English

An Algebraic-Combinatorial Proof Technique for the GM-MDS Conjecture

Information Theory 2017-05-15 v2 math.IT

Abstract

This paper considers the problem of designing maximum distance separable (MDS) codes over small fields with constraints on the support of their generator matrices. For any given m×nm\times n binary matrix MM, the GM-MDS conjecture, due to Dau et al., states that if MM satisfies the so-called MDS condition, then for any field F\mathbb{F} of size qn+m1q\geq n+m-1, there exists an [n,m]q[n,m]_q MDS code whose generator matrix GG, with entries in F\mathbb{F}, fits MM (i.e., MM is the support matrix of GG). Despite all the attempts by the coding theory community, this conjecture remains still open in general. It was shown, independently by Yan et al. and Dau et al., that the GM-MDS conjecture holds if the following conjecture, referred to as the TM-MDS conjecture, holds: if MM satisfies the MDS condition, then the determinant of a transformation matrix TT, such that TVTV fits MM, is not identically zero, where VV is a Vandermonde matrix with distinct parameters. In this work, we generalize the TM-MDS conjecture, and present an algebraic-combinatorial approach based on polynomial-degree reduction for proving this conjecture. Our proof technique's strength is based primarily on reducing inherent combinatorics in the proof. We demonstrate the strength of our technique by proving the TM-MDS conjecture for the cases where the number of rows (mm) of MM is upper bounded by 55. For this class of special cases of MM where the only additional constraint is on mm, only cases with m4m\leq 4 were previously proven theoretically, and the previously used proof techniques are not applicable to cases with m>4m > 4.

Keywords

Cite

@article{arxiv.1702.01734,
  title  = {An Algebraic-Combinatorial Proof Technique for the GM-MDS Conjecture},
  author = {Anoosheh Heidarzadeh and Alex Sprintson},
  journal= {arXiv preprint arXiv:1702.01734},
  year   = {2017}
}

Comments

Accepted for publication in Proc. ISIT 2017

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