Receding horizon decoding of convolutional codes

TL;DR

This paper introduces a novel iterative decoding method for convolutional codes inspired by model predictive control, offering a computationally cheaper alternative to classical algorithms with strong error correction, especially for cyclic convolutional codes.

Contribution

It presents a new decoding algorithm based on receding horizon principles, improving efficiency over traditional methods like Viterbi decoding.

Findings

Cheaper to implement than classical algorithms

Effective for decoding cyclic convolutional codes

Offers significant error correction capabilities

Abstract

Decoding of convolutional codes poses a significant challenge for coding theory. Classical methods, based on e.g. Viterbi decoding, suffer from being computationally expensive and are restricted therefore to codes of small complexity. Based on analogies with model predictive optimal control, we propose a new iterative method for convolutional decoding that is cheaper to implement than established algorithms, while still offering significant error correction capabilities. The algorithm is particularly well-suited for decoding special types of convolutional codes, such as e.g. cyclic convolutional codes.