core algorithms such as viterbi/siso were refactored

and implemented using templates.
Minor renaming of some files

1 files changed, 466 insertions, 0 deletions

diff --git a/gr-trellis/src/lib/trellis_sccc_decoder_combined_XX.cc.t b/gr-trellis/src/lib/trellis_sccc_decoder_combined_XX.cc.t
new file mode 100644
index 000000000..c23445e61
--- /dev/null
+++ b/gr-trellis/src/lib/trellis_sccc_decoder_combined_XX.cc.t
@@ -0,0 +1,466 @@
+/* -*- c++ -*- */
+/*
+ * Copyright 2004,2010 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.
+ */
+
+// @WARNING@
+
+#ifdef HAVE_CONFIG_H
+#include "config.h"
+#endif
+
+#include <@NAME@.h>
+#include <gr_io_signature.h>
+#include <assert.h>
+#include <iostream>
+  
+static const float INF = 1.0e9;
+
+@SPTR_NAME@ 
+trellis_make_@BASE_NAME@ (
+  const fsm &FSMo, int STo0, int SToK,
+  const fsm &FSMi, int STi0, int STiK,
+  const interleaver &INTERLEAVER,
+  int blocklength,
+  int repetitions,
+  trellis_siso_type_t SISO_TYPE,
+  int D,
+  const std::vector<@I_TYPE@> &TABLE,
+  trellis_metric_type_t METRIC_TYPE
+)
+{
+  return gnuradio::get_initial_sptr (new @NAME@ (
+    FSMo, STo0, SToK,
+    FSMi, STi0, STiK,
+    INTERLEAVER,
+    blocklength,
+    repetitions,
+    SISO_TYPE,
+    D,
+    TABLE,METRIC_TYPE
+    ));
+}
+
+@NAME@::@NAME@ (
+  const fsm &FSMo, int STo0, int SToK,
+  const fsm &FSMi, int STi0, int STiK,
+  const interleaver &INTERLEAVER,
+  int blocklength,
+  int repetitions,
+  trellis_siso_type_t SISO_TYPE,
+  int D,
+  const std::vector<@I_TYPE@> &TABLE,
+  trellis_metric_type_t METRIC_TYPE
+)
+  : gr_block ("@BASE_NAME@",
+			  gr_make_io_signature (1, 1, sizeof (@I_TYPE@)),
+			  gr_make_io_signature (1, 1, sizeof (@O_TYPE@))),  
+  d_FSMo (FSMo), d_STo0 (STo0), d_SToK (SToK),
+  d_FSMi (FSMo), d_STi0 (STi0), d_STiK (STiK),
+  d_INTERLEAVER (INTERLEAVER),
+  d_blocklength (blocklength),
+  d_repetitions (repetitions),
+  d_SISO_TYPE (SISO_TYPE),
+  d_D (D),
+  d_TABLE (TABLE),
+  d_METRIC_TYPE (METRIC_TYPE)
+{
+    set_relative_rate (1.0 / ((double) d_D));
+    set_output_multiple (d_blocklength);
+}
+
+
+void
+@NAME@::forecast (int noutput_items, gr_vector_int &ninput_items_required)
+{
+  assert (noutput_items % d_blocklength == 0);
+  int input_required =  d_D * noutput_items ;
+  unsigned ninputs = ninput_items_required.size();
+  for (unsigned int i = 0; i < ninputs; i++) {
+    ninput_items_required[i] = input_required;
+  }
+}
+
+//=======================================
+
+inline float min(float a, float b)
+{
+  return a <= b ? a : b;
+}
+
+inline float min_star(float a, float b)
+{
+  return (a <= b ? a : b)-log(1+exp(a <= b ? a-b : b-a));
+}
+
+
+//=======================================
+
+void siso_outer_algorithm(int I, int S, int O,
+             const std::vector<int> &NS,
+             const std::vector<int> &OS,
+             const std::vector< std::vector<int> > &PS,
+             const std::vector< std::vector<int> > &PI,
+             int K,
+             int S0,int SK,
+             bool POSTI, bool POSTO,
+             float (*p2mymin)(float,float),
+             const float *priori, const float *prioro, float *post,
+             @O_TYPE@ *data
+             )
+{
+  float norm,mm,minm;
