Tensor Codes and their Invariants

12/15/2021
by   Eimear Byrne, et al.
0

In 1991, Roth introduced a natural generalization of rank metric codes, namely tensor codes. The latter are defined to be subspaces of r-tensors where the ambient space is endowed with the tensor rank as a distance function. In this work, we describe the general class of tensor codes and we study their invariants that correspond to different families of anticodes. In our context, an anticode is a perfect space that has some additional properties. A perfect space is one that is spanned by tensors of rank 1. Our use of the anticode concept is motivated by an interest in capturing structural properties of tensor codes. In particular, we indentify four different classes of tensor anticodes and show how these gives different information on the codes they describe. We also define the generalized tensor binomial moments and the generalized tensor weight distribution of a code and establish a bijection between these invariants. We use the generalized tensor binomial moments to define the concept of an i-tensor BMD code, which is an extremal code in relation to an inequality arising from them. Finally, we give MacWilliams identities for generalized tensor binomial moments.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
12/12/2022

Zeta Functions for Tensor Codes

In this work we introduce a new class of optimal tensor codes related to...
research
12/04/2019

Rank-Metric Codes, Generalized Binomial Moments and their Zeta Functions

In this paper we introduce a new class of extremal codes, namely the i-B...
research
01/20/2022

Non-minimum tensor rank Gabidulin codes

The tensor rank of some Gabidulin codes of small dimension is investigat...
research
04/10/2019

Tensor Representation of Rank-Metric Codes

We present the theory of rank-metric codes with respect to the 3-tensors...
research
12/23/2022

Generalized column distances

We define a notion of r-generalized column distances for the j-truncatio...
research
05/21/2020

Matrix moments of the diffusion tensor distribution

Purpose: To facilitate the implementation/validation of signal represent...
research
11/29/2019

Equivalence and Characterizations of Linear Rank-Metric Codes Based on Invariants

We show that the sequence of dimensions of the linear spaces, generated ...

Please sign up or login with your details

Forgot password? Click here to reset