On the weight distribution of convolutional codes

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


Detailed information about the weight distribution of a convolutional code is given by the adjacency matrix of the state diagram associated with a minimal realization 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.

Original languageEnglish
Pages (from-to)298-326
Number of pages29
JournalLinear Algebra and Its Applications
Issue number1-3
StatePublished - Oct 1 2005


  • Controller canonical form
  • Convolutional coding theory
  • MacWilliams duality
  • Monomial equivalence
  • Weight distribution

ASJC Scopus subject areas

  • Algebra and Number Theory
  • Numerical Analysis
  • Geometry and Topology
  • Discrete Mathematics and Combinatorics


Dive into the research topics of 'On the weight distribution of convolutional codes'. Together they form a unique fingerprint.

Cite this