siso_nsc.cpp
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#include <itpp/comm/siso.h>
#include <itpp/base/itcompat.h>
#include <limits>
#ifndef INFINITY
#define INFINITY std::numeric_limits<double>::infinity()
#endif
namespace itpp
{
void SISO::gen_nsctrellis(void)
/*
generate trellis for non systematic non recursive convolutional codes
r - number of outputs of the CC
mem_len - memory length of the CC
*/
{
//get parameters
int r = gen.rows();
int mem_len = gen.cols()-1;
//other parameters
register int n,k,j,p;
itpp::bin inputs[] = {0,1};
int index;
nsctrellis.stateNb = (1<<mem_len);
nsctrellis.output = new double[nsctrellis.stateNb*2*r];
nsctrellis.nextState = new int[nsctrellis.stateNb*2];
nsctrellis.prevState = new int[nsctrellis.stateNb*2];
nsctrellis.input = new int[nsctrellis.stateNb*2];
itpp::bvec enc_mem(mem_len);
itpp::bin out;
#pragma omp parallel for private(n,k,j,p,enc_mem,out)
for (n=0; n<nsctrellis.stateNb; n++) //initial state
{
enc_mem = itpp::dec2bin(mem_len, n);
//output
for (k=0; k<2; k++)
{
for (j=0; j<r; j++)
{
out = inputs[k]*gen(j,0);
for (p=1; p<=mem_len; p++)
{
out ^= (enc_mem[p-1]*gen(j,p));//0 or 1
}
nsctrellis.output[n+k*nsctrellis.stateNb+j*nsctrellis.stateNb*2] = double(out);
}
}
//next state
for (j=(mem_len-1); j>0; j--)
{
enc_mem[j] = enc_mem[j-1];
}
for (k=0; k<2; k++)
{
enc_mem[0] = inputs[k];
nsctrellis.nextState[n+k*nsctrellis.stateNb] = itpp::bin2dec(enc_mem, true);
}
}
#pragma omp parallel for private(n,k,j,index)
for (j=0; j<nsctrellis.stateNb; j++)
{
index = 0;
for (n=0; n<nsctrellis.stateNb; n++)
{
for (k=0; k<2; k++)
{
if (nsctrellis.nextState[n+k*nsctrellis.stateNb]==j)
{
nsctrellis.prevState[j+index*nsctrellis.stateNb] = n;
nsctrellis.input[j+index*nsctrellis.stateNb] = k;//0 or 1
index++;
}
}
}
}
}
void SISO::nsc_logMAP(itpp::vec &extrinsic_coded, itpp::vec &extrinsic_data, const itpp::vec &intrinsic_coded, const itpp::vec &apriori_data)
/*
* generalized decoder for NSC codes (after the NSC code a scrambler of pattern phi is used) using log MAP alg.
* extrinsic_coded - extrinsic information of coded bits (output if the shaping filter)
* extrinsic_data - extrinsic information of data bits
* intrinsic_coded - intrinsic information of coded bits
* apriori_data - a priori information of data bits
*/
{
//get parameters
int N = apriori_data.length();
int Nc = scrambler_pattern.length();
int r = gen.rows();//number of outputs of the CC
//other parameters
register int n,k,m,mp,j,i;
int pstates[2];
int nstates[2];
int inputs[2];
double C[2];//log(gamma)
double sum;
double sumbis;
int index;
//initialize trellis
gen_nsctrellis();
//initialize log(alpha) and log(beta)
double* A = new double[nsctrellis.stateNb*(N+1)];
double* B = new double[nsctrellis.stateNb*(N+1)];
A[0] = 0;
B[N*nsctrellis.stateNb] = 0;
sum = (tail?-INFINITY:0);
#pragma omp parallel for private(n)
for (n=1; n<nsctrellis.stateNb; n++)
{
A[n] = -INFINITY;
B[n+N*nsctrellis.stateNb] = sum;//if tail==false the final state is not known
}
#pragma omp parallel sections private(n,sum,m,k,i,j,C)
{
//forward recursion
for (n=1; n<(N+1); n++)
{
sum = 0;//normalization factor
for (m=0; m<nsctrellis.stateNb; m++) //final state
{
for (k=0; k<2; k++)
{
pstates[k] = nsctrellis.prevState[m+nsctrellis.stateNb*k];//determine previous states
inputs[k] = nsctrellis.input[m+nsctrellis.stateNb*k];//determine input
C[k] = (inputs[k]?apriori_data[n-1]:0);//compute log of gamma
}
for (i=0; i<2; i++)//for each C[i]
