麻雀搜索算法优化极端梯度提升的扩频码研究

梁智儒; 边东明; 张更新

doi:10.13756/j.gtxyj.2024.230082

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麻雀搜索算法优化极端梯度提升的扩频码研究

无线通信技术 | 更新时间：2024-12-05

- 麻雀搜索算法优化极端梯度提升的扩频码研究
- Research on Spread Spectrum Codes Optimized by Sparrow Search Algorithm and Extreme Gradient Boosting
- 光通信研究 2024年第6期页码：23008201-23008206
- 作者机构：
  
  南京邮电大学　通信与信息工程学院，南京　210003
- 作者简介：
  
  梁智儒，硕士。E-mail：574799923@qq.com
- 基金信息：
  
  国家自然科学基金资助项目(61801445)
- DOI：10.13756/j.gtxyj.2024.230082
  中图分类号： TN914
- 纸质出版日期：2024-12-10，
  
  收稿日期：2023-08-01，
  
  修回日期：2023-09-27，
- 稿件说明：
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梁智儒，边东明，张更新. 麻雀搜索算法优化极端梯度提升的扩频码研究[J]. 光通信研究，2024(6):230082.

Liang Z R, Bian D M, Zhang G X. Research on Spread Spectrum Codes Optimized by Sparrow Search Algorithm and Extreme Gradient Boosting [J]. Study on Optical Communications, 2024(6)：230082.
梁智儒，边东明，张更新. 麻雀搜索算法优化极端梯度提升的扩频码研究[J]. 光通信研究，2024(6):230082. DOI： 10.13756/j.gtxyj.2024.230082.

Liang Z R, Bian D M, Zhang G X. Research on Spread Spectrum Codes Optimized by Sparrow Search Algorithm and Extreme Gradient Boosting [J]. Study on Optical Communications, 2024(6)：230082. DOI： 10.13756/j.gtxyj.2024.230082.

摘要

【目的】

直接序列扩频系统（DSSS）在军事和民用通信中都得到了广泛应用，因其具有对各种常见干扰较强的抵抗能力和安全性较高等优点，且易于实现，被广泛应用于码分多址（CDMA）之中。然而，在非协作通信的应用场景中，检测DSSS信号，对DSSS信号参数进行估计，甚至截获信息，都是文章需要考虑的问题。在DSSS中，正确识别所使用的扩频序列是正确解扩的重要前提条件。针对低信噪比DSSS信号扩频码识别成功率低的问题，文章通过结合

序列的三阶相关函数（TCF）峰值特性，在降噪预处理的前提下，通过功率谱二次处理识别DSSS信号的伪码周期作为先验信息，将扩频码的识别问题具体成为一个峰值检测分类的问题，进而对峰值识别分类进行了研究。

【方法】

文章提出了使用麻雀搜索算法（SSA）优化极端梯度提升（XGBOOST）的DSSS信号三阶相关峰值分类方法，提高了对

序列分类识别的准确率。

【结果】

通过在不同信噪比下对比常规峰值检测和决策树分类方法以及对比不同序列周期的识别分类准确率，仿真结果表明，经过预处理的SSA XGBOOST扩频码识别分类方法比起常规机器学习与峰值检测方法分类识别成功率更高，在高序列周期下性能逐步提升。

【结论】

文章所提方法能在较低的信噪比条件下更准确地识别分类扩频码

序列。

Abstract

【Objective】

Direct Sequence Spread Spectrum (DSSS) has been widely used in military and civilian communications due to its strong resistance to various common interferences

high security

and ease of implementation. It has been widely used in Code Division Multiple Access (CDMA) system. However

in non-cooperative communication scenarios

detecting DSSS signals

estimating DSSS signal parameters

and even intercepting information are all issues that need to be considered. In DSSS

correctly identifying the spread spectrum sequence used is an important prerequisite for correcting despreading. To address the problem of low success rate of spread code identification for low signal-to-noise ratio DSSS signals

this paper combines the Third-order Correlation Function (TCF) of

-sequences and its peak characteristics to identify the pseudo-code period of DSSS signals as prior information through power spectrum secondary processing on the premise of denoising preprocessing. The problem of spread code identification is transferred into a peak detection classification problem. The peak identification and classification is then

studied.

【Methods】

This paper proposes a method of using Sparrow Search Algorithm (SSA) to optimize Extreme Gradient Boosting (XGBOOST) for third-order correlation peak classification of direct spread signals to improve the accuracy of

-sequence classification and identification.

【Results】

By comparing conventional peak detection and decision tree classification methods at different signal-to-noise ratios and comparing the classification accuracy of different sequence periods

the simulation results show that the spread code identification and classification method optimized by SSA with XGBOOST after preprocessing has a higher classification and identification success rate than conventional machine learning and peak detection methods. Its performance gradually improves at high sequence periods.

【Conclusion】

This method can more accurately identify and classify

-sequence spread codes under low signal-to-noise ratio conditions.

关键词

m序列三阶相关函数麻雀搜索算法优化极端梯度提升

Keywords

m-sequenceTCFSSAXGBOOST

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