Logo image
On superregular matrices and MDP convolutional codes
Journal article   Open access   Peer reviewed

On superregular matrices and MDP convolutional codes

Ryan Hutchinson, Roxana Smarandache and Jochen Trumpf
Linear algebra and its applications, Vol.428(11-12), pp.2585-2596
01/06/2008

Abstract

Column distances Convolutional codes Maximum distance profile Partial realization problem Superregular matrices
Superregular matrices are a type of lower triangular Toeplitz matrix that arises in the context of constructing convolutional codes having a maximum distance profile. These matrices are characterized by the property that the only submatrices having a zero determinant are those whose determinants are trivially zero due to the lower triangular structure. In this paper, we discuss how superregular matrices may be used to construct codes having a maximum distance profile. We also present an upper bound on the minimum size a finite field must have in order that a superregular matrix of a given size can exist over that field. This, in turn, gives an upper bound on the smallest field size over which an MDP (n,k,δ) convolutional code can exist.
url
https://doi.org/10.1016/j.laa.2008.02.011View
Published (Version of record) Open

Metrics

Details

Logo image