| /* -*- c++ -*- */ |
| /* |
| * @file |
| * @author Piotr Krysik <pkrysik@stud.elka.pw.edu.pl> |
| * @section LICENSE |
| * |
| * 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, 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; see the file COPYING. If not, write to |
| * the Free Software Foundation, Inc., 51 Franklin Street, |
| * Boston, MA 02110-1301, USA. |
| */ |
| |
| /* |
| * viterbi_detector: |
| * This part does the detection of received sequnece. |
| * Employed algorithm is viterbi Maximum Likehood Sequence Estimation. |
| * At this moment it gives hard decisions on the output, but |
| * it was designed with soft decisions in mind. |
| * |
| * SYNTAX: 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) |
| * |
| * INPUT: input: Complex received signal afted matched filtering. |
| * samples_num: Number of samples in the input table. |
| * rhh: The autocorrelation of the estimated channel |
| * impulse response. |
| * start_state: Number of the start point. In GSM each burst |
| * starts with sequence of three bits (0,0,0) which |
| * indicates start point of the algorithm. |
| * stop_states: Table with numbers of possible stop states. |
| * stops_num: Number of possible stop states |
| * |
| * |
| * OUTPUT: output: Differentially decoded hard output of the algorithm: |
| * -1 for logical "0" and 1 for logical "1" |
| * |
| * SUB_FUNC: none |
| * |
| * 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; |
| } |
| } |