Efficient SC-Flip Decoding for Polar Codes through Decision-Boundary Normalization
Abstract
Accurate first erroneous position (FEP) identification is key to improving the error-correction performance of successive cancellation flip (SCF) decoding. However, FEP identification remains challenging with a limited number of re-decoding attempts. To address this issue, we propose decision-boundary normalization (DBN) to improve FEP identification and develop a novel DBN-SCF decoding algorithm. DBN normalizes the magnitude of each decision log-likelihood ratio (LLR) by the standard deviation predicted by the Gaussian approximation (GA) for the corresponding bit-channel. This allows a single normalized threshold to be shared across bit-channels within each code configuration, giving it a consistent statistical meaning and avoiding per-channel calibration. DBN-SCF decoding selects candidate flip positions from the information set in the natural successive cancellation (SC) decoding order using a threshold, without sorting. Moreover, DBN-SCF decoding achieves better error-correction performance with a limited number of re-decoding attempts. It also requires lower average decoding complexity than existing SCF decoding methods at a comparable level of error-correction performance.
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BibTeX
@misc{yiming2026efficient,
title = {{Efficient SC-Flip Decoding for Polar Codes through Decision-Boundary Normalization}},
author = {Gulinisha Yiming and Zhongjun Yang and Zhicheng Liu and Zhiming Ma and Xumin Ni and Yuting Liu},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.10846},
eprint = {2611.10846},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.10846},
}
RIS
TY - UNPB
TI - Efficient SC-Flip Decoding for Polar Codes through Decision-Boundary Normalization
AU - Gulinisha Yiming
AU - Zhongjun Yang
AU - Zhicheng Liu
AU - Zhiming Ma
AU - Xumin Ni
AU - Yuting Liu
PY - 2026
DA - 2026/11/07
PB - arXiv
DO - 10.48550/arxiv.2611.10846
UR - https://arxiv.org/abs/2611.10846
AB - Accurate first erroneous position (FEP) identification is key to improving the error-correction performance of successive cancellation flip (SCF) decoding. However, FEP identification remains challenging with a limited number of re-decoding attempts. To address this issue, we propose decision-boundary normalization (DBN) to improve FEP identification and develop a novel DBN-SCF decoding algorithm. DBN normalizes the magnitude of each decision log-likelihood ratio (LLR) by the standard deviation predicted by the Gaussian approximation (GA) for the corresponding bit-channel. This allows a single normalized threshold to be shared across bit-channels within each code configuration, giving it a consistent statistical meaning and avoiding per-channel calibration. DBN-SCF decoding selects candidate flip positions from the information set in the natural successive cancellation (SC) decoding order using a threshold, without sorting. Moreover, DBN-SCF decoding achieves better error-correction performance with a limited number of re-decoding attempts. It also requires lower average decoding complexity than existing SCF decoding methods at a comparable level of error-correction performance.
ER -