template <class GF_q, class real> bool linear_code_utils<GF_q, real>::is_systematic(
const matrix<GF_q> & genMat)
{
int dim_k = genMat.size().rows();
int loop1 = 0;
bool isSystematic = true;
//easy check - is the diagonal made of 1s
while (isSystematic && (loop1 < dim_k))
{
if (genMat(loop1, loop1) != GF_q(1))
{
isSystematic = false;
}
loop1++;
}
//check that the non-diagonal entries are zero
loop1 = 0;
int loop2 = 1;
while (isSystematic && (loop1 < dim_k))
{
do
{
if ((GF_q(0) != genMat(loop1, loop2)) && (loop1 != loop2))
{
isSystematic = false;
}
loop2++;
} while (isSystematic && (loop2 < dim_k));
loop2 = 0;
loop1++;
}
return isSystematic;
}
template <class GF_q, class real> bool linear_code_utils<GF_q, real>::compute_syndrome(
const matrix<GF_q> & parMat, const array1gfq_t & received_word_hd,
array1gfq_t & syndrome_vec)
{
bool dec_success = true;
int dim_m = parMat.size().rows();
int length_n = parMat.size().cols();
//check that we have compatible lengths
assertalways(received_word_hd.size().length() == length_n);
syndrome_vec.init(dim_m);
GF_q tmp_val = GF_q(0);
for (int rows = 0; rows < dim_m; rows++)
{
tmp_val = GF_q(0);
for (int cols = 0; cols < length_n; cols++)
{
tmp_val += parMat(rows, cols) * received_word_hd(cols);
}
if (tmp_val != GF_q(0))
{
//the syndrome is non-zero
dec_success = false;
}
syndrome_vec(rows) = tmp_val;
}
return dec_success;
}
template <class GF_q, class real> void linear_code_utils<GF_q, real>::encode_cw(
const matrix<GF_q> & mat_G, const array1i_t & source, array1i_t & encoded)
{
#if DEBUG>=2
libbase::trace << std::endl << "encoding";
#endif
//initialise encoded
int length_n = mat_G.size().cols();
int dim_k = mat_G.size().rows();
assertalways(dim_k == source.size().length());
encoded.init(length_n);
for (int i = 0; i < length_n; i++)
{
GF_q val = GF_q(0);
for (int j = 0; j < dim_k; j++)
{
val += GF_q(source(j)) * mat_G(j, i);
}
encoded(i) = val;
}
#if DEBUG>=2
libbase::trace << std::endl << "finished encoding";
#endif
}
//determine the most likely symbol
template <class GF_q, class real> void linear_code_utils<GF_q, real>::get_most_likely_received_word(
const array1dv_t& received_likelihoods, array1d_t & received_word_sd,
array1gfq_t& received_word_hd)
{
//some helper variables
int length_n = received_likelihoods.size();
real mostlikely_sofar = real(0.0);
int indx = 0;
array1d_t tmp_vec;
int num_of_symbs;
received_word_sd.init(length_n);
received_word_hd.init(length_n);
for (int loop_n = 0; loop_n < length_n; loop_n++)
{
mostlikely_sofar = 0;
indx = 0;
tmp_vec = received_likelihoods(loop_n);
num_of_symbs = tmp_vec.size();
for (int loop_q = 0; loop_q < num_of_symbs; loop_q++)
{
if (mostlikely_sofar <= tmp_vec(loop_q))
{
mostlikely_sofar = tmp_vec(loop_q);
indx = loop_q;
}
}
received_word_sd(loop_n) = mostlikely_sofar;
received_word_hd(loop_n) = GF_q(indx);
}
}
template <class GF_q, class real> void linear_code_utils<GF_q, real>::compute_dual_code(
const matrix<GF_q> & orgMat, matrix<GF_q> & dualCodeGenMatrix,
array1i_t & systematic_perm)
{
int length_n = orgMat.size().cols();
int dim_k = orgMat.size().rows();
int dim_m = length_n - dim_k;
matrix<GF_q> refOrgMat;
#if DEBUG>=2
std::cout << "The original matrix is given by:" << std::endl;
orgMat.serialize(std::cout, '\n');
#endif
linear_code_utils::compute_row_dim(orgMat, refOrgMat);
dim_k = refOrgMat.size().rows();
dim_m = length_n - dim_k;
// Now we need to check that the columns are systematic
//if they aren't then we need to perform some column permutation
//otherwise the permutation is simple the identy map
systematic_perm.init(length_n);
for (int loop1 = 0; loop1 < length_n; loop1++)
{
//the identity permutation
systematic_perm(loop1) = loop1;
}
bool needsPermutation =
!(libbase::linear_code_utils<GF_q, real>::is_systematic(refOrgMat));