{
for (k=0; k<r; k++)
{
for (j=0; j<Nc; j++)
{
C[i] += nsctrellis.output[pstates[i]+inputs[i]*nsctrellis.stateNb+k*nsctrellis.stateNb*2]*(1-2*double(scrambler_pattern[j]))*intrinsic_coded[j+k*Nc+(n-1)*Nc*r];
}
}
}
A[m+n*nsctrellis.stateNb] = itpp::log_add(A[pstates[0]+(n-1)*nsctrellis.stateNb]+C[0], A[pstates[1]+(n-1)*nsctrellis.stateNb]+C[1]);
sum += std::exp(A[m+n*nsctrellis.stateNb]);
}
//normalization
sum = std::log(sum);
if (std::isinf(sum))
{
continue;
}
for (m=0; m<nsctrellis.stateNb; m++)
{
A[m+n*nsctrellis.stateNb] -= sum;
}
}
//backward recursion
#pragma omp section
for (n=N-1; n>=0; n--)
{
sum = 0;//normalisation factor
for (m=0; m<nsctrellis.stateNb; m++) //initial state
{
for (k=0; k<2; k++)
{
nstates[k] = nsctrellis.nextState[m+k*nsctrellis.stateNb];//determine next states
C[k] = (k?apriori_data[n]:0);//compute log of gamma
}
for (i=0; i<2; i++)
{
for (k=0; k<r; k++)
{
for (j=0; j<Nc; j++)
{
C[i] += nsctrellis.output[m+i*nsctrellis.stateNb+k*nsctrellis.stateNb*2]*(1-2*double(scrambler_pattern[j]))*intrinsic_coded[j+k*Nc+n*Nc*r];
}
}
}
B[m+n*nsctrellis.stateNb] = itpp::log_add(B[nstates[0]+(n+1)*nsctrellis.stateNb]+C[0], B[nstates[1]+(n+1)*nsctrellis.stateNb]+C[1]);
sum += std::exp(B[m+n*nsctrellis.stateNb]);
}
//normalisation
sum = std::log(sum);
if (std::isinf(sum))
{
continue;
}
for (m=0; m<nsctrellis.stateNb; m++)
{
B[m+n*nsctrellis.stateNb] -= sum;
}
}
}
//compute extrinsic_data
extrinsic_data.set_size(N);
#pragma omp parallel for private(n,k,sum,sumbis)
for (n=1; n<(N+1); n++)
{
sum = 0;
sumbis = 0;
for (k=0; k<(nsctrellis.stateNb/2); k++)
{
sum += std::exp(A[k+(nsctrellis.stateNb/2)+n*nsctrellis.stateNb]+B[k+(nsctrellis.stateNb/2)+n*nsctrellis.stateNb]);//nominator
sumbis += std::exp(A[k+n*nsctrellis.stateNb]+B[k+n*nsctrellis.stateNb]);//denominator
}
extrinsic_data[n-1] = std::log(sum/sumbis)-apriori_data[n-1];
}
//compute extrinsic_coded
double *sum0 = new double[r];
double *sum1 = new double[r];
extrinsic_coded.set_size(N*Nc*r);
for (n=0; n<N; n++)
{
for (k=0; k<r; k++)
{
sum0[k] = 0;
sum1[k] = 0;
}
for (mp=0; mp<nsctrellis.stateNb; mp++) //initial state
{
for (i=0; i<2; i++)
{
m = nsctrellis.nextState[mp+i*nsctrellis.stateNb];//final state
//compute log of sigma
index = (m>=(nsctrellis.stateNb/2));//0 if input is 0, 1 if input is 1
C[0] = (index?apriori_data[n]:0);
for (k=0; k<r; k++)
{
for (j=0; j<Nc; j++)
{
C[0] += nsctrellis.output[mp+index*nsctrellis.stateNb+k*nsctrellis.stateNb*2]*(1-2*double(scrambler_pattern[j]))*intrinsic_coded[j+k*Nc+n*Nc*r];
}
}
C[1] = A[mp+n*nsctrellis.stateNb]+C[0]+B[m+(n+1)*nsctrellis.stateNb];//this is only a buffer
//compute sums
for (k=0; k<r; k++)
{
if (nsctrellis.output[mp+index*nsctrellis.stateNb+k*nsctrellis.stateNb*2])
{
sum1[k] += std::exp(C[1]);
}
else
{
sum0[k] += std::exp(C[1]);
}
}
}
}
for (k=0; k<r; k++)
{
for (j=0; j<Nc; j++)
{
index = j+k*Nc+n*r*Nc;
extrinsic_coded[index] = (1-2*double(scrambler_pattern[j]))*std::log(sum1[k]/sum0[k])-intrinsic_coded[index];
}
}
}
//free memory
delete[] nsctrellis.output;
delete[] nsctrellis.nextState;
delete[] nsctrellis.prevState;
delete[] nsctrellis.input;
delete[] A;
delete[] B;
delete[] sum0;
delete[] sum1;
}
void SISO::nsc_maxlogMAP(itpp::vec &extrinsic_coded, itpp::vec &extrinsic_data, const itpp::vec &intrinsic_coded, const itpp::vec &apriori_data)
/*
* generalized decoder for NSC codes (after the NSC code a scrambler of pattern phi is used) using max log MAP alg.
