{"title":"Blind identification for Turbo codes in AMC systems","authors":"R. Pei, Zulin Wang, Qiang Xiao, Li Quan","doi":"10.1109/ICCSN.2016.7587202","DOIUrl":null,"url":null,"abstract":"Blind identification for channel codes are essential in adaptive modulation and coding (AMC) systems. Since Turbo codes are popular in AMC systems, it's necessary to identify its parameters. In this paper, we focus on the identification for Turbo codes from a closed-set. The proposed approach firstly identifies the first component code by accumulating Log-Likelihood Ratio (LLR) for syndrome a posteriori probability, then the interleaver and the other component code are identified by decoding based on zero insertion and LLR accumulation. This approach is robust to noise due to LLR. Moreover, it applies to both symmetric Turbo codes with two same component codes and asymmetric Turbo codes with two different component codes. Simulation results demonstrate that the proposed blind identification scheme is able to identify Turbo codes at signal-to-noise ratio (SNR) larger than 3.5dB.","PeriodicalId":158877,"journal":{"name":"2016 8th IEEE International Conference on Communication Software and Networks (ICCSN)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 8th IEEE International Conference on Communication Software and Networks (ICCSN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCSN.2016.7587202","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Blind identification for channel codes are essential in adaptive modulation and coding (AMC) systems. Since Turbo codes are popular in AMC systems, it's necessary to identify its parameters. In this paper, we focus on the identification for Turbo codes from a closed-set. The proposed approach firstly identifies the first component code by accumulating Log-Likelihood Ratio (LLR) for syndrome a posteriori probability, then the interleaver and the other component code are identified by decoding based on zero insertion and LLR accumulation. This approach is robust to noise due to LLR. Moreover, it applies to both symmetric Turbo codes with two same component codes and asymmetric Turbo codes with two different component codes. Simulation results demonstrate that the proposed blind identification scheme is able to identify Turbo codes at signal-to-noise ratio (SNR) larger than 3.5dB.
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