Soft information perturbation boosts polar code decoding accuracy
Efficient Perturbation-Based SC Decoding for Long Polar Codes
Information Theory
Summary
Polar codes are used to detect and fix errors in digital communication, but decoding them accurately and quickly can be hard. The authors show that tweaking the input soft information in a specific way helps catch early mistakes made during decoding. This correction improves overall performance without making the process slower or less reliable. They also develop a new hybrid decoding method that mixes random and targeted tweaks to better fix errors, achieving performance close to more complex methods but with less computational effort.
What this means in practice
- •For communication system engineers: Implement faster and more efficient decoding in long polar-coded communication systems by using optimized soft information perturbations to correct early decoding errors.
- •For wireless hardware developers: Design decoding modules that achieve near list decoding accuracy without the complexity of path sorting or list expansion, lowering hardware demands in 5G and beyond devices.
Authors
Zhongjun Yang, Zhichen Liu, Li Chen, Xianbin Wang, Huazi Zhang
Abstract
Soft information perturbations can significantly improve the successive cancellation (SC) decoding performance of polar codes. This is an effective method for realizing low-latency yet high performance for long polar codes, since each perturbation-based SC (PSC) decoding can be performed in parallel. However, little theoretical insight has been revealed so far regarding its work rationale. This paper provides the theoretical analysis of the soft information perturbation. It reveals that PSC decoding improves the SC decoding performance through correcting the erroneous bit estimations, especially the first erroneous bit (FEB). It further validates that perturbing the soft information does not degrade the error-correction capability of SC decoding. Such revelations lead to the design of optimal perturbations that maximizes the FEB correction probability, resulting in an enhanced PSC decoding performance. Based on this, a novel hybrid PSC (HPSC) decoding is proposed. By introducing the random and biased perturbations, the HPSC decoding can better divert from the initial erroneous estimation and converge to the intended one. Our simulation results demonstrate that with the optimal perturbation, the HPSC decoding can achieve a similar performance as the cyclic redundancy check (CRC) aided SC list (CA-SCL) decoding, while avoiding path sorting and expansion.