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October 20, 2025Open Access

Neural-Model-Augmented Hybrid NMS-OSD Decoders for Near-ML in Short Block Codes

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The proposed hybrid decoder achieves near-maximum likelihood performance in decoding short block codes, enhancing error correction efficiency.
Extensive simulations show that this architecture outperforms existing methods in frame error rates while optimizing throughput and complexity.
Key innovations include a convolutional neural network model for refining bit reliability estimates and an adaptive decoding path for improved accuracy.
The implementation includes an undetected error detector ensuring that erroneous outcomes are corrected through ordered statistics decoding.

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This approach combines a min-sum decoder and reinforced statistics decoding for short block codes, indicating improved performance.

Li et al. (2025) studied this question.

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