高誤碼率下Turbo碼分量編碼器快速識別算法

張立民; 吳昭軍; 鐘兆根

doi:10.11999/JEIT170168

高誤碼率下Turbo碼分量編碼器快速識別算法

doi: 10.11999/JEIT170168 cstr: 32379.14.JEIT170168

1.
(海軍航空大學(xué)信息融合所煙臺 264001) ②(海軍航空大學(xué)電子信息工程系煙臺 264001)

基金項目:

國家自然科學(xué)基金(91538201)，泰山學(xué)者工程專項經(jīng)費(fèi)(ts201511020)

計量
- 文章訪問數(shù): 1478
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- 被引次數(shù): 0
出版歷程
- 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: 針對目前Turbo碼中，分量編碼器遞歸系統(tǒng)卷積碼識別算法計算量大，容錯性不好兩大缺點(diǎn)，該文提出了一種容錯性能較好的快速識別算法。首先，在分析遞歸系統(tǒng)卷積碼特殊結(jié)構(gòu)的基礎(chǔ)上，定義了更具普遍意義的廣義碼重概念；其次，建立出遞歸系統(tǒng)卷積碼生成多項式數(shù)據(jù)庫，按照數(shù)據(jù)庫中多項式是否為實(shí)際編碼多項式的情況，分析出多項式所對應(yīng)的結(jié)果向量廣義碼重概率分布；然后，按照分析出的廣義碼重概率分布，基于極大極小準(zhǔn)則，導(dǎo)出快速識別算法判決門限的計算公式；最后通過遍歷多項式數(shù)據(jù)庫，將遍歷的多項式所對應(yīng)的校驗(yàn)方程廣義碼重值與判決門限比較，從而實(shí)現(xiàn)參數(shù)的快速識別。仿真結(jié)果表明：理論分析出的廣義碼重概率分布與仿真結(jié)果相一致，同時算法容錯性能較好，在誤碼率高達(dá)0.09的條件下，各種編碼約束長度下的遞歸系統(tǒng)卷積碼識別率在90%以上，并且計算復(fù)雜度較小。
- 遞歸系統(tǒng)卷積碼 /
- 多項式數(shù)據(jù)庫 /
- 判決門限 /
- 極大極小準(zhǔn)則 /
- 識別
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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