A Fast Algorithm for Blind Identification of Turbo at High BER

ZHANG Limin; WU Zhaojun; ZHONG Zhaogen

doi:10.11999/JEIT170168

Volume 40 Issue 1

Jan. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(1): 235-243

ZHANG Limin, WU Zhaojun, ZHONG Zhaogen. A Fast Algorithm for Blind Identification of Turbo at High BER[J]. Journal of Electronics & Information Technology, 2018, 40(1): 235-243. doi: 10.11999/JEIT170168

Citation:

ZHANG Limin, WU Zhaojun, ZHONG Zhaogen. A Fast Algorithm for Blind Identification of Turbo at High BER[J]. Journal of Electronics & Information Technology, 2018, 40(1): 235-243. doi: 10.11999/JEIT170168

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A Fast Algorithm for Blind Identification of Turbo at High BER

doi: 10.11999/JEIT170168

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)

Received Date: 2017-02-28
Rev Recd Date: 2017-08-17
Publish Date: 2018-01-19

Abstract

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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References(28)

References

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