4] = pm_candidate2;
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trans_table[sample_nr][4] = 1.0;
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}
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pm_candidate1 = old_path_metrics[2] + input_symbol_real + increment[5];
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pm_candidate2 = old_path_metrics[10] + input_symbol_real - increment[2];
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if(pm_candidate1 > pm_candidate2){
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new_path_metrics[5] = pm_candidate1;
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trans_table[sample_nr][5] = -1.0;
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}
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else{
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new_path_metrics[5] = pm_candidate2;
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trans_table[sample_nr][5] = 1.0;
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}
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pm_candidate1 = old_path_metrics[3] - input_symbol_real - increment[4];
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pm_candidate2 = old_path_metrics[11] - input_symbol_real + increment[3];
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if(pm_candidate1 > pm_candidate2){
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new_path_metrics[6] = pm_candidate1;
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trans_table[sample_nr][6] = -1.0;
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}
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else{
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new_path_metrics[6] = pm_candidate2;
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trans_table[sample_nr][6] = 1.0;
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}
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pm_candidate1 = old_path_metrics[3] + input_symbol_real + increment[4];
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pm_candidate2 = old_path_metrics[11] + input_symbol_real - increment[3];
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if(pm_candidate1 > pm_candidate2){
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new_path_metrics[7] = pm_candidate1;
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trans_table[sample_nr][7] = -1.0;
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}
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else{
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new_path_metrics[7] = pm_candidate2;
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trans_table[sample_nr][7] = 1.0;
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}
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|
||||
pm_candidate1 = old_path_metrics[4] - input_symbol_real - increment[3];
|
||||
pm_candidate2 = old_path_metrics[12] - input_symbol_real + increment[4];
|
||||
if(pm_candidate1 > pm_candidate2){
|
||||
new_path_metrics[8] = pm_candidate1;
|
||||
trans_table[sample_nr][8] = -1.0;
|
||||
}
|
||||
else{
|
||||
new_path_metrics[8] = pm_candidate2;
|
||||
trans_table[sample_nr][8] = 1.0;
|
||||
}
|
||||
|
||||
pm_candidate1 = old_path_metrics[4] + input_symbol_real + increment[3];
|
||||
pm_candidate2 = old_path_metrics[12] + input_symbol_real - increment[4];
|
||||
if(pm_candidate1 > pm_candidate2){
|
||||
new_path_metrics[9] = pm_candidate1;
|
||||
trans_table[sample_nr][9] = -1.0;
|
||||
}
|
||||
else{
|
||||
new_path_metrics[9] = pm_candidate2;
|
||||
trans_table[sample_nr][9] = 1.0;
|
||||
}
|
||||
|
||||
pm_candidate1 = old_path_metrics[5] - input_symbol_real - increment[2];
|
||||
pm_candidate2 = old_path_metrics[13] - input_symbol_real + increment[5];
|
||||
if(pm_candidate1 > pm_candidate2){
|
||||
new_path_metrics[10] = pm_candidate1;
|
||||
trans_table[sample_nr][10] = -1.0;
|
||||
}
|
||||
else{
|
||||
new_path_metrics[10] = pm_candidate2;
|
||||
