Turbo码在AMC系统中的盲识别

2016 8th IEEE International Conference on Communication Software and Networks (ICCSN) Pub Date : 2016-06-04 DOI:10.1109/ICCSN.2016.7587202

R. Pei, Zulin Wang, Qiang Xiao, Li Quan

{"title":"Turbo码在AMC系统中的盲识别","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":"{\"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}","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

摘要

信道码的盲识别是自适应调制编码(AMC)系统的关键。由于Turbo码在AMC系统中很流行，因此有必要对其参数进行识别。本文主要研究了Turbo码在闭集中的识别问题。该方法首先通过累积后验概率对数似然比(LLR)识别第一分量码，然后基于零插入和LLR累积的译码方法识别交织器和其他分量码。由于LLR的存在，该方法对噪声具有鲁棒性。此外，它既适用于具有两个相同分量码的对称Turbo码，也适用于具有两个不同分量码的非对称Turbo码。仿真结果表明，该盲识别方案能够在信噪比大于3.5dB的情况下识别Turbo码。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Blind identification for Turbo codes in AMC systems

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.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

2016 8th IEEE International Conference on Communication Software and Networks (ICCSN)

自引率

0.00%

发文量