通信学报 ›› 2017, Vol. 38 ›› Issue (3): 174-182.doi: 10.11959/j.issn.1000-436x.2017043

• 学术通信 • 上一篇    

基于软信息的扰码盲识别方法

陈泽亮,彭华,巩克现,于沛东   

  1. 解放军信息工程大学信息系统工程学院,河南 郑州 450001
  • 修回日期:2016-09-01 出版日期:2017-03-01 发布日期:2017-04-13
  • 作者简介:陈泽亮(1992-),男,湖南岳阳人,解放军信息工程大学硕士生,主要研究方向为信道编码识别分析。|彭华(1973-),男,江西萍乡人,解放军信息工程大学教授、博士生导师,主要研究方向为软件无线电、通信信号处理等。|巩克现(1976-),男,山东泰安人,解放军信息工程大学副教授、硕士生导师,主要研究方向为软件无线电、差错控制编码等。|于沛东(1989-),男,湖南慈利人,解放军信息工程大学博士生,主要研究方向为信道编码及其识别分析。
  • 基金资助:
    国家自然科学基金资助项目(61401511)

Scrambler blind recognition method based on soft information

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

  1. School of Information Systems Engineering,PLA Information Engineering University,Zhengzhou 450001,China
  • Revised:2016-09-01 Online:2017-03-01 Published:2017-04-13
  • Supported by:
    The National Natural Science Foundation of China(61401511)

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。

关键词: 软信息, 扰码盲识别, 代价函数, 符合度, 抗噪性能

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.

Key words: soft information, scrambler blind recognition, cost function, conformity degree, anti-noise performance

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