基于K均值聚类的SPPM分步分类检测算法

王惠琴; 侯文斌; 彭清斌; 曹明华; 黄瑞; 刘玲

doi:10.11959/j.issn.1000-436x.2022010

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基于K均值聚类的SPPM分步分类检测算法

学术论文 | 更新时间：2024-06-05

- 基于K均值聚类的SPPM分步分类检测算法
- Step-by-step classification detection algorithm of SPPM based on K-means clustering
- 通信学报 2022年43卷第1期页码：161-171
- 作者机构：
  
  兰州理工大学计算机与通信学院，甘肃兰州 730050
- 作者简介：
  
  [ "王惠琴（1971- ），女，甘肃渭源人，博士，兰州理工大学教授、博士生导师，主要研究方向为无线光通信理论与技术" ]
  [ "侯文斌（1997- ），男，河南长垣人，兰州理工大学硕士生，主要研究方向为无线光通信理论与系统、机器学习算法" ]
  [ "彭清斌（1983- ），男，甘肃兰州人，兰州理工大学博士生、讲师，主要研究方向为无线光通信技术" ]
  [ "曹明华（1979- ），男，甘肃平凉人，博士，兰州理工大学教授、博士生导师，主要研究方向为无线光通信理论与技术、光无线融合技术" ]
  [ "黄瑞（1973- ），男，宁夏银川人，兰州理工大学高级工程师，主要研究方向为无线光通信理论与技术" ]
  [ "刘玲（1997- ），女，甘肃天水人，兰州理工大学硕士生，主要研究方向为光通信理论与技术、阶梯码" ]
- 基金信息：
  
  国家自然科学基金资助项目(61861026);国家自然科学基金资助项目(61875080);甘肃省自然科学基金资助项目(20JR5RA472)
- DOI：10.11959/j.issn.1000-436x.2022010
  中图分类号： TN929.12
- 网络出版日期：2022-01，
  
  纸质出版日期：2022-01-25
- 稿件说明：
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王惠琴, 侯文斌, 彭清斌, 等. 基于K均值聚类的SPPM分步分类检测算法[J]. 通信学报, 2022,43(1):161-171.

Huiqin WANG, Wenbin HOU, Qingbin PENG, et al. Step-by-step classification detection algorithm of SPPM based on K-means clustering[J]. Journal on communications, 2022, 43(1): 161-171.
王惠琴, 侯文斌, 彭清斌, 等. 基于K均值聚类的SPPM分步分类检测算法[J]. 通信学报, 2022,43(1):161-171. DOI： 10.11959/j.issn.1000-436x.2022010.

Huiqin WANG, Wenbin HOU, Qingbin PENG, et al. Step-by-step classification detection algorithm of SPPM based on K-means clustering[J]. Journal on communications, 2022, 43(1): 161-171. DOI： 10.11959/j.issn.1000-436x.2022010.

摘要

针对空间脉冲位置调制系统中采用最大似然检测算法时存在计算复杂度高的问题，依据空间脉冲位置调制信号矩阵的特点，提出了一种基于K均值聚类的分步分类检测算法。首先，采用基于信号向量检测算法完成训练样本中光源索引号的检测，利用 K 均值聚类算法对训练样本进行离线训练得到其质心与调制符号间的映射关系。然后，以该映射关系为准则完成在线调制符号的实时检测，以穷搜索方式检测出光源索引号。最后，采用蒙特卡罗方法研究了聚类数目、初始化次数等关键参数对系统误比特率性能的影响。仿真结果表明，所提算法能够取得近似最大似然算法的误比特率性能，同时大幅度降低了信号检测的计算复杂度。与线性译码算法相比，所提算法还可适用于探测器数目小于光源数目的通信场景。

Abstract

In view of the high computational complexity in spatial pulse position modulation systems when using maximum likelihood detection algorithm

a step-by-step classification detection algorithm based on K-means clustering was proposed according to the characteristics of signal matrix with spatial pulse position modulation.The signal vector detection algorithm was utilized to detect the index of light source in the training samples.The on K-means clustering algorithm was utilized to acquire the mapping rule between centroid of samples and modulated symbol by offline training.Subsequently

online detection of modulated symbols was achieved based on the mapping rule

and then the index of light sources was detected by exhaustive search.In addition

Monte Carlo method was used to investigate the effects of key parameters such as the number of clusters and initialization times on the system bit error rate (BER) performance.Simulation results demonstrate that the proposed algorithm can achieve an approximate BER performance as the maximum likelihood algorithm on the basis of greatly reducing the computational complexity.Compared with the linear decoding algorithms

the proposed algorithm is also applicable to scenarios where the number of detectors is less than the number of light sources.

关键词

Keywords

references

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