if (needsPermutation)
{
//matrix needs its columns permuted before it is in systematic form
//find the pivots
int col_pos = 0;
for (int loop1 = 0; loop1 < dim_k; loop1++)
{
while ((GF_q(1)) != refOrgMat(loop1, col_pos))
{
col_pos++;
}
std::swap(systematic_perm(loop1), systematic_perm(col_pos));
col_pos++;
}
#if DEBUG>=2
std::cout << std::endl << "The permutation is given by:" << std::endl;
systematic_perm.serialize(std::cout, ' ');
#endif
if (needsPermutation)
{
//rejig the matrix
matrix<GF_q> tmprefMatrix;
tmprefMatrix.init(dim_k, length_n);
int col_index;
for (int loop1 = 0; loop1 < length_n; loop1++)
{
col_index = systematic_perm(loop1);
tmprefMatrix.insertcol(refOrgMat.extractcol(col_index), loop1);
}
refOrgMat = tmprefMatrix;
}
}
//the matrix should now be in the right format
#if DEBUG>=2
std::cout << "After permuting any columns, the matrix is now given by:" << std::endl;
refOrgMat.serialize(std::cout, '\n');
#endif
//extract the P part of G'=(I_k|P)
matrix<GF_q> tmp_p_mat;
tmp_p_mat.init(dim_k, dim_m);
for (int loop = dim_k; loop < length_n; loop++)
{
tmp_p_mat.insertcol(refOrgMat.extractcol(loop), loop - dim_k);
}
//this will hold -P^t=P^t (as we are in char GF(q)=2)
matrix<GF_q> tmp_p_mat_t;
tmp_p_mat_t.init(dim_m, length_n - dim_m);
//now transpose yourself
tmp_p_mat_t = tmp_p_mat.transpose();
matrix<GF_q> id_m = matrix<GF_q>::eye(dim_m);
//Construct the dual code gen matrix which is of the form
//H=(-P^t|I_m)
dualCodeGenMatrix.init(dim_m, length_n);
//insert the transposed matrix
for (int loop = 0; loop < (length_n - dim_m); loop++)
{
dualCodeGenMatrix.insertcol(tmp_p_mat_t.extractcol(loop), loop);
}
//now add the identity matrix
int counter = 0;
for (int loop = (length_n - dim_m); loop < length_n; loop++)
{
dualCodeGenMatrix.insertcol(id_m.extractcol(counter), loop);
counter++;
}
#if DEBUG>=2
std::cout << "The generator matrix of the permuted dual code is given by:" << std::endl;
dualCodeGenMatrix.serialize(std::cout, '\n');
#endif
//undo any permutation that we did
if (needsPermutation)
{
matrix<GF_q> tmpdualCodeGenMatrix;
tmpdualCodeGenMatrix.init(dim_m, length_n);
int col_index;
for (int loop1 = 0; loop1 < length_n; loop1++)
{
col_index = systematic_perm(loop1);
tmpdualCodeGenMatrix.insertcol(dualCodeGenMatrix.extractcol(loop1),
col_index);
}
dualCodeGenMatrix = tmpdualCodeGenMatrix;
}
#if DEBUG>=2
std::cout
<< "After undoing the permutation, the generator matrix of the dual code is given by:" << std::endl;
dualCodeGenMatrix.serialize(std::cout, '\n');
#endif
#if DEBUG>=2
std::cout
<< "we now multiply the generator matrix by the transpose of the original matrix, ie:" << std::endl;
std::cout << "the transpose of:" << std::endl;
orgMat.serialize(std::cout, '\n');
std::cout << "is:" << std::endl;
matrix<GF_q> trans(orgMat.transpose());
trans.serialize(std::cout, '\n');
matrix<GF_q> zeroTest(dualCodeGenMatrix*trans);
std::cout << "the result is:" << std::endl;
zeroTest.serialize(std::cout, '\n');
assertalways(GF_q(0)==zeroTest.max());
#endif
}
template <class GF_q, class real> void linear_code_utils<GF_q, real>::compute_row_dim(
const matrix<GF_q>& orgMat, matrix<GF_q> & maxRowSpaceMat)
{
int length_n = orgMat.size().cols();
int dim_k = orgMat.size().rows();
//copy original matrix and reduce it to REF, ie G'=(I_k|P)
matrix<GF_q> refOrgMat(orgMat.reduce_to_ref());
#if DEBUG>=2
std::cout << "The REF is given by:" << std::endl;
refOrgMat.serialize(std::cout, '\n');
#endif
//quick check that we at least have the right dimension
//since the matrix is in REF, the last rows of the matrix
//should not be equal to zero. If the last row of the matrix is zero
//then keep dropping it until it isn't. Consequently, the row dimension
//of the generator matrix is reduced by the number of dropped rows.
