Design of Low-Complexity Convolutional Codes over GF(q)

TL;DR

This paper introduces a new family of low-complexity recursive systematic convolutional codes over GF(q) for non-binary turbo codes, demonstrating improved performance over existing codes and binary counterparts with various modulations.

Contribution

It presents a general framework for designing optimal non-binary convolutional codes over GF(q), enhancing performance in turbo coding applications.

Findings

Codes outperform existing non-binary convolutional codes.

Codes outperform binary counterparts with QAM or lower order modulations.

Framework enables systematic code design over various GF(q).

Abstract

This paper proposes a new family of recursive systematic convolutional codes, defined in the non-binary domain over different Galois fields GF(q) and intended to be used as component codes for the design of non-binary turbo codes. A general framework for the design of the best codes over different GF(q) is described. The designed codes offer better performance than the non-binary convolutional codes found in the literature. They also outperform their binary counterparts when combined with their corresponding QAM modulation or with lower order modulations.