基于卷积神经网络的超宽带信道环境的分类算法

doi:10.11772/j.issn.1001-9081.2018071516

计算机应用 ›› 2019, Vol. 39 ›› Issue (5): 1421-1424.DOI: 10.11772/j.issn.1001-9081.2018071516

基于卷积神经网络的超宽带信道环境的分类算法

杨亚楠, 夏斌, 赵磊, 袁文浩

山东理工大学计算机科学与技术学院, 山东淄博 255049

收稿日期:2018-07-23 修回日期:2018-09-17 出版日期:2019-05-10 发布日期:2019-05-14
通讯作者: 夏斌
作者简介:杨亚楠(1992-),女,山东枣庄人,硕士研究生,主要研究方向:深度学习;夏斌(1973-),男,山东淄博人,副教授,博士,主要研究方向:深度学习、无线通信;赵磊(1964-),男,山东淄博人,教授,博士,主要研究方向:移动网络、人工智能;袁文浩(1985-),男,山东潍坊人,讲师,博士,主要研究方向:信号处理。
基金资助:
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）。

Ultra-wideband channel environment classification algorithm based on CNN

YANG Yanan, XIA Bin, ZHAO Lei, YUAN Wenhao

School of Computer Science and Technology, Shandong University of Technology, Zibo Shandong 255049, China

Received:2018-07-23 Revised:2018-09-17 Online:2019-05-10 Published:2019-05-14
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61701286), the Natural Science Foundation of Shandong Province (ZR2017MF047).

摘要/Abstract

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。

关键词: 非视距, 卷积神经网络, 信道环境, 超宽带, BP网络

Abstract: 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.

Key words: Non Line Of Sight (NLOS), Convolutional Neural Network (CNN), channel environment, Ultra-WideBand (UWB), Back-Propagation (BP) network

中图分类号:

TN911

杨亚楠, 夏斌, 赵磊, 袁文浩. 基于卷积神经网络的超宽带信道环境的分类算法[J]. 计算机应用, 2019, 39(5): 1421-1424.

YANG Yanan, XIA Bin, ZHAO Lei, YUAN Wenhao. Ultra-wideband channel environment classification algorithm based on CNN[J]. Journal of Computer Applications, 2019, 39(5): 1421-1424.

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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

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