A convolutional code can be decomposed into smaller codes if it admits decoupled encoders. In this paper, we show that if a code can be decomposed into smaller codes (subcodes) its column distances are the minimum of the column distances of its subcodes. Moreover, the j-th column distance of a convolutional code C is equal to the j-th column distance of the convolutional codes generated by the truncation of the canonical encoders of C to matrices which entries have degree smaller or equal than j. We show that if one of such codes can be decomposed into smaller codes, so can be all the other codes
Abstract—A new module structure for convolutional codes is in-troduced and used to establish further...
Fundamentals of Convolutional Coding, Second Edition, regarded as a bible of convolutional coding br...
Several structural and distance properties of the class of codes are derived and utilized in develop...
A convolutional code can be decomposed into smaller codes if it admits decoupled encoders. In this p...
The minimum distance of a code is an important measure of robustness of the code since it provides a...
Nested convolutional codes are a set of convolutional codes that is derived from a given generator m...
AbstractIn this paper, we characterize four models of concatenation of a block code and a convolutio...
In this paper we present the analytical results of the computational requirement for the minimum-dis...
AbstractIn this paper, polynomial matrix fraction descriptions (MFDs) are used as a tool for investi...
AbstractDetailed information about the weight distribution of a convolutional code is given by the a...
Convolutional codes are characterized by a trellis structure. Maximum-likelihood decoding is charact...
AbstractThis article focuses on the characterization of two models of concatenated convolutional cod...
Many communication systems obtain enhanced performance by using concatenated coding schemes. Turbo c...
Convolutional codes are essential in a wide range of practical applications due to their efficient n...
We discuss about construction of convolutional codes via linear system approach. The main discussion...
Abstract—A new module structure for convolutional codes is in-troduced and used to establish further...
Fundamentals of Convolutional Coding, Second Edition, regarded as a bible of convolutional coding br...
Several structural and distance properties of the class of codes are derived and utilized in develop...
A convolutional code can be decomposed into smaller codes if it admits decoupled encoders. In this p...
The minimum distance of a code is an important measure of robustness of the code since it provides a...
Nested convolutional codes are a set of convolutional codes that is derived from a given generator m...
AbstractIn this paper, we characterize four models of concatenation of a block code and a convolutio...
In this paper we present the analytical results of the computational requirement for the minimum-dis...
AbstractIn this paper, polynomial matrix fraction descriptions (MFDs) are used as a tool for investi...
AbstractDetailed information about the weight distribution of a convolutional code is given by the a...
Convolutional codes are characterized by a trellis structure. Maximum-likelihood decoding is charact...
AbstractThis article focuses on the characterization of two models of concatenated convolutional cod...
Many communication systems obtain enhanced performance by using concatenated coding schemes. Turbo c...
Convolutional codes are essential in a wide range of practical applications due to their efficient n...
We discuss about construction of convolutional codes via linear system approach. The main discussion...
Abstract—A new module structure for convolutional codes is in-troduced and used to establish further...
Fundamentals of Convolutional Coding, Second Edition, regarded as a bible of convolutional coding br...
Several structural and distance properties of the class of codes are derived and utilized in develop...