Blind Recognition of Polar Codes Using Successive Cancellation List Decoding

2026-05-13

AI Summary

This work addresses the challenge of blind identification of polar code information sets in non-cooperative scenarios, where existing methods often fail to fully exploit soft information from received signals. To overcome this limitation, the study introduces successive cancellation list (SCL) decoding into the identification task for the first time and proposes a novel approach based on statistical differences in log-likelihood ratios (LLRs). By hypothesizing each bit position as either frozen or information, the method dynamically expands candidate paths and selects the most reliable one according to average path metrics, ultimately yielding the optimal information set. Evaluated on (32,16), (64,32), and (128,64) polar codes, the proposed scheme significantly outperforms state-of-the-art techniques, achieving at least a 2.5 dB gain in identification success rate with a list size of 64.

Abstract

Blind recognition of polar codes remains challenging in non-cooperative scenarios, particularly for information-set recognition with known code length. Existing methods mainly rely on threshold decisions determined by the generator-matrix structure and channel bit error probability, without fully exploiting the soft information in received signals. In this letter, we propose a blind recognition method using successive cancellation list (SCL) decoding for polar codes with known code length. The proposed method exploits the distinct statistical behaviors of frozen and information bits in source-side decision log-likelihood ratios (LLRs) over multiple received vectors: frozen bits tend to favor zero decisions, whereas information bits exhibit nearly equiprobable $0/1$ decisions. Based on this property, the decoder expands candidate paths under the frozen-bit and information-bit hypotheses at each bit position, evaluates their reliabilities using the corresponding average path metrics, and retains only the $L_{\mathrm{list}}$ most reliable paths for subsequent recognition. Finally, the information-set pattern corresponding to the most reliable surviving path is selected as the recognition result. Simulation results show that the proposed scheme improves the recognition success rate as the list size increases. For the $(32,16)$, $(64,32)$, and $(128,64)$ polar codes, it achieves at least $2.5$ dB gain over the previous method when $L_{\mathrm{list}}=64$.

Problem

• blind recognition
• polar codes
• information-set recognition
• non-cooperative scenarios
• code length

Innovation

• blind recognition
• polar codes
• successive cancellation list decoding
• log-likelihood ratios
• information-set identification