标题:UWB Channel Classification Based on Deep-Learning for Adaptive Transmission
作者:Lu, Zecheng ;Wang, Deqiang ;Qin, Yunrui ;Liu, Ran
通讯作者:Wang, Deqiang
作者机构:[Lu, Zecheng ;Wang, Deqiang ;Qin, Yunrui ;Liu, Ran ] School of Information Science and Engineering, Shandong University, Qindao; 266237, China
会议名称:12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019
会议日期:19 October 2019 through 21 October 2019
来源:Proceedings - 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019
出版年:2019
DOI:10.1109/CISP-BMEI48845.2019.8966054
关键词:Channel classification; channel model; deep neural networks; UWB system
摘要:In indoor environments, the throughput of ultrawideband (UWB) systems depends much on the channel condition. In this paper, a two-stage adaptive UWB transmission scheme is proposed to improve the throughput, where deep-learning is used to realize channel classification. In the first stage, the channel type is identified by using a newly designed channel classifier which i s based o n deep neural networks (DNN) and trained with IEEE 802.15.3a channel model data set. In the second stage, the transmission rate is adjusted to match the channel condition and thus improve the throughput. To gain accurate channel classification, an ensemble decision layer is used jointly with the DNN-based channel classifier. Simulation results show that attractive classification accuracy c an b e a chieved by using the proposed channel classifer and the throughput of UWB system can be improved effectively. © 2019 IEEE.
收录类别:EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079190254&doi=10.1109%2fCISP-BMEI48845.2019.8966054&partnerID=40&md5=0508f806c69a301159435454c4595d02
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