On the construction of jointly superregular lower triangular Toeplitz matrices

Abstract

Abstract-Superregular matrices have the property that all of their submatrices, which can be full rank are so. Lower triangular superregular matrices are useful for e.g., maximum distance separable convolutional codes as well as for (sequential) network codes. In this work, we provide an explicit design for all superregular lower triangular Toeplitz matrices in GF(2 p ) for the case of matrices with dimensions less than or equal to 5 × 5. For higher dimensional matrices, we present a greedy algorithm that finds a solution provided the field size is sufficiently high. We also introduce the notions of jointly superregular and product preserving jointly superregular matrices, and extend our explicit constructions of superregular matrices to these cases. Jointly superregular matrices are necessary to achieve optimal decoding capabilities for the case of codes with a rate lower than 1 /2, and the product preserving property is necessary for optimal decoding capabilities in network recoding.

Block Toeplitz matrices for burst-correcting convolutional codes

“…Currently, there are only two general construction of superregular matrices and both require huge finite fields [1,2,8]. Some computer search algorithms to seek superregular matrices have been recently proposed [6,11,12]. However, many problems in the area remain open, as for instance, the minimum field size needed for the existence of a superregular matrix of a given size [14], although some conjectures have been proposed in the literature, see for instance [8].…”