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arXiv 2605.13331cs.ITmath.IT
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极码的盲识别使用逐次取消列表解码

Blind Recognition of Polar Codes Using Successive Cancellation List Decoding

Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications(北京邮电大学通用无线通信重点实验室,教育部)

Changwei Tu, Yang Liu, Xianzhao Feng, Kai Niu

AI总结:

本文提出了一种基于逐次取消列表解码的极码盲识别方法,利用冻结位和信息位在接收向量上的统计行为差异,提高识别成功率。

摘要:

极码的盲识别在非合作场景中仍具挑战性,特别是在已知码长的信息集识别中。现有方法主要依赖生成矩阵结构和信道比特误码概率决定的阈值决策,未充分利用接收到的信号中的软信息。本文提出了一种利用逐次取消列表(SCL)解码的盲识别方法,通过分析源侧决策对数似然比(LLRs)中冻结位和信息位的统计行为差异,扩展候选路径,评估路径可靠性,并保留最可靠的L_list路径用于识别。仿真结果表明,随着列表大小的增加,识别成功率提高。对于(32,16)、(64,32)和(128,64)极码,当L_list=64时,相比先前方法至少获得2.5dB增益。

英文摘要:

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

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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