trans_table[sample_nr][10] = 1.0;
|
||||
}
|
||||
|
||||
pm_candidate1 = old_path_metrics[5] + input_symbol_real + increment[2];
|
||||
pm_candidate2 = old_path_metrics[13] + input_symbol_real - increment[5];
|
||||
if(pm_candidate1 > pm_candidate2){
|
||||
new_path_metrics[11] = pm_candidate1;
|
||||
trans_table[sample_nr][11] = -1.0;
|
||||
}
|
||||
else{
|
||||
new_path_metrics[11] = pm_candidate2;
|
||||
trans_table[sample_nr][11] = 1.0;
|
||||
}
|
||||
|
||||
pm_candidate1 = old_path_metrics[6] - input_symbol_real - increment[1];
|
||||
pm_candidate2 = old_path_metrics[14] - input_symbol_real + increment[6];
|
||||
if(pm_candidate1 > pm_candidate2){
|
||||
new_path_metrics[12] = pm_candidate1;
|
||||
trans_table[sample_nr][12] = -1.0;
|
||||
}
|
||||
else{
|
||||
new_path_metrics[12] = pm_candidate2;
|
||||
trans_table[sample_nr][12] = 1.0;
|
||||
}
|
||||
|
||||
pm_candidate1 = old_path_metrics[6] + input_symbol_real + increment[1];
|
||||
pm_candidate2 = old_path_metrics[14] + input_symbol_real - increment[6];
|
||||
if(pm_candidate1 > pm_candidate2){
|
||||
new_path_metrics[13] = pm_candidate1;
|
||||
trans_table[sample_nr][13] = -1.0;
|
||||
}
|
||||
else{
|
||||
new_path_metrics[13] = pm_candidate2;
|
||||
trans_table[sample_nr][13] = 1.0;
|
||||
}
|
||||
|
||||
pm_candidate1 = old_path_metrics[7] - input_symbol_real - increment[0];
|
||||
pm_candidate2 = old_path_metrics[15] - input_symbol_real + increment[7];
|
||||
if(pm_candidate1 > pm_candidate2){
|
||||
new_path_metrics[14] = pm_candidate1;
|
||||
trans_table[sample_nr][14] = -1.0;
|
||||
}
|
||||
else{
|
||||
new_path_metrics[14] = pm_candidate2;
|
||||
trans_table[sample_nr][14] = 1.0;
|
||||
}
|
||||
|
||||
pm_candidate1 = old_path_metrics[7] + input_symbol_real + increment[0];
|
||||
pm_candidate2 = old_path_metrics[15] + input_symbol_real - increment[7];
|
||||
if(pm_candidate1 > pm_candidate2){
|
||||
new_path_metrics[15] = pm_candidate1;
|
||||
trans_table[sample_nr][15] = -1.0;
|
||||
}
|
||||
else{
|
||||
new_path_metrics[15] = pm_candidate2;
|
||||
trans_table[sample_nr][15] = 1.0;
|
||||
}
|
||||
tmp=old_path_metrics;
|
||||
old_path_metrics=new_path_metrics;
|
||||
new_path_metrics=tmp;
|
||||
|
||||
sample_nr++;
|
||||
}
|
||||
|
||||
/*
|
||||
* Find the best from the stop states by comparing their path metrics.
|
||||
* Not every stop state is always possible, so we are searching in
|
||||
* a subset of them.
|
||||
*/
|
||||
unsigned int best_stop_state;
|
||||
float stop_state_metric, max_stop_state_metric;
|
||||
best_stop_state = stop_states[0];
|
||||
max_stop_state_metric = old_path_metrics[best_stop_state];
|
||||
for(i=1; i< stops_num; i++){
|
||||
stop_state_metric = old_path_metrics[stop_states[i]];
|
||||
if(stop_state_metric > max_stop_state_metric){
|
||||
max_stop_state_metric = stop_state_metric;
|
||||
best_stop_state = stop_states[i];
|
||||
}
|
||||
}
|
||||
|
||||
/*
|
||||
* This table was generated with hope that it gives a litle speedup during
|
||||
* traceback stage.
|
||||
* Received bit is related to the number of state in the trellis.
|
||||
* I've numbered states so their parity (number of ones) is related
|
||||
* to a received bit.
|
||||
*/
|
||||
static const unsigned int parity_table[PATHS_NUM] = { 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, };
|
||||
|
||||
/*
|
||||
* Table of previous states in the trellis diagram.
|
||||
* For GMSK modulation every state has two previous states.
|
||||
* Example:
|
||||
* previous_state_nr1 = prev_table[current_state_nr][0]
|
||||
* previous_state_nr2 = prev_table[current_state_nr][1]
|
||||
*/
|
||||
static const unsigned int prev_table[PATHS_NUM][2] = { {0,8}, {0,8}, {1,9}, {1,9}, {2,10}, {2,10}, {3,11}, {3,11}, {4,12}, {4,12}, {5,13}, {5,13}, {6,14}, {6,14}, {7,15}, {7,15}, };
|
||||
|
||||
/*
|
||||
* Traceback and differential decoding of received sequence.
|
||||
* Decisions stored in trans_table are used to restore best path in the trellis.
