Design of MDP Convolutional Codes and Maximally Recoverable Codes Through the Lens of Matrix Completion

TL;DR

This paper explores the design of advanced coding schemes, specifically MDP convolutional codes and maximally recoverable codes, using matrix completion techniques to unify and enhance their construction.

Contribution

It introduces general construction methods for these codes emphasizing sparsity and subfield entry properties, advancing coding theory design strategies.

Findings

Provided new constructions for MDP convolutional codes and MR codes.

Demonstrated the effectiveness of matrix completion in code design.

Achieved codes with sparse generator matrices and limited subfield entries.

Abstract

The matrix completion problem provides a unifying lens through which many fundamental problems in coding theory can be viewed. In this paper, we investigate Locally Recoverable Codes (LRCs) with Maximal Recoverability (MR) and Maximum Distance Profile (MDP) convolutional codes in the framework of matrix completion. In particular, we present techniques that are general enough to provide constructions for both types of codes. A common feature of our code constructions is the sparsity of their generator matrices and the property that a large number of the entries of the generator matrices are elements of a small subfield of a larger extension field.