Construction of LDPC convolutional codes via difference triangle sets

TL;DR

This paper introduces a new method for constructing LDPC convolutional codes using difference triangle sets, establishing relationships between code parameters and graph properties to improve code performance.

Contribution

It generalizes existing constructions by linking difference triangle sets to LDPC convolutional codes and analyzes conditions to prevent harmful cycles in Tanner graphs.

Findings

Relation between free distance and parameter w

Conditions for Tanner graph cycle avoidance

Lower bounds on field size for cycle prevention

Abstract

In this paper, a construction of $(n,k,\delta)$ LDPC convolutional codes over arbitrary finite fields, which generalizes the work of Robinson and Bernstein and the later work of Tong is provided. The sets of integers forming a $(k,w)$-(weak) difference triangle set are used as supports of some columns of the sliding parity-check matrix of an $(n,k,\delta)$ convolutional code, where $n\in\mathbb{N}$, $n>k$. The parameters of the convolutional code are related to the parameters of the underlying difference triangle set. In particular, a relation between the free distance of the code and $w$ is established as well as a relation between the degree of the code and the scope of the difference triangle set. Moreover, we show that some conditions on the weak difference triangle set ensure that the Tanner graph associated to the sliding parity-check matrix of the convolutional code is free from $2\ell$-cycles not satisfying the full rank condition over any finite field. Finally, we relax these conditions and provide a lower bound on the field size, depending on the parity of $\ell$, that is sufficient to still avoid $2\ell$-cycles. This is important for improving the performance of a code and avoiding the presence of low-weight codewords and absorbing sets.