基于SNR-Means的分簇协作式频谱感知方法

赵志勇; 潘耀宗; 毛忠阳; 王甍娇; 徐建武

doi:10.11959/j.issn.1000

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基于SNR-Means的分簇协作式频谱感知方法

更新时间：2026-02-26

- 基于SNR-Means的分簇协作式频谱感知方法
- Clustered Cooperative Spectrum Sensing Based on SNR-Means
- 通信学报 2026年
- 作者机构：
  
  海军航空大学，山东烟台 264001
- 作者简介：
  
  [ "赵志勇（1976—），男，山东济南人，博士，海军航空大学教授，主要研究方向为无线通信、数据链网络、信号处理。" ]
  [ "潘耀宗（1984—），男，河北衡水人，博士，海军航空大学讲师，主要研究方向为无线通信、数据链网络、人工智能。" ]
  [ "毛忠阳（1979—），男，河南新乡人，博士，海军航空大学教授、博士生导师，主要研究方向为无线通信、人工智能、信号处理。" ]
- 基金信息：
  
  山东省自然科学基金资助项目(ZR202204300003)
- DOI：10.11959/j.issn.1000
  中图分类号： TN92
- 收稿：2025-11-20，
  
  修回：2026-02-21，
  
  录用：2026-02-26，
- 稿件说明：
移动端阅览
赵志勇, 潘耀宗, 毛忠阳, 等. 基于SNR-Means的分簇协作式频谱感知方法[J/OL]. 通信学报, 2026.

Zhao Zhiyong, Pan Yaozong, Mao Zhongyang, et al. Clustered Cooperative Spectrum Sensing Based on SNR-Means[J/OL]. Journal on Communications, 2026.
赵志勇, 潘耀宗, 毛忠阳, 等. 基于SNR-Means的分簇协作式频谱感知方法[J/OL]. 通信学报, 2026. DOI： 10.11959/j.issn.1000.

Zhao Zhiyong, Pan Yaozong, Mao Zhongyang, et al. Clustered Cooperative Spectrum Sensing Based on SNR-Means[J/OL]. Journal on Communications, 2026. DOI： 10.11959/j.issn.1000.

摘要

以K-Means为代表的硬聚类分簇方法对复杂电磁环境适应能力弱，感知性能易受干扰的影响。针对该问题，该方法摒弃了空间布局的分簇方式，从感知环境的电磁频谱特征入手，以感知节点间的接收信噪比最小误差平方和为目标函数，将感知节点划分为信噪比等距的多个簇；并综合考虑空间布局、连通性、本地感知性能等多种因素合理设置簇头；以节点的本地频谱感知能力为依据合理分配权重，加权融合各节点的感知观测数据，提高了频谱感知性能。

Abstract

Hard clustering methods

represented by K-Means

exhibited weak adaptability to complex electromagnetic environments

and their sensing performance was susceptible to the impact of interference. To address this issue

this method abandoned the spatial layout-based clustering approach and instead started from the electromagnetic spectrum characteristics of the sensing environment. Using the minimum sum of squared errors of the received signal-to-noise ratio among sensing nodes as the objective function

it partitioned the sensing nodes into multiple clusters with equidistant signal-to-noise ratios. Additionally

it comprehensively considered factors such as spatial layout

connectivity

and local sensing performance to reasonably designate cluster heads. Based on the local spectrum sensing capability of the nodes

weights were appropriately assigned

and the sensing observation data from each node were weighted and fused

thereby improving spectrum sensing performance.

关键词

Keywords

references

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