Polar Decoding Tree Pruning Based on Soft Output Extraction

2026-06-11

AI Summary

This work addresses the high computational and sorting complexity of successive cancellation list (SCL) decoding for polar codes, which stems from retaining numerous low-contribution paths. The authors propose a pruning strategy based on soft-output extraction, introducing for the first time a block-level soft information extraction mechanism into both SCL and its fast variant (FSCL). This mechanism approximates the correctness probability of each path and dynamically eliminates unreliable candidates using an adaptive threshold. The resulting SOP-SCL and SOP-FSCL decoders significantly reduce decoding complexity while preserving error-correction performance, outperforming state-of-the-art pruning approaches.

Abstract

Although the successive cancellation list (SCL) decoding of polar codes exhibits excellent performance, it retains many decoding paths in the list with negligible contribution to the final output, resulting in high sorting and computational complexity. In this letter, we propose a novel pruning strategy to mitigate the decoding complexity. By leveraging the blockwise soft output extraction process of soft-output SCL and soft-output fast SCL decoding, we provide an accurate approximation of the probability that a decoding path is correct, and thus accordingly prune the paths failing to meet a pre-defined reliability threshold. The complexity reduction achieved by the proposed soft-output-based pruned SCL (SOP-SCL) decoder and its fast version, SOP-FSCL decoder, is significant, without any compromise in error-correction performance. Meanwhile, they also prove to be more efficient than state-of-the-art pruned polar decoders.

Problem

• polar codes
• successive cancellation list decoding
• decoding complexity
• path pruning
• soft output

Innovation

• polar codes
• soft-output decoding
• tree pruning
• SCL decoding
• complexity reduction