高误码率下Turbo码分量编码器快速识别算法

张立民; 吴昭军; 钟兆根

doi:10.11999/JEIT170168

高误码率下Turbo码分量编码器快速识别算法

doi: 10.11999/JEIT170168

1.
(海军航空大学信息融合所烟台 264001) ②(海军航空大学电子信息工程系烟台 264001)

基金项目:

国家自然科学基金(91538201)，泰山学者工程专项经费(ts201511020)

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- 文章访问数: 1417
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- 被引次数: 0
出版历程
- 收稿日期: 2017-02-28
- 修回日期: 2017-08-17
- 刊出日期: 2018-01-19

A Fast Algorithm for Blind Identification of Turbo at High BER

1.
(Department of Information Fusion, the University of Naval Aeronatical, Yantai 264001, China)
2.
(Department of Electronic and Information Engineering, the University of Naval Aeronatical, Yantai 264001, China)

Funds:

The National Natural Science Foundation of China (91538201), The Taishan Scholar Special Foundation (ts201511020)

摘要

摘要: 针对目前Turbo码中，分量编码器递归系统卷积码识别算法计算量大，容错性不好两大缺点，该文提出了一种容错性能较好的快速识别算法。首先，在分析递归系统卷积码特殊结构的基础上，定义了更具普遍意义的广义码重概念；其次，建立出递归系统卷积码生成多项式数据库，按照数据库中多项式是否为实际编码多项式的情况，分析出多项式所对应的结果向量广义码重概率分布；然后，按照分析出的广义码重概率分布，基于极大极小准则，导出快速识别算法判决门限的计算公式；最后通过遍历多项式数据库，将遍历的多项式所对应的校验方程广义码重值与判决门限比较，从而实现参数的快速识别。仿真结果表明：理论分析出的广义码重概率分布与仿真结果相一致，同时算法容错性能较好，在误码率高达0.09的条件下，各种编码约束长度下的递归系统卷积码识别率在90%以上，并且计算复杂度较小。
- 递归系统卷积码 /
- 多项式数据库 /
- 判决门限 /
- 极大极小准则 /
- 识别
Abstract: In order to solve the defects which are poor error tolerance and large amount of calculation in current algorithms to recognize the Recursive Systematic Convolutional (RSC) encoder in Turbo codes, a new fast algorithm is proposed. Firstly, based on special structure of RSC codes, the concept named generalized code weight is defined which is more general. Secondly, the RSC polynomial database is built up, the probability distribution of generalized code weight can be analyzed under two situation whether the polynomials in database is actual polynomial, then based on distribution and Maxmin criteria, the decision threshold of the fast algorithm is deduced. Finally, the parameters can be recognized by traversing the polynomials in database and compare the corresponding generalized code weight with decision threshold. The simulation results show that theoretical analysis of the probability distribution is consistent with the simulations and the performance of error tolerant is preferable. The actual simulation show that correct rate of recognition can reach above 90% when the rate of bit error is as high as 0.09, besides the computational complexity is low.
- Recursive Systematic Convolutional (RSC) code /
- Polynomial database /
- Decision threshold /
- Maxmin criteria /
- Identification

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参考文献(28)

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