Discrete Applied Mathematics - Special volume on applied algebra, algebraic algorithms, and error-correcting codes
nD Polynomial Matrices with Applications to MultidimensionalSignal Analysis
Multidimensional Systems and Signal Processing
Gröbner Bases and Systems Theory
Multidimensional Systems and Signal Processing
Gröbner bases for problem solving in multidimensional systems
Multidimensional Systems and Signal Processing
Multidimensional Convolutional Codes
SIAM Journal on Discrete Mathematics
IEEE Transactions on Information Theory - Part 1
On behaviors and convolutional codes
IEEE Transactions on Information Theory - Part 1
Constructions of MDS-convolutional codes
IEEE Transactions on Information Theory
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A representation of an multidimensional (m-D) convolutional encoder is analogous to the representation of a transfer function for a MIMO m-D FIR system. The encoder matrix is usually not square and thus finding its inverse (decoder matrix) typically employs the Moore-Penrose generalized inverse. However, the result may not be FIR (polynomial matrix) even if the generator matrix is a polynomial matrix. In this paper a constructive algorithm for computing the FIR pseudo inverse, based on the usage of Gröbner basis is presented along with detailed examples. The result obtained can be parameterized to cover the class of all possible FIR inverses. In addition, by using the computation method of syzygy with the Gröbner basis module, the syndrome matrix for a given m-D convolutional encoder is shown. Furthermore, the theory of Gröbner basis is applied to solve the algebraic syndrome decoder problems using the maximum likelihood (nearest neighborhood) criteria and the procedure for 2-D convolutional code error correction is proposed. Despite the complication of the decoding process, the proposed method is the only error correcting decoder for multidimensional convolutional code available to date.