* extrinsic_coded - extrinsic information of coded bits (output if the shaping filter)
* extrinsic_data - extrinsic information of data bits
* intrinsic_coded - intrinsic information of coded bits
* apriori_data - a priori information of data bits
*/
{
//get parameters
int N = apriori_data.length();
int Nc = scrambler_pattern.length();
int r = gen.rows();//number of outputs of the CC
//other parameters
register int n,k,m,mp,j,i;
int pstates[2];
int nstates[2];
int inputs[2];
double C[2];//log(gamma)
double sum;
double sumbis;
int index;
//initialize trellis
gen_nsctrellis();
//initialize log(alpha) and log(beta)
double* A = new double[nsctrellis.stateNb*(N+1)];
double* B = new double[nsctrellis.stateNb*(N+1)];
A[0] = 0;
B[N*nsctrellis.stateNb] = 0;
sum = (tail?-INFINITY:0);
#pragma omp parallel for private(n)
for (n=1; n<nsctrellis.stateNb; n++)
{
A[n] = -INFINITY;
B[n+N*nsctrellis.stateNb] = sum;//if tail==false the final state is not known
}
//forward recursion
#pragma omp parallel sections private(n,sum,m,k,i,j,C)
{
for (n=1; n<(N+1); n++)
{
sum = -INFINITY;//normalisation factor
for (m=0; m<nsctrellis.stateNb; m++) //final state
{
for (k=0; k<2; k++)
{
pstates[k] = nsctrellis.prevState[m+nsctrellis.stateNb*k];//determine previous states
inputs[k] = nsctrellis.input[m+nsctrellis.stateNb*k];//determine input
C[k] = (inputs[k]?apriori_data[n-1]:0);//compute log of gamma
}
for (i=0; i<2; i++)//for each C[i]
{
for (k=0; k<r; k++)
{
for (j=0; j<Nc; j++)
{
C[i] += nsctrellis.output[pstates[i]+inputs[i]*nsctrellis.stateNb+k*nsctrellis.stateNb*2]*(1-2*double(scrambler_pattern[j]))*intrinsic_coded[j+k*Nc+(n-1)*Nc*r];
}
}
}
A[m+n*nsctrellis.stateNb] = std::max(A[pstates[0]+(n-1)*nsctrellis.stateNb]+C[0], A[pstates[1]+(n-1)*nsctrellis.stateNb]+C[1]);
sum = std::max(sum, A[m+n*nsctrellis.stateNb]);
}
//normalization
if (std::isinf(sum))
{
continue;
}
for (m=0; m<nsctrellis.stateNb; m++)
{
A[m+n*nsctrellis.stateNb] -= sum;
}
}
//backward recursion
#pragma omp section
for (n=N-1; n>=0; n--)
{
sum = -INFINITY;//normalisation factor
for (m=0; m<nsctrellis.stateNb; m++) //initial state
{
for (k=0; k<2; k++)
{
nstates[k] = nsctrellis.nextState[m+k*nsctrellis.stateNb];//determine next states
C[k] = (k?apriori_data[n]:0);//compute log of gamma
}
for (i=0; i<2; i++)
{
for (k=0; k<r; k++)
{
for (j=0; j<Nc; j++)
{
C[i] += nsctrellis.output[m+i*nsctrellis.stateNb+k*nsctrellis.stateNb*2]*(1-2*double(scrambler_pattern[j]))*intrinsic_coded[j+k*Nc+n*Nc*r];
}
}
}
B[m+n*nsctrellis.stateNb] = std::max(B[nstates[0]+(n+1)*nsctrellis.stateNb]+C[0], B[nstates[1]+(n+1)*nsctrellis.stateNb]+C[1]);
sum = std::max(sum, B[m+n*nsctrellis.stateNb]);
}
//normalisation
if (std::isinf(sum))
{
continue;
}
for (m=0; m<nsctrellis.stateNb; m++)
{
B[m+n*nsctrellis.stateNb] -= sum;