|
||||
*/
|
||||
sample_nr=samples_num;
|
||||
unsigned int state_nr=best_stop_state;
|
||||
unsigned int decision;
|
||||
bool out_bit=0;
|
||||
|
||||
while(sample_nr>0){
|
||||
sample_nr--;
|
||||
decision = (trans_table[sample_nr][state_nr]>0);
|
||||
|
||||
if(decision != out_bit)
|
||||
output[sample_nr]=-trans_table[sample_nr][state_nr];
|
||||
else
|
||||
output[sample_nr]=trans_table[sample_nr][state_nr];
|
||||
|
||||
out_bit = out_bit ^ real_imag ^ parity_table[state_nr];
|
||||
state_nr = prev_table[state_nr][decision];
|
||||
real_imag = !real_imag;
|
||||
}
|
||||
}
|
||||
|
|
@ -53,500 +53,4 @@
|
|||
** TEST(S): Tested with real world normal burst.
|
||||
*/
|
||||
|
||||
#include <gnuradio/gr_complex.h>
|
||||
#include <gsm_constants.h>
|
||||
#define PATHS_NUM (1 << (CHAN_IMP_RESP_LENGTH-1))
|
||||
|
||||
void viterbi_detector(const gr_complex * input, unsigned int samples_num, gr_complex * rhh, unsigned int start_state, const unsigned int * stop_states, unsigned int stops_num, float * output)
|
||||
{
|
||||
float increment[8];
|
||||
float path_metrics1[16];
|
||||
float path_metrics2[16];
|
||||
float * new_path_metrics;
|
||||
float * old_path_metrics;
|
||||
float * tmp;
|
||||
float trans_table[BURST_SIZE][16];
|
||||
float pm_candidate1, pm_candidate2;
|
||||
bool real_imag;
|
||||
float input_symbol_real, input_symbol_imag;
|
||||
unsigned int i, sample_nr;
|
||||
|
||||
/*
|
||||
* Setup first path metrics, so only state pointed by start_state is possible.
|
||||
* Start_state metric is equal to zero, the rest is written with some very low value,
|
||||
* which makes them practically impossible to occur.
|
||||
*/
|
||||
for(i=0; i<PATHS_NUM; i++){
|
||||
path_metrics1[i]=(-10e30);
|
||||
}
|
||||
path_metrics1[start_state]=0;
|
||||
|
||||
/*
|
||||
* Compute Increment - a table of values which does not change for subsequent input samples.
|
||||
* Increment is table of reference levels for computation of branch metrics:
|
||||
* branch metric = (+/-)received_sample (+/-) reference_level
|
||||
*/
|
||||
increment[0] = -rhh[1].imag() -rhh[2].real() -rhh[3].imag() +rhh[4].real();
|
||||
increment[1] = rhh[1].imag() -rhh[2].real() -rhh[3].imag() +rhh[4].real();
|
||||
increment[2] = -rhh[1].imag() +rhh[2].real() -rhh[3].imag() +rhh[4].real();
|
||||
increment[3] = rhh[1].imag() +rhh[2].real() -rhh[3].imag() +rhh[4].real();
|
||||
increment[4] = -rhh[1].imag() -rhh[2].real() +rhh[3].imag() +rhh[4].real();
|
||||
increment[5] = rhh[1].imag() -rhh[2].real() +rhh[3].imag() +rhh[4].real();
|
||||
increment[6] = -rhh[1].imag() +rhh[2].real() +rhh[3].imag() +rhh[4].real();
|
||||
increment[7] = rhh[1].imag() +rhh[2].real() +rhh[3].imag() +rhh[4].real();
|
||||
|
||||
|
||||
/*
|
||||
* Computation of path metrics and decisions (Add-Compare-Select).
|
||||
* It's composed of two parts: one for odd input samples (imaginary numbers)
|
||||
* and one for even samples (real numbers).
|
||||
* Each part is composed of independent (parallelisable) statements like
|
||||
* this one:
|
||||
* pm_candidate1 = old_path_metrics[0] - input_symbol_real - increment[7];
|
||||
* pm_candidate2 = old_path_metrics[8] - input_symbol_real + increment[0];
|
||||
* if(pm_candidate1 > pm_candidate2){
|
||||
* new_path_metrics[0] = pm_candidate1;
|
||||
* trans_table[sample_nr][0] = -1.0;
|
||||
* }
|
||||
* else{
|
||||
* new_path_metrics[0] = pm_candidate2;
|
||||
* trans_table[sample_nr][0] = 1.0;
|
||||
* }
|
||||
* This is very good point for optimisations (SIMD or OpenMP) as it's most time
|
||||
* consuming part of this function.