+  std::vector<float> alpha(S*(K+1));
+  std::vector<float> beta(S*(K+1));
+
+
+  if(S0<0) { // initial state not specified
+      for(int i=0;i<S;i++) alpha[0*S+i]=0;
+  }
+  else {
+      for(int i=0;i<S;i++) alpha[0*S+i]=INF;
+      alpha[0*S+S0]=0.0;
+  }
+
+  for(int k=0;k<K;k++) { // forward recursion
+      norm=INF;
+      for(int j=0;j<S;j++) {
+          minm=INF;
+          for(unsigned int i=0;i<PS[j].size();i++) {
+              //int i0 = j*I+i;
+              mm=alpha[k*S+PS[j][i]]+priori[k*I+PI[j][i]]+prioro[k*O+OS[PS[j][i]*I+PI[j][i]]];
+              minm=(*p2mymin)(minm,mm);
+          }
+          alpha[(k+1)*S+j]=minm;
+          if(minm<norm) norm=minm;
+      }
+      for(int j=0;j<S;j++)
+          alpha[(k+1)*S+j]-=norm; // normalize total metrics so they do not explode
+  }
+
+  if(SK<0) { // final state not specified
+      for(int i=0;i<S;i++) beta[K*S+i]=0;
+  }
+  else {
+      for(int i=0;i<S;i++) beta[K*S+i]=INF;
+      beta[K*S+SK]=0.0;
+  }
+
+  for(int k=K-1;k>=0;k--) { // backward recursion
+      norm=INF;
+      for(int j=0;j<S;j++) {
+          minm=INF;
+          for(int i=0;i<I;i++) {
+              int i0 = j*I+i;
+              mm=beta[(k+1)*S+NS[i0]]+priori[k*I+i]+prioro[k*O+OS[i0]];
+              minm=(*p2mymin)(minm,mm);
+          }
+          beta[k*S+j]=minm;
+          if(minm<norm) norm=minm;
+      }
+      for(int j=0;j<S;j++)
+          beta[k*S+j]-=norm; // normalize total metrics so they do not explode
+  }
+
+if (POSTI && POSTO)
+{
+  for(int k=0;k<K;k++) { // input combining
+      norm=INF;
+      for(int i=0;i<I;i++) {
+          minm=INF;
+          for(int j=0;j<S;j++) {
+              mm=alpha[k*S+j]+prioro[k*O+OS[j*I+i]]+beta[(k+1)*S+NS[j*I+i]];
+              minm=(*p2mymin)(minm,mm);
+          }
+          post[k*(I+O)+i]=minm;
+          if(minm<norm) norm=minm;
+      }
+      for(int i=0;i<I;i++)
+          post[k*(I+O)+i]-=norm; // normalize metrics
+  }
+
+
+  for(int k=0;k<K;k++) { // output combining
+      norm=INF;
+      for(int n=0;n<O;n++) {
+          minm=INF;
+          for(int j=0;j<S;j++) {
+              for(int i=0;i<I;i++) {
+                  mm= (n==OS[j*I+i] ? alpha[k*S+j]+priori[k*I+i]+beta[(k+1)*S+NS[j*I+i]] : INF);
+                  minm=(*p2mymin)(minm,mm);
+              }
+          }
+          post[k*(I+O)+I+n]=minm;
+          if(minm<norm) norm=minm;
+      }
+      for(int n=0;n<O;n++)
+          post[k*(I+O)+I+n]-=norm; // normalize metrics
+  }
+}
+else if(POSTI)
+{
+  for(int k=0;k<K;k++) { // input combining
+      norm=INF;
+      for(int i=0;i<I;i++) {
+          minm=INF;
+          for(int j=0;j<S;j++) {
+              mm=alpha[k*S+j]+prioro[k*O+OS[j*I+i]]+beta[(k+1)*S+NS[j*I+i]];
+              minm=(*p2mymin)(minm,mm);
+          }
+          post[k*I+i]=minm;
+          if(minm<norm) norm=minm;
+      }
+      for(int i=0;i<I;i++)
+          post[k*I+i]-=norm; // normalize metrics
+  }
+}
+else if(POSTO)
+{
+  for(int k=0;k<K;k++) { // output combining
+      norm=INF;
+      for(int n=0;n<O;n++) {
+          minm=INF;
+          for(int j=0;j<S;j++) {
+              for(int i=0;i<I;i++) {
+                  mm= (n==OS[j*I+i] ? alpha[k*S+j]+priori[k*I+i]+beta[(k+1)*S+NS[j*I+i]] : INF);
