Node-Based Soft-Output Fast Successive Cancellation List Decoding of Polar Codes

2026-04-19

AI Summary

This work addresses the high latency of conventional soft-output successive cancellation list (SCL) decoding, which stems from sequential processing and struggles to balance computational efficiency with soft information accuracy. The paper presents the first soft-output capability integrated into a fast SCL architecture, introducing the SO-FSCL algorithm along with its logarithmic-domain, hardware-friendly variant. By leveraging node-level fast decoding and posterior log-likelihood ratio (LLR) estimation, the method efficiently extracts soft information within special nodes. Supporting both hard-decision and soft-output modes, the proposed approach reduces decoding time steps by 81.8%, decreases adder and comparator counts by 41.3% and 46.4%, respectively, and achieves soft-output performance comparable to that of SO-SCL while significantly outperforming existing alternatives.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Hardware-aware ML
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Security and Privacy: Large-scale security measurements

Abstract

The soft-output successive cancellation list (SO-SCL) decoder provides a methodology for estimating the a-posteriori probability log-likelihood ratios by only leveraging the conventional SCL decoder of polar codes. However, the sequential decoding nature of SCL introduces high decoding latency to SO-SCL. In this paper, we incorporate node-based fast decoding into the SO-SCL framework. After addressing the challenge of soft output extraction in special node decoding, we proposed the soft-output fast SCL (SO-FSCL) decoding algorithm, along with its log-domain implementation and hardware-friendly version. The proposed SO-FSCL decoder can be regarded as an add-on extension to FSCL decoder, enabling us to autonomously choose whether to output only hard decisions like FSCL or to provide additional soft outputs. Latency and complexity analyses demonstrate that SO-FSCL can significantly reduce, for example, decoding time steps by 81.8\% (with unlimited resources), the number of additions by 41.3\%, and the number of comparisons by 46.4\%. Meanwhile, simulation results indicate that SO-FSCL delivers almost the same soft-output performance as SO-SCL, outperforming other soft-output polar decoders, especially in scenarios involving iterative decoding.

Problem

• polar codes
• soft-output decoding
• successive cancellation list
• decoding latency
• iterative decoding

Innovation

• soft-output decoding
• polar codes
• fast successive cancellation list
• node-based decoding
• iterative decoding