}
}
}
//compute extrinsic_data
extrinsic_data.set_size(N);
#pragma omp parallel for private(n,k,sum,sumbis)
for (n=1; n<(N+1); n++)
{
sum = -INFINITY;
sumbis = -INFINITY;
for (k=0; k<(nsctrellis.stateNb/2); k++)
{
sum = std::max(sum, A[k+(nsctrellis.stateNb/2)+n*nsctrellis.stateNb]+B[k+(nsctrellis.stateNb/2)+n*nsctrellis.stateNb]);//nominator
sumbis = std::max(sumbis, A[k+n*nsctrellis.stateNb]+B[k+n*nsctrellis.stateNb]);//denominator
}
extrinsic_data[n-1] = (sum-sumbis)-apriori_data[n-1];
}
//compute extrinsic_coded
double *sum0 = new double[r];
double *sum1 = new double[r];
extrinsic_coded.set_size(N*Nc*r);
for (n=0; n<N; n++)
{
for (k=0; k<r; k++)
{
sum0[k] = -INFINITY;
sum1[k] = -INFINITY;
}
for (mp=0; mp<nsctrellis.stateNb; mp++) //initial state
{
for (i=0; i<2; i++)
{
m = nsctrellis.nextState[mp+i*nsctrellis.stateNb];//final state
//compute log of sigma
index = (m>=(nsctrellis.stateNb/2));//0 if input is 0, 1 if input is 1
C[0] = (index?apriori_data[n]:0);
for (k=0; k<r; k++)
{
for (j=0; j<Nc; j++)
{
C[0] += nsctrellis.output[mp+index*nsctrellis.stateNb+k*nsctrellis.stateNb*2]*(1-2*double(scrambler_pattern[j]))*intrinsic_coded[j+k*Nc+n*Nc*r];
}
}
C[1] = A[mp+n*nsctrellis.stateNb]+C[0]+B[m+(n+1)*nsctrellis.stateNb];//this is only a buffer
//compute sums
for (k=0; k<r; k++)
{
if (nsctrellis.output[mp+index*nsctrellis.stateNb+k*nsctrellis.stateNb*2])
{
sum1[k] = std::max(sum1[k], C[1]);
}
else
{
sum0[k] = std::max(sum0[k], C[1]);
}
}
}
}
for (k=0; k<r; k++)
{
for (j=0; j<Nc; j++)
{
index = j+k*Nc+n*r*Nc;
extrinsic_coded[index] = (1-2*double(scrambler_pattern[j]))*(sum1[k]-sum0[k])-intrinsic_coded[index];
}
}
}
//free memory
delete[] nsctrellis.output;
delete[] nsctrellis.nextState;
delete[] nsctrellis.prevState;
delete[] nsctrellis.input;
delete[] A;
delete[] B;
delete[] sum0;
delete[] sum1;
}
}//end namespace tr
itpp::Array::length
int length() const
Returns the number of data elements in the array object.
Definition array.h:157
itpp::bin
Binary arithmetic (boolean) class.
Definition binary.h:57
itpp::log_add
double log_add(double log_a, double log_b)
Safe substitute for log(exp(log_a) + exp(log_b))
Definition log_exp.cpp:40
itpp::sum
T sum(const Vec< T > &v)
Sum of all elements in the vector.
Definition matfunc.h:59
itcompat.h
IT++ compatibility types and functions.
itpp
itpp namespace
Definition itmex.h:37
itpp::bin2dec
ITPP_EXPORT int bin2dec(const bvec &inbvec, bool msb_first=true)
Convert a bvec to decimal int with the first bit as MSB if msb_first == true.
itpp::dec2bin
ITPP_EXPORT bvec dec2bin(int length, int index)
Convert a decimal int index to bvec using length bits in the representation.
siso.h
Definitions for Soft Input Soft Output (SISO) modules class.

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