吉林大学学报（信息科学版）2026，Vol.44Issue(2)：270-275,6.
基于k-Means聚类算法的医院隐蔽性网络抗干扰通信
Anti-Jamming Communication in Hospital Covert Network Based on k-Means Clustering Algorithm
摘要
Abstract
Due to the large number of radio equipment and medical devices in hospitals,a large amount of electromagnetic interference is generated,causing serious interference to the communication quality.In order to improve the communication performance of hospital networks,an anti interference communication method for hospital covert networks based on unsupervised learning is proposed.Through preprocessing the interference signal,the time domain moment kurtosis coefficient,frequency domain moment kurtosis coefficient,single frequency energy aggregation degree,and average spectrum flatness coefficient are selected as the characteristic parameters of the interference signal.The unsupervised learning algorithm-k-means clustering algorithm is introduced,the characteristics of the interference signal is extracted,time domain and frequency domain interference signal suppression algorithms is developed,and the interference signal in network communication is suppressed.Experimental results show that the bit error probability of the proposed method reaches a stable state of 2.4％,and the minimum proportion of interference signals is 1.29％,which meets the application requirements of interference signal suppression.关键词
干扰信号/网络通信/无监督学习/隐蔽性网络/抗干扰性能Key words
interference signal/network communications/unsupervised learning/hidden network/anti interference performance分类
信息技术与安全科学引用本文复制引用
王润..基于k-Means聚类算法的医院隐蔽性网络抗干扰通信[J].吉林大学学报（信息科学版）,2026,44(2):270-275,6.基金项目
河南省社会科学界联合会青少年工作研究专项调研课题基金资助项目(QSNYJ2020003) （QSNYJ2020003）