+                  minm=(*p2mymin)(minm,mm);
+              }
+          }
+          post[k*O+n]=minm;
+          if(minm<norm) norm=minm;
+      }
+      for(int n=0;n<O;n++)
+          post[k*O+n]-=norm; // normalize metrics
+  }
+}
+else
+    throw std::runtime_error ("Not both POSTI and POSTO can be false.");
+
+}
+
+//==================================
+
+void siso_inner_algorithm_combined(int I, int S, int O,
+             const std::vector<int> &NS,
+             const std::vector<int> &OS,
+             const std::vector< std::vector<int> > &PS,
+             const std::vector< std::vector<int> > &PI,
+             int K,
+             int S0,int SK,
+             bool POSTI, bool POSTO,
+             float (*p2mymin)(float,float),
+             int D,
+             const std::vector<@I_TYPE@> &TABLE,
+             trellis_metric_type_t TYPE,
+             const float *priori, const @I_TYPE@ *observations, float *post
+             )
+{
+  float norm,mm,minm;
+  std::vector<float> alpha(S*(K+1));
+  std::vector<float> beta(S*(K+1));
+  float *prioro = new float[O*K];
+
+
+  if(S0<0) { // initial state not specified
+      for(int i=0;i<S;i++) alpha[0*S+i]=0;
+  }
+  else {
+      for(int i=0;i<S;i++) alpha[0*S+i]=INF;
+      alpha[0*S+S0]=0.0;
+  }
+
+  for(int k=0;k<K;k++) { // forward recursion
+      calc_metric(O, D, TABLE, &(observations[k*D]), &(prioro[k*O]),TYPE); // calc metrics
+      norm=INF;
+      for(int j=0;j<S;j++) {
+          minm=INF;
+          for(unsigned int i=0;i<PS[j].size();i++) {
+              //int i0 = j*I+i;
+              mm=alpha[k*S+PS[j][i]]+priori[k*I+PI[j][i]]+prioro[k*O+OS[PS[j][i]*I+PI[j][i]]];
+              minm=(*p2mymin)(minm,mm);
+          }
+          alpha[(k+1)*S+j]=minm;
+          if(minm<norm) norm=minm;
+      }
+      for(int j=0;j<S;j++)
+          alpha[(k+1)*S+j]-=norm; // normalize total metrics so they do not explode
+  }
+
+  if(SK<0) { // final state not specified
+      for(int i=0;i<S;i++) beta[K*S+i]=0;
+  }
+  else {
+      for(int i=0;i<S;i++) beta[K*S+i]=INF;
+      beta[K*S+SK]=0.0;
+  }
+
+  for(int k=K-1;k>=0;k--) { // backward recursion
+      norm=INF;
+      for(int j=0;j<S;j++) {
+          minm=INF;
+          for(int i=0;i<I;i++) {
+              int i0 = j*I+i;
+              mm=beta[(k+1)*S+NS[i0]]+priori[k*I+i]+prioro[k*O+OS[i0]];
+              minm=(*p2mymin)(minm,mm);
+          }
+          beta[k*S+j]=minm;
+          if(minm<norm) norm=minm;
+      }
+      for(int j=0;j<S;j++)
+          beta[k*S+j]-=norm; // normalize total metrics so they do not explode
+  }
+
+
+  if (POSTI && POSTO)
+  {
+    for(int k=0;k<K;k++) { // input combining
+        norm=INF;
+        for(int i=0;i<I;i++) {
+            minm=INF;
+            for(int j=0;j<S;j++) {
+                mm=alpha[k*S+j]+prioro[k*O+OS[j*I+i]]+beta[(k+1)*S+NS[j*I+i]];
+                minm=(*p2mymin)(minm,mm);
+            }
+            post[k*(I+O)+i]=minm;
+            if(minm<norm) norm=minm;
+        }
+        for(int i=0;i<I;i++)
+            post[k*(I+O)+i]-=norm; // normalize metrics
+    }
+
+
+    for(int k=0;k<K;k++) { // output combining
+        norm=INF;
+        for(int n=0;n<O;n++) {
+            minm=INF;
