Polar code decoding improves error rates on noisy phase channels
Log Density Spectrum Based Decoding of Polar Codes for Channels with Strong Phase Noise
Information Theory
Summary
Some communication channels suffer from strong phase noise, which makes it hard for traditional decoders to correctly interpret received signals. The authors propose a new decoding method that works with distributions of phase noise rather than simple probabilities, capturing more detailed information. To keep computations manageable, they represent log-density functions using spectra and derive formulas for updating these spectra during decoding. Their method shows better error performance than standard decoders and Kalman-based approaches under strong phase noise conditions.
What this means in practice
- •For wireless communication engineers: Implement improved decoding algorithms for polar-coded transmissions over channels affected by strong phase noise to reduce error rates.
- •For satellite communication teams: Enhance decoding performance of polar codes for satellite links suffering from phase distortions and noise.
Authors
Yinuo Mei, Daiming Qu
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.