电波科学学报2024，Vol.39Issue(3)：432-441,10.DOI:10.12265/j.cjors.2023126
面向无人机通信网络的信道全域特性空间聚类和识别
Space clustering and identification based on full-domain channel characteristics for UAV communication networks
摘要
Abstract
In order to improve the stability and reliability of unmanned aerial vehicle(UAV)communication networks,a channel subspace clustering and identification scheme based on channel full-domain characteristics is proposed in this paper.Firstly,the channels are characterized completely using distance domain,time delay domain,spatial domain and Doppler domain characteristics,and a channel subspace clustering method is proposed to form the channel subspace of channels with similar full domain characteristics as a basis for classification of UAV communication scenarios.Then,a channel subspace identification method based on back-propagation neural network is proposed,which can determine whether the new channel data belongs to the structure of the original channel subspace and channel full-domain characteristics are used as the feature tensor to improve the identification accuracy.Meanwhile,the influence of training data outliers is eliminated by calculating the distance between the channel and the center of the channel subspace,thus improving the robustness of identification method.Finally,176 typical digital city scenarios are simulated in this paper by shooting and bounce ray/image mirror to obtain the full-domain characteristics of 176 000 channels and the corresponding channel state information,which are used to verify the accuracy of the clustering and identification method proposed in this paper.Simulation results show that the scenario identification method proposed in this paper can reduce the 176 identification targets of the traditional scenario identification method to 20,and the accuracy of the channel state characteristics in the channel subspace and identification method reaches 99％and 98.7％.Therefore,the method proposed in this paper can accurately identify the channel subspace in the UAV communication and provide a basis for UAV communication performance optimization.关键词
无人机/信道子空间/信道全域特性/聚类和识别/特征张量Key words
unmanned aerial vehicle/channel subspace/full-domain channel characteristics/clustering and identification/feature tensor分类
信息技术与安全科学引用本文复制引用
朱古月,李双德,刘芫健,朱秋明,张静怡,毛开,周哲豪..面向无人机通信网络的信道全域特性空间聚类和识别[J].电波科学学报,2024,39(3):432-441,10.基金项目
南京邮电大学引进人才自然科学研究启动基金(NY222059) （NY222059）
国家自然科学基金(62371248) （62371248）
江苏省研究生科研与实践创新计划(SJCX20_0247) （SJCX20_0247）