|
||||
*/
|
||||
sample_nr=0;
|
||||
old_path_metrics=path_metrics1;
|
||||
new_path_metrics=path_metrics2;
|
||||
while(sample_nr<samples_num){
|
||||
//Processing imag states
|
||||
real_imag=1;
|
||||
input_symbol_imag = input[sample_nr].imag();
|
||||
|
||||
pm_candidate1 = old_path_metrics[0] + input_symbol_imag - increment[2];
|
||||
pm_candidate2 = old_path_metrics[8] + input_symbol_imag + increment[5];
|
||||
if(pm_candidate1 > pm_candidate2){
|
||||
new_path_metrics[0] = pm_candidate1;
|
||||
trans_table[sample_nr][0] = -1.0;
|
||||
}
|
||||
else{
|
||||
new_path_metrics[0] = pm_candidate2;
|
||||
trans_table[sample_nr][0] = 1.0;
|
||||
}
|
||||
|
||||
pm_candidate1 = old_path_metrics[0] - input_symbol_imag + increment[2];
|
||||
pm_candidate2 = old_path_metrics[8] - input_symbol_imag - increment[5];
|
||||
if(pm_candidate1 > pm_candidate2){
|
||||
new_path_metrics[1] = pm_candidate1;
|
||||
trans_table[sample_nr][1] = -1.0;
|
||||
}
|
||||
else{
|
||||
new_path_metrics[1] = pm_candidate2;
|
||||
trans_table[sample_nr][1] = 1.0;
|
||||
}
|
||||
|
||||
pm_candidate1 = old_path_metrics[1] + input_symbol_imag - increment[3];
|
||||
pm_candidate2 = old_path_metrics[9] + input_symbol_imag + increment[4];
|
||||
if(pm_candidate1 > pm_candidate2){
|
||||
new_path_metrics[2] = pm_candidate1;
|
||||
trans_table[sample_nr][2] = -1.0;
|
||||
}
|
||||
else{
|
||||
new_path_metrics[2] = pm_candidate2;
|
||||
trans_table[sample_nr][2] = 1.0;
|
||||
}
|
||||
|
||||
pm_candidate1 = old_path_metrics[1] - input_symbol_imag + increment[3];
|
||||
pm_candidate2 = old_path_metrics[9] - input_symbol_imag - increment[4];
|
||||
if(pm_candidate1 > pm_candidate2){
|
||||
new_path_metrics[3] = pm_candidate1;
|
||||
trans_table[sample_nr][3] = -1.0;
|
||||
}
|
||||
else{
|
||||
new_path_metrics[3] = pm_candidate2;
|
||||
trans_table[sample_nr][3] = 1.0;
|
||||
}
|
||||
|
||||
pm_candidate1 = old_path_metrics[2] + input_symbol_imag - increment[0];
|
||||
pm_candidate2 = old_path_metrics[10] + input_symbol_imag + increment[7];
|
||||
if(pm_candidate1 > pm_candidate2){
|
||||
new_path_metrics[4] = pm_candidate1;
|
||||
trans_table[sample_nr][4] = -1.0;
|
||||
}
|
||||
else{
|
||||
new_path_metrics[4] = pm_candidate2;
|
||||
trans_table[sample_nr][4] = 1.0;
|
||||
}
|
||||
|
||||
pm_candidate1 = old_path_metrics[2] - input_symbol_imag + increment[0];
|
||||
pm_candidate2 = old_path_metrics[10] - input_symbol_imag - increment[7];
|
||||
if(pm_candidate1 > pm_candidate2){
|
||||
new_path_metrics[5] = pm_candidate1;
|
||||
trans_table[sample_nr][5] = -1.0;
|
||||
}
|
||||
else{
|
||||
new_path_metrics[5] = pm_candidate2;
|
||||
trans_table[sample_nr][5] = 1.0;
|
||||
}
|
||||
|
||||
pm_candidate1 = old_path_metrics[3] + input_symbol_imag - increment[1];
|
||||
pm_candidate2 = old_path_metrics[11] + input_symbol_imag + increment[6];
|
||||
if(pm_candidate1 > pm_candidate2){
|
||||
new_path_metrics[6] = pm_candidate1;
|
||||
trans_table[sample_nr][6] = -1.0;
|
||||
}
|
||||
else{
|
||||
new_path_metrics[6] = pm_candidate2;
|
||||
trans_table[sample_nr][6] = 1.0;
|
||||
}
|
||||
|
||||
pm_candidate1 = old_path_metrics[3] - input_symbol_imag + increment[1];
|
||||
pm_candidate2 = old_path_metrics[11] - input_symbol_imag - increment[6];
|
