A Neural Network-aided Low Complexity Chase Decoder for URLLC
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915415879319552 |
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| author | Testi, Enrico Paolini, Enrico |
| author_facet | Testi, Enrico Paolini, Enrico |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_10513 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Neural Network-aided Low Complexity Chase Decoder for URLLC Testi, Enrico Paolini, Enrico Signal Processing 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. |
| title | A Neural Network-aided Low Complexity Chase Decoder for URLLC |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2506.10513 |