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Heide Gluesing-Luerssen

Publications and source records attributed to Heide Gluesing-Luerssen.

48 records · Page 3Linked to original sources
arXiv2009

On Isometries for Convolutional Codes

In this paper we will discuss isometries and strong isometries for convolutional codes. Isometries are weight-preserving module isomorphisms whereas strong isometries are, in addition, degree-preserving. Special cases of these maps are certain types of monomial transformations. We will show a form of MacWilliams Equivalence Theorem, that is, each isometry between convolutional codes is given by a monomial transformation. Examples show that strong isometries cannot be characterized this way, but special attention paid to the weight adjacency matrices allows for further descriptions. Various distance parameters appearing in the literature on convolutional codes will be discussed as well.

cs.IT↗
arXiv2008

A MacWilliams Identity for Convolutional Codes: The General Case

A MacWilliams Identity for convolutional codes will be established. It makes use of the weight adjacency matrices of the code and its dual, based on state space realizations (the controller canonical form) of the codes in question. The MacWilliams Identity applies to various notions of duality appearing in the literature on convolutional coding theory.

cs.IT↗
arXiv2007

A Matrix Ring Description for Cyclic Convolutional Codes

In this paper, we study convolutional codes with a specific cyclic structure. By definition, these codes are left ideals in a certain skew polynomial ring. Using that the skew polynomial ring is isomorphic to a matrix ring we can describe the algebraic parameters of the codes in a more accessible way. We show that the existence of such codes with given algebraic parameters can be reduced to the solvability of a modified rook problem. It is our strong belief that the rook problem is always solvable, and we present solutions in particular cases.

cs.IT↗
arXiv2006

State Space Realizations and Monomial Equivalence for Convolutional Codes

We will study convolutional codes with the help of state space realizations. It will be shown that two such minimal realizations belong to the same code if and only if they are equivalent under the full state feedback group. This result will be used in order to prove that two codes with positive Forney indices are monomially equivalent if and only if they share the same adjacency matrix. The adjacency matrix counts in a detailed way the weights of all possible outputs and thus contains full information about the weights of the codewords in the given code.

cs.IT↗
arXiv2006

On the MacWilliams Identity for Convolutional Codes

The adjacency matrix associated with a convolutional code collects in a detailed manner information about the weight distribution of the code. A MacWilliams Identity Conjecture, stating that the adjacency matrix of a code fully determines the adjacency matrix of the dual code, will be formulated, and an explicit formula for the transformation will be stated. The formula involves the MacWilliams matrix known from complete weight enumerators of block codes. The conjecture will be proven for the class of convolutional codes where either the code itself or its dual does not have Forney indices bigger than one. For the general case the conjecture is backed up by many examples, and a weaker version will be established.

cs.IT↗
arXiv2005

On the weight distribution of convolutional codes

Detailed information about the weight distribution of a convolutional code is given by the adjacency matrix of the state diagram associated with a controller canonical form of the code. We will show that this matrix is an invariant of the code. Moreover, it will be proven that codes with the same adjacency matrix have the same dimension and the same Forney indices and finally that for one-dimensional binary convolutional codes the adjacency matrix determines the code uniquely up to monomial equivalence.

cs.IT↗
arXiv2004

A class of one-dimensional MDS convolutional codes

A class of one-dimensional convolutional codes will be presented. They are all MDS codes, i. e., have the largest distance among all one-dimensional codes of the same length n and overall constraint length delta. Furthermore, their extended row distances are computed, and they increase with slope n-delta. In certain cases of the algebraic parameters, we will also derive parity check matrices of Vandermonde type for these codes. Finally, cyclicity in the convolutional sense will be discussed for our class of codes. It will turn out that they are cyclic if and only if the field element used in the generator matrix has order n. This can be regarded as a generalization of the block code case.

cs.IT↗
arXiv2004

On doubly-cyclic convolutional codes

Cyclicity of a convolutional code (CC) is relying on a nontrivial automorphism of the algebra F[x]/(x^n-1), where F is a finite field. If this automorphism itself has certain specific cyclicity properties one is lead to the class of doubly-cyclic CC's. Within this large class Reed-Solomon and BCH convolutional codes can be defined. After constructing doubly-cyclic CC's, basic properties are derived on the basis of which distance properties of Reed-Solomon convolutional codes are investigated.This shows that some of them are optimal or near optimal with respect to distance and performance.

math.RA↗
arXiv2003

On the Parameters of Convolutional Codes with Cyclic Structure

In this paper convolutional codes with cyclic structure will be investigated. These codes can be understood as left principal ideals in a suitable skew-polynomial ring. It has been shown in [3] that only certain combinations of the parameters (field size, length, dimension, and Forney indices) can occur for cyclic codes. We will investigate whether all these combinations can indeed be realized by a suitable cyclic code and, if so, how to construct such a code. A complete characterization and construction will be given for minimal cyclic codes. It is derived from a detailed investigation of the units in the skew-polynomial ring.

math.RA↗
arXiv2003

Distance bounds for convolutional codes and some optimal codes

After a discussion of the Griesmer and Heller bound for the distance of a convolutional code we present several codes with various parameters, over various fields, and meeting the given distance bounds. Moreover, the Griesmer bound is used for deriving a lower bound for the field size of an MDS convolutional code and examples are presented showing that, in most cases, the lower bound is tight. Most of the examples in this paper are cyclic convolutional codes in a generalized sense as it has been introduced in the seventies. A brief introduction to this promising type of cyclicity is given at the end of the paper in order to make the examples more transparent.

math.RA↗
arXiv2003

Strongly MDS Convolutional Codes

MDS convolutional codes have the property that their free distance is maximal among all codes of the same rate and the same degree. In this paper we introduce a class of MDS convolutional codes whose column distances reach the generalized Singleton bound at the earliest possible instant. We call these codes strongly MDS convolutional codes. It is shown that these codes can decode a maximum number of errors per time interval when compared with other convolutional codes of the same rate and degree. These codes have also a maximum or near maximum distance profile. A code has a maximum distance profile if and only if the dual code has this property.

math.RA↗