基于混沌映射和高斯扰动的多通道恒模盲均衡

胡爽; 冯姣; 张治中; 李鹏; 周华

doi:10.11959/j.issn.1000-0801.2025101

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基于混沌映射和高斯扰动的多通道恒模盲均衡

研究与开发 | 更新时间：2025-06-03

- 基于混沌映射和高斯扰动的多通道恒模盲均衡
- Chaotic-mapping and Gaussian perturbation-based multi-channel constant modulus blind equalization
- 电信科学 2025年41卷第5期页码：96-106
- 作者机构：
  
  南京信息工程大学电子与信息工程学院，江苏南京 210044
- 作者简介：
  
  [ "胡爽（2000- ），女，南京信息工程大学电子与信息工程学院硕士生，主要研究方向为无线通信技术。" ]
  [ "冯姣（1984- ），女，博士，南京信息工程大学电子与信息工程学院副教授，主要研究方向为无线通信技术等。" ]
  [ "张治中（1972- ），男，博士，南京信息工程大学电子与信息工程学院教授，主要研究方向为5G/6G移动通信与信息处理等。" ]
  [ "李鹏（1984- ），男，博士，南京信息工程大学电子与信息工程学院教授，主要研究方向为无线通信技术等。" ]
  [ "周华（1984- ），男，博士，南京信息工程大学电子与信息工程学院副教授，主要研究方向为5G/6G移动通信与信息处理等。" ]
- 基金信息：
  
  国家重点研发计划项目(2022YFB2902100);江苏省重点研发计划项目(BE2023088)
- DOI：10.11959/j.issn.1000-0801.2025101
  中图分类号： TN911.5
- 收稿：2024-12-17，
  
  修回：2025-02-25，
  
  纸质出版：2025-05-20
- 稿件说明：
移动端阅览
胡爽,冯姣,张治中等.基于混沌映射和高斯扰动的多通道恒模盲均衡[J].电信科学,2025,41(05):96-106.

HU Shuang,FENG Jiao,ZHANG Zhizhong,et al.Chaotic-mapping and Gaussian perturbation-based multi-channel constant modulus blind equalization[J].Telecommunications Science,2025,41(05):96-106.
胡爽,冯姣,张治中等.基于混沌映射和高斯扰动的多通道恒模盲均衡[J].电信科学,2025,41(05):96-106. DOI： 10.11959/j.issn.1000-0801.2025101.

HU Shuang,FENG Jiao,ZHANG Zhizhong,et al.Chaotic-mapping and Gaussian perturbation-based multi-channel constant modulus blind equalization[J].Telecommunications Science,2025,41(05):96-106. DOI： 10.11959/j.issn.1000-0801.2025101.

摘要

在多通道信道仿真系统中，通道之间幅相不一致会使系统性能恶化，因此通道均衡技术必不可少。与传统的均衡器设计不同，盲均衡算法无须训练序列，提高了系统效率，不干扰仿真流程。基于粒子群优化的改进恒模盲均衡算法是一种新的盲均衡算法，引入粒子群算法寻找均衡器的最优解，提高了算法的收敛速度。然而该算法对初始参数敏感，容易陷入局部最优，恒定权重和学习因子会使算法稳态均方误差变大，局部和全局搜索能力不均。针对上述问题，提出了一种基于混沌映射和高斯扰动的改进粒子群恒模盲均衡算法。经过仿真验证，所提算法性能有所提升。对算法初期设置的参数敏感性降低；稳定后的适应度降低0.011；在误码率达到

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=82399597&type=

2.53999996

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=82399609&type=

5.58799982

量级时，信噪比相较于传统算法降低更多；均方误差降低1.77 dB；码间干扰降低0.64 dB。此外，对比了不同的惯性权重方案，进一步验证了所提算法收敛速度更快，码间干扰更低。

Abstract

In multi-channel communication simulation systems

inconsistencies in amplitude and phase between channels can degrade system performance

making channel equalization technology essential. Unlike traditional equalizer designs

blind equalization algorithms do not require training sequences

improving system efficiency and not interfering with the simulation process. The improved constant modulus blind equalization algorithm based on particle swarm optimization is a new blind equalization method that introducing particle swarm optimization to find the optimal solution for the equalizer

thereby improving the convergence speed of the algorithm. However

this algorithm is sensitive to initial parameters and is prone to get stuck in local opti

mum. Constant weights and learning factors can increase the steady-state mean square error

resulting in uneven local and global search capabilities. To address these issues

an improved particle swarm constant modulus blind equalization algorithm based on chaotic-mapping and Gaussian perturbation was proposed. After simulation verification

the performance of the proposed algorithm has been improved. The sensitivity to parameters set in the early stages of the algorithm is reduced. The fitness decreases by 0.011 after stabilization. When the symbol error rate reaches

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=82399624&type=

2.53999996

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=82399601&type=

5.58799982

level

the signal-to-noise ratio decreases more compared to traditional algorithms. The mean square error is reduced by 1.77 dB

and intersymbol interference is reduced by 0.64 dB. In addition

by comparing different inertia weight schemes

it is further verified that the proposed algorithm achieves faster convergence speed and lower inter-symbol interference.

关键词

Keywords

references

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