基于卷积神经网络的超宽带信道环境的分类算法  

Ultra-wideband channel environment classification algorithm based on CNN

作　　者：杨亚楠 夏斌[1] 赵磊[1] 袁文浩[1] YANG Yanan;XIA Bin;ZHAO Lei;YUAN Wenhao(School of Computer Science and Technology, Shandong University of Technology, Zibo Shandong 255049, China)

机构地区：[1]山东理工大学计算机科学与技术学院,山东淄博255049

出　　处：《计算机应用》2019年第5期1421-1424,共4页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61701286);山东省自然科学基金资助项目(ZR2017MF047)~~

摘　　要：针对非视距(NLOS)状态鉴别需要已知信道类型的分类的问题,提出了一种基于卷积神经网络(CNN)的信道环境分类算法。首先,对超宽带(UWB)信道进行采样,构建样本集合;然后,利用样本集合训练CNN,对不同的信道场景特征进行提取;最终实现超宽带信道环境的分类。实验结果表明:所采用的分类方法的总模型准确率约为93.40%,能有效地实现信道环境的分类识别。To solve the problem that Non Line Of Sight(NLOS) state identification requires classification of known channel types, a channel environment classification algorithm based on Convolutional Neural Network(CNN) was proposed. Firstly, an Ultra-WideBand(UWB) channel was sampled, and a sample set was constructed. Then, a CNN was trained by the sample set to extract features of different channel scenes. Finally, the classification of UWB channel environment was realized. The experimental results show that the overall accuracy of the model using the proposed algorithm is about 93.40% and the algorithm can effectively realize the classification of channel environments.

关 键 词：非视距 卷积神经网络 信道环境 超宽带 BP网络 

分 类 号：TN911[电子电信—通信与信息系统]