A subspace based method for error correction with DFT codes
A subspace based method for real error correction with low-pass DFT codes is proposed. The common roots of the eigen-polynomials of the syndrome covariance matrix associated with the zero eigenvalue gives the error locations. When the codevectors are quantized, the localization algorithm is modified so as to minimize the quantization noise effects. The proposed method is analogous to the subspace based spectral estimation techniques in array signal processing, but adapted to discrete error locations and only one set of syndrome coefficients. By combining the analytical results for perfect error localization with the subspace based approach, we obtain superior results than the existing approach.