Log Density Spectrum Based Decoding of Polar Codes for Channels with Strong Phase Noise
Abstract
A decoding method is proposed for polar codes under channels with strong phase noise. In contrast to traditional SC decoding, where recursions are performed on scalar soft information such as probabilities, the proposed decoding recurses over probability density functions defined on the phase-noise variable. To make the density-level recursion computationally tractable, we represent each log-density by its spectrum, and derive approximate formulas for the spectrum recursion, therefore, the proposed method is termed log-density spectrum recursion (LDSR). Numerical results are given under strong phase noise and AWGN, showing that a prominent superiority of error performance is achieved by LDSR in comparison with traditional SC and Kalman-based methods.
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BibTeX
@misc{mei2026density,
title = {{Log Density Spectrum Based Decoding of Polar Codes for Channels with Strong Phase Noise}},
author = {Yinuo Mei and Daiming Qu},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.16363},
eprint = {2610.16363},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.16363},
}
RIS
TY - UNPB
TI - Log Density Spectrum Based Decoding of Polar Codes for Channels with Strong Phase Noise
AU - Yinuo Mei
AU - Daiming Qu
PY - 2026
DA - 2026/10/29
PB - arXiv
DO - 10.48550/arxiv.2610.16363
UR - https://arxiv.org/abs/2610.16363
AB - A decoding method is proposed for polar codes under channels with strong phase noise. In contrast to traditional SC decoding, where recursions are performed on scalar soft information such as probabilities, the proposed decoding recurses over probability density functions defined on the phase-noise variable. To make the density-level recursion computationally tractable, we represent each log-density by its spectrum, and derive approximate formulas for the spectrum recursion, therefore, the proposed method is termed log-density spectrum recursion (LDSR). Numerical results are given under strong phase noise and AWGN, showing that a prominent superiority of error performance is achieved by LDSR in comparison with traditional SC and Kalman-based methods.
ER -