Unbiased Self-synchronous Scrambler Identification Based on Log Conditional Likelihood Ratio

ZHONG Zhaogen; TAN Jiyuan; XIE Cunxiang

doi:10.11999/JEIT230992

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ZHONG Zhaogen, TAN Jiyuan, XIE Cunxiang. Unbiased Self-synchronous Scrambler Identification Based on Log Conditional Likelihood Ratio[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230992

Citation:

ZHONG Zhaogen, TAN Jiyuan, XIE Cunxiang. Unbiased Self-synchronous Scrambler Identification Based on Log Conditional Likelihood Ratio[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230992

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Unbiased Self-synchronous Scrambler Identification Based on Log Conditional Likelihood Ratio

doi: 10.11999/JEIT230992

1.
School of Basis of Aviation, Naval Aviation University, Yantai, Shandong 264001, China
2.
Naval Submarine Academy, Qingdao, Shandong 266199, China

Funds: The National Natural Science Foundation of China (62371465), Key Laboratory of Information System Security Technology Fund Grant Project (6142111190404)

Received Date: 2022-09-11
Rev Recd Date: 2023-12-10

Available Online: 2023-12-20

Abstract

Abstract

To overcome the drawback of poor adaptability of existing unbiased self-synchronous scrambling code recognition algorithms at low Signal-to-Noise Ratios (SNR), a soft-judgement recognition method based on the log conditional likelihood ratio is proposed. Firstly, the linear constraint equations for the pairwise even-vector product of the self-synchronous scrambler of linear grouping codes and the self-synchronous scrambler of convolutional codes are constructed, and then the log likelihood ratio function is introduced, the log conditional likelihood ratio function based on the soft judgment is constructed, and the distribution characteristics of its mean and variance are analyzed. Finally the identification of self-synchronous scrambler of linear grouping codes and self-synchronous scrambler of convolutional codes is accomplished through binary assumption and the derived coresponding judgement threshold value. The simulations show that the proposed algorithm is able to complete the recognition of generating polynomials at low signal-to-noise ratios, and has a good low signal-to-noise adaptation capability. Compared with the recognition method based on solving the cost function, the recognition rate of the algorithm is greatly improved at signal-to-noise ratios below 3 dB, and when the recognition rate is 90%, the proposed algorithm achieves a performance gain of about 3 dB.
- Self-synchronous scrambler,
- Linear block code,
- Convolutional code,
- Log conditional likelihood ratios

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References(20)

References

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