||||
if(pm_candidate1 > pm_candidate2){
|
||||
new_path_metrics[7] = pm_candidate1;
|
||||
trans_table[sample_nr][7] = -1.0;
|
||||
}
|
||||
else{
|
||||
new_path_metrics[7] = pm_candidate2;
|
||||
trans_table[sample_nr][7] = 1.0;
|
||||
}
|
||||
|
||||
pm_candidate1 = old_path_metrics[4] + input_symbol_imag - increment[6];
|
||||
pm_candidate2 = old_path_metrics[12] + input_symbol_imag + increment[1];
|
||||
if(pm_candidate1 > pm_candidate2){
|
||||
new_path_metrics[8] = pm_candidate1;
|
||||
trans_table[sample_nr][8] = -1.0;
|
||||
}
|
||||
else{
|
||||
new_path_metrics[8] = pm_candidate2;
|
||||
trans_table[sample_nr][8] = 1.0;
|
||||
}
|
||||
|
||||
pm_candidate1 = old_path_metrics[4] - input_symbol_imag + increment[6];
|
||||
pm_candidate2 = old_path_metrics[12] - input_symbol_imag - increment[1];
|
||||
if(pm_candidate1 > pm_candidate2){
|
||||
new_path_metrics[9] = pm_candidate1;
|
||||
trans_table[sample_nr][9] = -1.0;
|
||||
}
|
||||
else{
|
||||
new_path_metrics[9] = pm_candidate2;
|
||||
trans_table[sample_nr][9] = 1.0;
|
||||
}
|
||||
|
||||
pm_candidate1 = old_path_metrics[5] + input_symbol_imag - increment[7];
|
||||
pm_candidate2 = old_path_metrics[13] + input_symbol_imag + increment[0];
|
||||
if(pm_candidate1 > pm_candidate2){
|
||||
new_path_metrics[10] = pm_candidate1;
|
||||
trans_table[sample_nr][10] = -1.0;
|
||||
}
|
||||
else{
|
||||
new_path_metrics[10] = pm_candidate2;
|
||||
trans_table[sample_nr][10] = 1.0;
|
||||
}
|
||||
|
||||
pm_candidate1 = old_path_metrics[5] - input_symbol_imag + increment[7];
|
||||
pm_candidate2 = old_path_metrics[13] - input_symbol_imag - increment[0];
|
||||
if(pm_candidate1 > pm_candidate2){
|
||||
new_path_metrics[11] = pm_candidate1;
|
||||
trans_table[sample_nr][11] = -1.0;
|
||||
}
|
||||
else{
|
||||
new_path_metrics[11] = pm_candidate2;
|
||||
trans_table[sample_nr][11] = 1.0;
|
||||
}
|
||||
|
||||
pm_candidate1 = old_path_metrics[6] + input_symbol_imag - increment[4];
|
||||
pm_candidate2 = old_path_metrics[14] + input_symbol_imag + increment[3];
|
||||
if(pm_candidate1 > pm_candidate2){
|
||||
new_path_metrics[12] = pm_candidate1;
|
||||
trans_table[sample_nr][12] = -1.0;
|
||||
}
|
||||
else{
|
||||
new_path_metrics[12] = pm_candidate2;
|
||||
trans_table[sample_nr][12] = 1.0;
|
||||
}
|
||||
|
||||
pm_candidate1 = old_path_metrics[6] - input_symbol_imag + increment[4];
|
||||
pm_candidate2 = old_path_metrics[14] - input_symbol_imag - increment[3];
|
||||
if(pm_candidate1 > pm_candidate2){
|
||||
new_path_metrics[13] = pm_candidate1;
|
||||
trans_table[sample_nr][13] = -1.0;
|
||||
}
|
||||
else{
|
||||
new_path_metrics[13] = pm_candidate2;
|
||||
trans_table[sample_nr][13] = 1.0;
|
||||
}
|
||||
|
||||
pm_candidate1 = old_path_metrics[7] + input_symbol_imag - increment[5];
|
||||
pm_candidate2 = old_path_metrics[15] + input_symbol_imag + increment[2];
|
||||
if(pm_candidate1 > pm_candidate2){
|
||||
new_path_metrics[14] = pm_candidate1;
|
||||
trans_table[sample_nr][14] = -1.0;
|
||||
}
|
||||
else{
|
||||
new_path_metrics[14] = pm_candidate2;
|
||||
trans_table[sample_nr][14] = 1.0;
|
||||
}
|
||||
|
||||
pm_candidate1 = old_path_metrics[7] - input_symbol_imag + increment[5];
|
||||
pm_candidate2 = old_path_metrics[15] - input_symbol_imag - increment[2];
