On the weight distribution of convolutional codes
Heide Gluesing-Luerssen
Abstract
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.
Create a lesson
Related papers
Optimal Communication for Distributed Sparse Histogram Aggregation
Fangwei Ye, Yijun Fan
Hardware-Efficient Reinforcement-Learned Scheduling for Quantum LDPC Decoders
Mohsen Moradi, Vahid Nourozi, Wei Tang et al.
Making Every Edit Count: Efficient Binary Edit Correction at the Gilbert-Varshamov Bound
Ryan Gabrys, Shubhransh Singhvi, Eitan Yaakobi
Timely Ground-Station Status Delivery via UAV-Assisted Data Collection and Hierarchical RL
Xiangjian Gao, Mohammad Moltafet, Hamid R. Sadjadpour
Uniform Sampling of Linear Codes with Prescribed Hull Dimension
Keita Ishizuka
Quantum Private Information Retrieval on Forests
Mohamed Nomeir, Sennur Ulukus