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arXiv · 2506.10513

A Neural Network-aided Low Complexity Chase Decoder for URLLC

Enrico Testi·Enrico Paolini
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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.

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Enrico Testi, Enrico Paolini. 2025-06-12. A Neural Network-aided Low Complexity Chase Decoder for URLLC. https://arxiv.org/abs/2506.10513

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