int loop1 = dim_k;
int loop2;
bool isZero = true;
while (isZero && (loop1 > 0))
{
loop1--;
loop2 = length_n;
while (isZero && (loop2 >= dim_k))
{
loop2--;
if (refOrgMat(loop1, loop2) != (GF_q(0)))
{
isZero = false;
}
}
}
#if DEBUG>=2
std::cout << "We have " << (dim_k - loop1) - 1 << " zero rows." << std::endl;
#endif
//compensate for the fact the we start counting rows from 0
loop1++;
if (loop1 < dim_k)
{
dim_k = loop1;
//the matrix contains zero rows - drop them
maxRowSpaceMat.init(dim_k, length_n);
for (loop2 = 0; loop2 < dim_k; loop2++)
{
maxRowSpaceMat.insertrow(refOrgMat.extractrow(loop2), loop2);
}
#if DEBUG>=2
std::cout << "After dropping zero rows, the REF is given by:" << std::endl;
maxRowSpaceMat.serialize(std::cout, '\n');
#endif
}
else
{
//the original matrix is ok already
maxRowSpaceMat = refOrgMat;
}
}
void sum_prod_alg_trad<GF_q, real>::compute_r_mn(int m, int n,
const array1i_t & tmpN_m)
{
//the number of remaining symbols that can vary
int num_of_var_syms = tmpN_m.size() - 1;
int num_of_elements = GF_q::elements();
//for each check node we need to consider num_of_elements^num_of_var_symbols cases
int num_of_cases = int(pow(num_of_elements, num_of_var_syms));
int pos_n = tmpN_m(n) - 1;//we count from 1;
int bitmask = num_of_elements - 1;
//only use the entries that are variable
array1i_t rel_N_m;
rel_N_m.init(num_of_var_syms);
int indx = 0;
for (int loop = 0; loop < num_of_var_syms; loop++)
{
if (indx == n)
{
indx++;
}
rel_N_m(loop) = tmpN_m(indx);
indx++;
}
//go through all cases - this will use bitwise manipulation
GF_q syndrome_sym = GF_q(0);
GF_q h_m_n_dash;
//GF_q h_m_n;
GF_q tmp_chk_val;
int int_sym_val;
int bits;
int pos_n_dash;
real q_nm_prod = real(1.0);
this->marginal_probs(m, pos_n).r_mxn = 0.0;
GF_q check_value = this->marginal_probs(m, pos_n).val;
for (int loop1 = 0; loop1 < num_of_cases; loop1++)
{
bits = loop1;
syndrome_sym = GF_q(0);
q_nm_prod = 1.0;
for (int loop2 = 0; loop2 < num_of_var_syms; loop2++)
{
pos_n_dash = rel_N_m(loop2) - 1;//we count from zero
//extract int value of the first symbol
int_sym_val = bits & bitmask;
//shift bits to the right by the dimension of the finite field
bits = bits >> GF_q::dimension();
//the parity check symbol at this position
h_m_n_dash = this->marginal_probs(m, pos_n_dash).val;
//compute the value that at this check
tmp_chk_val = h_m_n_dash * GF_q(int_sym_val);
//add it to the syndrome
syndrome_sym = syndrome_sym + tmp_chk_val;
//look up the prob that the chk_val was actually sent
q_nm_prod *= this->marginal_probs(m, pos_n_dash).q_mxn(int_sym_val);
}
//adjust the appropriate rmn value
int_sym_val = syndrome_sym / check_value;
this->marginal_probs(m, pos_n).r_mxn(int_sym_val) += q_nm_prod;
}
}
