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Blind recognition of polar code parameters based on log-likelihood ratio

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摘要 The syndrome a posteriori probability of the log-likelihood ratio of intercepted codewords is used to develop an algorithm that recognizes the polar code length and generator matrix of the underlying polar code.Based on the encoding structure,three theorems are proved,two related to the relationship between the length and rate of the polar code,and one related to the relationship between frozen-bit positions,information-bit positions,and codewords.With these three theorems,polar codes can be quickly reconstruced.In addition,to detect the dual vectors of codewords,the statistical characteristics of the log-likelihood ratio are analyzed,and then the information-and frozen-bit positions are distinguished based on the minimumerror decision criterion.The bit rate is obtained.The correctness of the theorems and effectiveness of the proposed algorithm are validated through simulations.The proposed algorithm exhibits robustness to noise and a reasonable computational complexity.
出处 《Journal of Systems Engineering and Electronics》 2025年第3期642-658,共17页 系统工程与电子技术(英文版)
基金 supported by the National Natural Science Foundation of China(62371465) Taishan Scholar Project of Shandong Province(ts201511020) the Chinese National Key Laboratory of Science and Technology on Information System Security(6142111190404).
作者简介 ZHONG Zhaogen,was born in 1984.He received his Ph.D.degree in information and communication engineering from the Naval Aviation University,in 2013.He is currently an associate professor with the Naval Aviation University.His research interests include spread spectrum signal processing.E-mail:zhongzhaogen@163.com;Corresponding author:XIE Cunxiang,was born in 1996.He received his M.S.degree in information and communication engineering from the Naval Aviation University,in 2021.He is pursuing his Ph.D.degree in information and communication engineering with the Department of Information Fusion,Naval Aviation University.His research interests include deep learning and specific emitter identification.E-mail:xiecunxiang1996@163.com;JIN Kun,was born in 1993.She received her M.S.degree from the Naval University of Engineering,in 2018.She is currently working with the Naval Aviation University.Her research interests are circuit and system and signal intelligence analysis and processing.E-mail:Jinkunhg@163.com.
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