基于大数据挖掘的光通信微弱信号检测研究
Research on weak signal detection in optical communication based on big data mining
董妮娅 1林毅2
作者信息
- 1. 重庆移通学院公共大数据安全技术重庆市重点实验室,重庆 401520
- 2. 重庆邮电大学通信与信息工程学院,重庆 400065
- 折叠
摘要
以高效、准确检测噪声淹没下光通信微弱信号为目的,设计基于大数据挖掘的光通信微弱信号检测方法.通过基于改进EMD与奇异值分解全面去除光通信信号中噪声分量,经灰狼算法寻优设置支持向量机参数后,由支持向量机模型构建信号分类超平面,分类检测样本中微弱信号.实验结果表明:光通信信号经所提方法去噪后,信号信噪比变小,最大值仅有0.01 dB;所提方法所检测的微弱信号波动幅值,与微弱信号实际幅值高度匹配,误差不超过1％,可100％检测出噪声淹没下光通信微弱信号的样本.
Abstract
Design an optical communication weak signal detection method based on big data mining with the aim of efficiently and accurately detecting weak signals submerged in noise in optical communication.The noise component in the optical communication signal is completely removed based on improved EMD and singular value decomposition.Af-ter the parameters of the support vector machine are optimized by the gray wolf algorithm,the signal classification hy-perplane is constructed from the support vector machine model,and the weak signals in the samples are classified and detected.The experimental results show that after denoising the optical communication signal using the proposed meth-od,the signal-to-noise ratio of the signal decreases,with a maximum value of only 0.01 dB.The fluctuation ampli-tude of the weak signal detected by the proposed method highly matches the actual amplitude of the weak signal,with an error of no more than 1％,100％detect samples of weak optical communication signals submerged in noise.
关键词
大数据挖掘/光通信/微弱信号/改进EMD算法/奇异值分解/支持向量机Key words
big data mining/optical communication/weak signal/improve the EMD algorithm/singular value decomposition/support vector machine引用本文复制引用
基金项目
重庆市教委科学技术研究计划青年项目(KJQN202202401)
出版年
2024
NETL
NSTL
万方数据