基于密度增量聚类的无线电频谱实时监测方法研究  

Research on Real-Time Radio Spectrum Monitoring Method Based on Density Incremental Clustering

作　　者：曹人予 CAO Renyu(NanningWuxu International Airport,Nanning,Guangxi 530000)

机构地区：[1]南宁吴圩国际机场,广西南宁530000

出　　处：《长江信息通信》2025年第2期149-151,共3页Changjiang Information & Communications

摘　　要：文章提出一种基于密度增量聚类的无线电频谱实时监测方法。该方法通过设计自适应密度阈值的增量聚类算法,实现对5G/6G场景下高速流动频谱数据的智能分析。系统采用“边缘-云端”混合架构,结合深度学习技术实现信号特征提取和自动分类。实验结果表明,在典型5G应用场景中,该方法较传统DBSCAN算法的聚类准确率提升20%以上,处理延迟降低至亚毫秒级,为复杂电磁环境下的频谱资源智能化管理提供了新的技术方案。This paper proposes a real-time radio spectrum monitoring method based on density incremental clustering.The method realizes intelligent analysis of high-speed mobile spectrum data in 5G/6G scenarios by designing an incremental clustering algorithm with adaptive density threshold.The system adopts an“edge-cloud”hybrid architecture,and combines deep learning techniques to realize signal feature extraction and automatic classification.Experimental results show that in typical 5G application scenarios,the clustering accuracy of this method is improved by more than 20%compared with the traditional DBSCAN algorithm,and the processing delay is reduced to sub-milliseconds,which provides a new technological solution for the intelligent management of spectrum resources in complex electromagnetic environments.

关 键 词：密度增量 聚类算法 无线电通信技术 频谱监测方法 

分 类 号：TN925[电子电信—通信与信息系统]