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 $L_{\mathrm{list}}=64$.

翻译：极化码的盲识别在非协作场景中仍具挑战性，尤其是已知码长下的信息集识别。现有方法主要依赖于由生成矩阵结构和信道误比特率确定的阈值判决，未能充分利用接收信号中的软信息。本文提出一种利用逐次消去列表（SCL）译码对已知码长的极化码进行盲识别的方法。该方法利用多个接收向量中冻结比特与信息比特在源端判决对数似然比（LLR）上的不同统计特性：冻结比特倾向于支持零判决，而信息比特的$0/1$判决近似等概率。基于这一特性，译码器在每个比特位置针对冻结比特和信息比特假设扩展候选路径，利用对应的平均路径度量评估其可靠性，并仅保留$L_{\mathrm{list}}$条最可靠路径用于后续识别。最终选取最可靠幸存路径对应的信息集模式作为识别结果。仿真结果表明，随着列表规模增大，所提方案提升了识别成功率。对于$(32,16)$、$(64,32)$和$(128,64)$极化码，当$L_{\mathrm{list}}=64$时，该方法相较于现有方案至少获得$2.5$ dB增益。