|
||||
if(pm_candidate1 > pm_candidate2){
|
||||
new_path_metrics[15] = pm_candidate1;
|
||||
trans_table[sample_nr][15] = -1.0;
|
||||
}
|
||||
else{
|
||||
new_path_metrics[15] = pm_candidate2;
|
||||
trans_table[sample_nr][15] = 1.0;
|
||||
}
|
||||
tmp=old_path_metrics;
|
||||
old_path_metrics=new_path_metrics;
|
||||
new_path_metrics=tmp;
|
||||
|
||||
sample_nr++;
|
||||
if(sample_nr==samples_num)
|
||||
break;
|
||||
|
||||
//Processing real states
|
||||
real_imag=0;
|
||||
input_symbol_real = input[sample_nr].real();
|
||||
|
||||
pm_candidate1 = old_path_metrics[0] - input_symbol_real - increment[7];
|
||||
pm_candidate2 = old_path_metrics[8] - input_symbol_real + increment[0];
|
||||
if(pm_candidate1 > pm_candidate2){
|
||||
new_path_metrics[0] = pm_candidate1;
|
||||
trans_table[sample_nr][0] = -1.0;
|
||||
}
|
||||
else{
|
||||
new_path_metrics[0] = pm_candidate2;
|
||||
trans_table[sample_nr][0] = 1.0;
|
||||
}
|
||||
|
||||
pm_candidate1 = old_path_metrics[0] + input_symbol_real + increment[7];
|
||||
pm_candidate2 = old_path_metrics[8] + input_symbol_real - increment[0];
|
||||
if(pm_candidate1 > pm_candidate2){
|
||||
new_path_metrics[1] = pm_candidate1;
|
||||
trans_table[sample_nr][1] = -1.0;
|
||||
}
|
||||
else{
|
||||
new_path_metrics[1] = pm_candidate2;
|
||||
trans_table[sample_nr][1] = 1.0;
|
||||
}
|
||||
|
||||
pm_candidate1 = old_path_metrics[1] - input_symbol_real - increment[6];
|
||||
pm_candidate2 = old_path_metrics[9] - input_symbol_real + increment[1];
|
||||
if(pm_candidate1 > pm_candidate2){
|
||||
new_path_metrics[2] = pm_candidate1;
|
||||
trans_table[sample_nr][2] = -1.0;
|
||||
}
|
||||
else{
|
||||
new_path_metrics[2] = pm_candidate2;
|
||||
trans_table[sample_nr][2] = 1.0;
|
||||
}
|
||||
|
||||
pm_candidate1 = old_path_metrics[1] + input_symbol_real + increment[6];
|
||||
pm_candidate2 = old_path_metrics[9] + input_symbol_real - increment[1];
|
||||
if(pm_candidate1 > pm_candidate2){
|
||||
new_path_metrics[3] = pm_candidate1;
|
||||
trans_table[sample_nr][3] = -1.0;
|
||||
}
|
||||
else{
|
||||
new_path_metrics[3] = pm_candidate2;
|
||||
trans_table[sample_nr][3] = 1.0;
|
||||
}
|
||||
|
||||
pm_candidate1 = old_path_metrics[2] - input_symbol_real - increment[5];
|
||||
pm_candidate2 = old_path_metrics[10] - input_symbol_real + increment[2];
|
||||
if(pm_candidate1 > pm_candidate2){
|
||||
new_path_metrics[4] = pm_candidate1;
|
||||
trans_table[sample_nr][4] = -1.0;
|
||||
}
|
||||
else{
|
||||
new_path_metrics[4] = pm_candidate2;
|
||||
trans_table[sample_nr][4] = 1.0;
|
||||
}
|
||||
|
||||
pm_candidate1 = old_path_metrics[2] + input_symbol_real + increment[5];
|
||||
pm_candidate2 = old_path_metrics[10] + input_symbol_real - increment[2];
|
||||
if(pm_candidate1 > pm_candidate2){
|
||||
new_path_metrics[5] = pm_candidate1;
|
||||
trans_table[sample_nr][5] = -1.0;
|
||||
}
|
||||
else{
|
||||
new_path_metrics[5] = pm_candidate2;
|
||||
trans_table[sample_nr][5] = 1.0;
|
||||
}
|
||||
|
||||
pm_candidate1 = old_path_metrics[3] - input_symbol_real - increment[4];
|
||||
pm_candidate2 = old_path_metrics[11] - input_symbol_real + increment[3];
|
||||
if(pm_candidate1 > pm_candidate2){
|
||||
new_path_metrics[6] = pm_candidate1;
|
||||
trans_table[sample_nr][6] = -1.0;
