Complete MDP convolutional codes

TL;DR

This paper introduces complete MDP convolutional codes, demonstrating their existence, genericity, and providing two construction methods that work for all parameters given a sufficiently large base field.

Contribution

It proves the existence and genericity of complete MDP convolutional codes and offers two new construction techniques applicable to all parameters with large enough fields.

Findings

Complete MDP convolutional codes exist for all parameters.

Two construction methods for complete MDP codes are proposed.

These codes improve decoding efficiency and recovery over erasure channels.

Abstract

Maximum distance profile (MDP) convolutional codes have the property that their column distances are as large as possible. It has been shown that, transmitting over an erasure channel, these codes have optimal recovery rate for windows of a certain length. Reverse MDP convolutional codes have the additional advantage that they are suitable for forward and backward decoding algorithms. Beyond that the subclass of complete MDP convolutional codes has the ability to reduce the waiting time during decoding. The first main result of this paper is to show the existence and genericity of complete MDP convolutional codes for all code parameters. The second main contribution is the presentation of two concrete construction techniques to obtain complete MDP convolutional codes. These constructions work for all code parameters but require that the size of the underlying base field is (sufficiently) large.