+            for(int j=0;j<S;j++) {
+                for(int i=0;i<I;i++) {
+                    mm= (n==OS[j*I+i] ? alpha[k*S+j]+priori[k*I+i]+beta[(k+1)*S+NS[j*I+i]] : INF);
+                    minm=(*p2mymin)(minm,mm);
+                }
+            }
+            post[k*(I+O)+I+n]=minm;
+            if(minm<norm) norm=minm;
+        }
+        for(int n=0;n<O;n++)
+            post[k*(I+O)+I+n]-=norm; // normalize metrics
+    }
+  }
+  else if(POSTI)
+  {
+    for(int k=0;k<K;k++) { // input combining
+        norm=INF;
+        for(int i=0;i<I;i++) {
+            minm=INF;
+            for(int j=0;j<S;j++) {
+                mm=alpha[k*S+j]+prioro[k*O+OS[j*I+i]]+beta[(k+1)*S+NS[j*I+i]];
+                minm=(*p2mymin)(minm,mm);
+            }
+            post[k*I+i]=minm;
+            if(minm<norm) norm=minm;
+        }
+        for(int i=0;i<I;i++)
+            post[k*I+i]-=norm; // normalize metrics
+    }
+  }
+  else if(POSTO)
+  {
+    for(int k=0;k<K;k++) { // output combining
+        norm=INF;
+        for(int n=0;n<O;n++) {
+            minm=INF;
+            for(int j=0;j<S;j++) {
+                for(int i=0;i<I;i++) {
+                    mm= (n==OS[j*I+i] ? alpha[k*S+j]+priori[k*I+i]+beta[(k+1)*S+NS[j*I+i]] : INF);
+                    minm=(*p2mymin)(minm,mm);
+                }
+            }
+            post[k*O+n]=minm;
+            if(minm<norm) norm=minm;
+        }
+        for(int n=0;n<O;n++)
+            post[k*O+n]-=norm; // normalize metrics
+    }
+  }
+  else
+    throw std::runtime_error ("Not both POSTI and POSTO can be false.");
+
+  delete [] prioro;
+
+}
+
+
+//==========================================================
+
+
+void sccc_decoder_combined(
+      const fsm &FSMo, int STo0, int SToK,
+      const fsm &FSMi, int STi0, int STiK,
+      const interleaver &INTERLEAVER, int blocklength, int repetitions,
+      float (*p2mymin)(float,float), 
+      int D, const std::vector<@I_TYPE@> &TABLE, 
+      trellis_metric_type_t METRIC_TYPE,
+      const @I_TYPE@ *observations, @O_TYPE@ *data
+    )
+{
+
+
+
+
+}
+
+
+
+      //d_FSMo.I(),d_FSMo.S(),d_FSMo.O(),d_FSMo.NS(),d_FSMo.OS(),d_FSMo.PS(),d_FSMo.PI(),
+//===========================================================
+
+int
+@NAME@::general_work (int noutput_items,
+                        gr_vector_int &ninput_items,
+                        gr_vector_const_void_star &input_items,
+                        gr_vector_void_star &output_items)
+{
+  assert (noutput_items % d_blocklength == 0);
+  int nblocks = noutput_items / d_blocklength;
+
+  float (*p2min)(float, float) = NULL;
+  if(d_SISO_TYPE == TRELLIS_MIN_SUM)
+    p2min = &min;
+  else if(d_SISO_TYPE == TRELLIS_SUM_PRODUCT)
+    p2min = &min_star;
+
+
+  const @I_TYPE@ *in = (const @I_TYPE@ *) input_items[0];
+  @O_TYPE@ *out = (@O_TYPE@ *) output_items[0];
+  for (int n=0;n<nblocks;n++) {
+    sccc_decoder_combined(
+      d_FSMo, d_STo0, d_SToK,
+      d_FSMi, d_STi0, d_STiK,
+      d_INTERLEAVER, d_blocklength, d_repetitions,
+      p2min, 
+      d_D,d_TABLE, 
+      d_METRIC_TYPE, 
+      &(in[n*d_blocklength*d_D]),&(out[n*d_blocklength])
+    );
+  }
+
+  consume_each (d_D * noutput_items );
+  return noutput_items;
+}