|
||||
}
|
||||
else{
|
||||
new_path_metrics[6] = pm_candidate2;
|
||||
trans_table[sample_nr][6] = 1.0;
|
||||
}
|
||||
|
||||
pm_candidate1 = old_path_metrics[3] + input_symbol_real + increment[4];
|
||||
pm_candidate2 = old_path_metrics[11] + input_symbol_real - increment[3];
|
||||
if(pm_candidate1 > pm_candidate2){
|
||||
new_path_metrics[7] = pm_candidate1;
|
||||
trans_table[sample_nr][7] = -1.0;
|
||||
}
|
||||
else{
|
||||
new_path_metrics[7] = pm_candidate2;
|
||||
trans_table[sample_nr][7] = 1.0;
|
||||
}
|
||||
|
||||
pm_candidate1 = old_path_metrics[4] - input_symbol_real - increment[3];
|
||||
pm_candidate2 = old_path_metrics[12] - input_symbol_real + increment[4];
|
||||
if(pm_candidate1 > pm_candidate2){
|
||||
new_path_metrics[8] = pm_candidate1;
|
||||
trans_table[sample_nr][8] = -1.0;
|
||||
}
|
||||
else{
|
||||
new_path_metrics[8] = pm_candidate2;
|
||||
trans_table[sample_nr][8] = 1.0;
|
||||
}
|
||||
|
||||
pm_candidate1 = old_path_metrics[4] + input_symbol_real + increment[3];
|
||||
pm_candidate2 = old_path_metrics[12] + input_symbol_real - increment[4];
|
||||
if(pm_candidate1 > pm_candidate2){
|
||||
new_path_metrics[9] = pm_candidate1;
|
||||
trans_table[sample_nr][9] = -1.0;
|
||||
}
|
||||
else{
|
||||
new_path_metrics[9] = pm_candidate2;
|
||||
trans_table[sample_nr][9] = 1.0;
|
||||
}
|
||||
|
||||
pm_candidate1 = old_path_metrics[5] - input_symbol_real - increment[2];
|
||||
pm_candidate2 = old_path_metrics[13] - input_symbol_real + increment[5];
|
||||
if(pm_candidate1 > pm_candidate2){
|
||||
new_path_metrics[10] = pm_candidate1;
|
||||
trans_table[sample_nr][10] = -1.0;
|
||||
}
|
||||
else{
|
||||
new_path_metrics[10] = pm_candidate2;
|
||||
trans_table[sample_nr][10] = 1.0;
|
||||
}
|
||||
|
||||
pm_candidate1 = old_path_metrics[5] + input_symbol_real + increment[2];
|
||||
pm_candidate2 = old_path_metrics[13] + input_symbol_real - increment[5];
|
||||
if(pm_candidate1 > pm_candidate2){
|
||||
new_path_metrics[11] = pm_candidate1;
|
||||
trans_table[sample_nr][11] = -1.0;
|
||||
}
|
||||
else{
|
||||
new_path_metrics[11] = pm_candidate2;
|
||||
trans_table[sample_nr][11] = 1.0;
|
||||
}
|
||||
|
||||
pm_candidate1 = old_path_metrics[6] - input_symbol_real - increment[1];
|
||||
pm_candidate2 = old_path_metrics[14] - input_symbol_real + increment[6];
|
||||
if(pm_candidate1 > pm_candidate2){
|
||||
new_path_metrics[12] = pm_candidate1;
|
||||
trans_table[sample_nr][12] = -1.0;
|
||||
}
|
||||
else{
|
||||
new_path_metrics[12] = pm_candidate2;
|
||||
trans_table[sample_nr][12] = 1.0;
|
||||
}
|
||||
|
||||
pm_candidate1 = old_path_metrics[6] + input_symbol_real + increment[1];
|
||||
pm_candidate2 = old_path_metrics[14] + input_symbol_real - increment[6];
|
||||
if(pm_candidate1 > pm_candidate2){
|
||||
new_path_metrics[13] = pm_candidate1;
|
||||
trans_table[sample_nr][13] = -1.0;
|
||||
}
|
||||
else{
|
||||
new_path_metrics[13] = pm_candidate2;
|
||||
trans_table[sample_nr][13] = 1.0;
|
||||
}
|
||||
|
||||
pm_candidate1 = old_path_metrics[7] - input_symbol_real - increment[0];
|
||||
pm_candidate2 = old_path_metrics[15] - input_symbol_real + increment[7];
|
||||
if(pm_candidate1 > pm_candidate2){
|
||||
new_path_metrics[14] = pm_candidate1;
|
||||
