Scrambler blind recognition method based on soft information

Ze-liang CHEN; Hua PENG; Ke-xian GONG; Pei-dong YU

doi:10.11959/j.issn.1000-436x.2017043

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Scrambler blind recognition method based on soft information

Correspondences | 更新时间：2024-06-05

- Scrambler blind recognition method based on soft information
- Journal on Communications Vol. 38, Issue 3, Pages: 174-182(2017)
- 作者机构：
  
  解放军信息工程大学信息系统工程学院，河南郑州 450001
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61401511)
- DOI：10.11959/j.issn.1000-436x.2017043
  CLC： TN911.7
- Online First：2017-03，
  
  Published：15 March 2017
- 稿件说明：
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Ze-liang CHEN, Hua PENG, Ke-xian GONG, et al. Scrambler blind recognition method based on soft information[J]. Journal on Communications, 2017, 38(3): 174-182.
DOI：

Ze-liang CHEN, Hua PENG, Ke-xian GONG, et al. Scrambler blind recognition method based on soft information[J]. Journal on Communications, 2017, 38(3): 174-182. DOI： 10.11959/j.issn.1000-436x.2017043.

摘要

针对非合作接收的加扰信号，提出2种基于软信息的扰码盲识别方法。方法1利用软信息建立了扰码系数的代价函数，采用实数域的优化理论进行正向求解，不再需要对多项式测试闭集进行遍历；方法2利用软信息建立了符合度的概念，以每个测试扰码多项式符合度的大小作为判别的标准，相比硬判决识别算法，其对接收信息得到了更充分的利用。仿真结果表明，方法1相比Cluzeau提出的遍历方法，其自同步扰码多项式的识别时间可从5 min 18 s缩短为8 s；方法2与现有硬判决算法相比，达到较高正确率时，具有约2 dB的信噪比增益。

Abstract

Two scrambler blind recognition methods based on soft information were proposed for received signal in non-cooperative ways.The first method established a cost function of the scrambler coefficients by using the soft information

and adopted the optimization theory of real number field for positive solution.So it didn’t need to traverse the closed set of test polynomial any more.The second method built conformity degree concept with the soft information

and used the size of conformity of each test scrambler polynomial as the discriminant criteria.So it made more full use of the received information compared to the hard sentence recognition algorithm.Simulation results show that the first method can shorten the recognition time of a synchronous scrambler polynomial from 5 min 18 s to 8 s compared with the traversal method put forward by Cluzeau

and the second method has 2 dB SNR gain when to achieve the relatively high accuracy compared with the hard sentence recognition algorithm.

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references

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