基于卷积神经网络的OFDM-UWB信道环境识别

doi:10.19678/j.issn.1000-3428.0058184

计算机工程 ›› 2021, Vol. 47 ›› Issue (7): 161-167. doi: 10.19678/j.issn.1000-3428.0058184

基于卷积神经网络的OFDM-UWB信道环境识别

王斐¹, 徐湛^1,2, 职如昕^1,2, 陈晋辉^1,2

1. 北京信息科技大学信息与通信工程学院, 北京 100101;
2. 北京信息科技大学现代测控技术教育部重点实验室, 北京 100101

收稿日期:2020-04-27 修回日期:2020-07-01 发布日期:2021-07-15
作者简介:王斐(1996-),男,硕士研究生,主研方向为信息与通信系统、无线通信;徐湛,教授、博士;职如昕,实验师、博士;陈晋辉,助理研究员、博士。
基金资助:
北京市科技计划课题项目（Z191100001419001）；北京市优秀人才资助计划青年拔尖项目（2016000026833ZK08）；北京市属高校高水平教师队伍建设支持计划（CIT&TCD201704065）。

OFDM-UWB Channel Environment Recognition Based on Convolutional Neural Network

WANG Fei¹, XU Zhan^1,2, ZHI Ruxin^1,2, CHEN Jinhui^1,2

1. School of Information and Communication Engineering, Beijing Information Science and Technology University, Beijing 100101, China;
2. Key Laboratory of Modern Measurement and Control Technology of Ministry of Education, Beijing Information Science and Technology University, Beijing 100101, China

Received:2020-04-27 Revised:2020-07-01 Published:2021-07-15

摘要/Abstract

摘要： 超宽带（UWB）无线通信技术被广泛应用于室内定位领域，其能识别出多径信道中的非视距信道，有助于去除影响信号的非理想因素，提升定位精度。基于OFDM方案的UWB系统，提出一种利用卷积神经网络对信道估计出的信道冲激响应时频图像进行训练，从而识别出信道环境的方法，将信道识别问题转化为图像识别问题，同时分析时频处理参数对识别结果的影响。仿真结果表明，该方法的识别率随通信系统比特信噪比（EbN0）的增加而提升，当EbN0增加至20 dB时稳定在90%，与传统基于支持向量机的信道识别方法相比获得了10%的性能提升。

关键词: 超宽带无线通信, 非视距信道识别, 冲激响应, 卷积神经网络, 短时傅里叶变换

Abstract: Widely used in the field of indoor positioning,the Ultra-WideBand(UWB) wireless communication technology can identify the Non-Line of Sight(NLOS) channel in the multi-path channel,helping in removing non-ideal factors and improving positioning accuracy.Based on the OFDM-UWB system,this paper proposes a LOS/NLOS channel recognition method.The method employs a Convolutional Neural Network(CNN) to train the estimated time-frequency images of channel impulse responses,transforming the channel recognition problem into an image recognition problem.In addition,the influence of time-frequency processing parameters on the recognition results is analyzed.The simulation results show that the recognition rate of this method increases with the bit signal-to-noise ratio(EbN0) of the communication system,and is stable at 90% when EbN0 increases to 20 dB.At the same time,the method provides 10% performance improvement compared with the traditional channel recognition method based on Support Vector Machine(SVM).

Key words: Ultra-WideBand(UWB) wireless communication, Non-Line of Sight(NLOS) channel recognition, impulse response, Convolutional Neural Network(CNN), Short-Time Fourier Transform(STFT)

中图分类号:

TN911

王斐, 徐湛, 职如昕, 陈晋辉. 基于卷积神经网络的OFDM-UWB信道环境识别[J]. 计算机工程, 2021, 47(7): 161-167.

WANG Fei, XU Zhan, ZHI Ruxin, CHEN Jinhui. OFDM-UWB Channel Environment Recognition Based on Convolutional Neural Network[J]. Computer Engineering, 2021, 47(7): 161-167.

http://www.ecice06.com/CN/Y2021/V47/I7/161

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