trans_table[sample_nr][14] = -1.0;
|
||||
}
|
||||
else{
|
||||
new_path_metrics[14] = pm_candidate2;
|
||||
trans_table[sample_nr][14] = 1.0;
|
||||
}
|
||||
|
||||
pm_candidate1 = old_path_metrics[7] + input_symbol_real + increment[0];
|
||||
pm_candidate2 = old_path_metrics[15] + input_symbol_real - increment[7];
|
||||
if(pm_candidate1 > pm_candidate2){
|
||||
new_path_metrics[15] = pm_candidate1;
|
||||
trans_table[sample_nr][15] = -1.0;
|
||||
}
|
||||
else{
|
||||
new_path_metrics[15] = pm_candidate2;
|
||||
trans_table[sample_nr][15] = 1.0;
|
||||
}
|
||||
tmp=old_path_metrics;
|
||||
old_path_metrics=new_path_metrics;
|
||||
new_path_metrics=tmp;
|
||||
|
||||
sample_nr++;
|
||||
}
|
||||
|
||||
/*
|
||||
* Find the best from the stop states by comparing their path metrics.
|
||||
* Not every stop state is always possible, so we are searching in
|
||||
* a subset of them.
|
||||
*/
|
||||
unsigned int best_stop_state;
|
||||
float stop_state_metric, max_stop_state_metric;
|
||||
best_stop_state = stop_states[0];
|
||||
max_stop_state_metric = old_path_metrics[best_stop_state];
|
||||
for(i=1; i< stops_num; i++){
|
||||
stop_state_metric = old_path_metrics[stop_states[i]];
|
||||
if(stop_state_metric > max_stop_state_metric){
|
||||
max_stop_state_metric = stop_state_metric;
|
||||
best_stop_state = stop_states[i];
|
||||
}
|
||||
}
|
||||
|
||||
/*
|
||||
* This table was generated with hope that it gives a litle speedup during
|
||||
* traceback stage.
|
||||
* Received bit is related to the number of state in the trellis.
|
||||
* I've numbered states so their parity (number of ones) is related
|
||||
* to a received bit.
|
||||
*/
|
||||
static const unsigned int parity_table[PATHS_NUM] = { 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, };
|
||||
|
||||
/*
|
||||
* Table of previous states in the trellis diagram.
|
||||
* For GMSK modulation every state has two previous states.
|
||||
* Example:
|
||||
* previous_state_nr1 = prev_table[current_state_nr][0]
|
||||
* previous_state_nr2 = prev_table[current_state_nr][1]
|
||||
*/
|
||||
static const unsigned int prev_table[PATHS_NUM][2] = { {0,8}, {0,8}, {1,9}, {1,9}, {2,10}, {2,10}, {3,11}, {3,11}, {4,12}, {4,12}, {5,13}, {5,13}, {6,14}, {6,14}, {7,15}, {7,15}, };
|
||||
|
||||
/*
|
||||
* Traceback and differential decoding of received sequence.
|
||||
* Decisions stored in trans_table are used to restore best path in the trellis.
|
||||
*/
|
||||
sample_nr=samples_num;
|
||||
unsigned int state_nr=best_stop_state;
|
||||
unsigned int decision;
|
||||
bool out_bit=0;
|
||||
|
||||
while(sample_nr>0){
|
||||
sample_nr--;
|
||||
decision = (trans_table[sample_nr][state_nr]>0);
|
||||
|
||||
if(decision != out_bit)
|
||||
output[sample_nr]=-trans_table[sample_nr][state_nr];
|
||||
else
|
||||
output[sample_nr]=trans_table[sample_nr][state_nr];
|
||||
|
||||
out_bit = out_bit ^ real_imag ^ parity_table[state_nr];
|
||||
state_nr = prev_table[state_nr][decision];
|
||||
real_imag = !real_imag;
|
||||
}
|
||||
}
|
||||
void viterbi_detector(const gr_complex * input, unsigned int samples_num, gr_complex * rhh, unsigned int start_state, const unsigned int * stop_states, unsigned int stops_num, float * output);
|
||||
|
|
|
|||
Loading…
Reference in New Issue