Near-Optimal Generalized Decoding of Polar-like Codes
<|reference_start|>Near-Optimal Generalized Decoding of Polar-like Codes: We present a framework that can exploit the tradeoff between the undetected error rate (UER) and block error rate (BLER) of polar-like codes. It is compatible with all successive cancellation (SC)-based decoding methods and relies on a novel approximation that we call codebook probability. This approximation is based on an auxiliary distribution that mimics the dynamics of decoding algorithms following an SC decoding schedule. Simulation results demonstrates that, in the case of SC list (SCL) decoding, the proposed framework outperforms the state-of-art approximations from Forney's generalized decoding rule for polar-like codes with dynamic frozen bits. In addition, dynamic Reed-Muller (RM) codes using the proposed generalized decoding significantly outperform CRC-concatenated polar codes decoded using SCL in both BLER and UER. Finally, we briefly discuss three potential applications of the approximated codebook probability: coded pilot-free channel estimation; bitwise soft-output decoding; and improved turbo product decoding.<|reference_end|>
@article{yuan2024near-optimal,
    title={Near-Optimal Generalized Decoding of Polar-like Codes},
    author={Peihong Yuan, Ken R. Duffy, Muriel M'edard},
    journal={arXiv preprint arXiv:2402.05004},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.05004},
    primaryClass={cs.IT math.IT}
}