A Neural Network-aided Low Complexity Chase Decoder for URLLC

TL;DR

This paper introduces a neural network-assisted Chase-II decoding algorithm that significantly reduces complexity while maintaining near-optimal performance, tailored for ultra-reliable low-latency communications (URLLC) systems.

Contribution

It presents a novel neural network-enhanced Chase-II decoding method that predicts effective perturbations, bridging the gap between high-performance ML decoding and practical low-complexity requirements.

Findings

Reduces decoding trials by predicting promising perturbations.

Achieves near-ML decoding performance with lower complexity.

Suitable for URLLC's stringent latency and reliability needs.

Abstract

Ultra-reliable low-latency communications (URLLC) demand decoding algorithms that simultaneously offer high reliability and low complexity under stringent latency constraints. While iterative decoding schemes for LDPC and Polar codes offer a good compromise between performance and complexity, they fall short in approaching the theoretical performance limits in the typical URLLC short block length regime. Conversely, quasi-ML decoding schemes for algebraic codes, like Chase-II decoding, exhibit a smaller gap to optimum decoding but are computationally prohibitive for practical deployment in URLLC systems. To bridge this gap, we propose an enhanced Chase-II decoding algorithm that leverages a neural network (NN) to predict promising perturbation patterns, drastically reducing the number of required decoding trials. The proposed approach combines the reliability of quasi-ML decoding with the efficiency of NN inference, making it well-suited for time-sensitive and resource-constrained applications.