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Titlebook: Communications, Signal Processing, and Systems; Proceedings of the 2 Qilian Liang,Xin Liu,Baoju Zhang Conference proceedings 2020 Springer

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书目名称Communications, Signal Processing, and Systems
副标题Proceedings of the 2
编辑Qilian Liang,Xin Liu,Baoju Zhang
视频videohttp://file.papertrans.cn/231/230557/230557.mp4
概述Brings together the papers presented at the 2018 International Conference on Communications, Signal Processing, and Systems held in Dalian, China on July 14–16, 2018.Includes the latest developments i
丛书名称Lecture Notes in Electrical Engineering
图书封面Titlebook: Communications, Signal Processing, and Systems; Proceedings of the 2 Qilian Liang,Xin Liu,Baoju Zhang Conference proceedings 2020 Springer
描述.This book brings together papers from the 2018 International Conference on Communications, Signal Processing, and Systems, which was held in Dalian, China on July 14–16, 2018. Presenting the latest developments and discussing the interactions and links between these multidisciplinary fields, the book spans topics ranging from communications, signal processing and systems. It is aimed at undergraduate and graduate electrical engineering, computer science and mathematics students, researchers and engineers from academia and industry as well as government employees..
出版日期Conference proceedings 2020
关键词CSPS 2018 proceedings; Wireless Communications; Internet of Things; Signal Processing for Communication
版次1
doihttps://doi.org/10.1007/978-981-13-6508-9
isbn_softcover978-981-13-6510-2
isbn_ebook978-981-13-6508-9Series ISSN 1876-1100 Series E-ISSN 1876-1119
issn_series 1876-1100
copyrightSpringer Nature Singapore Pte Ltd. 2020
The information of publication is updating

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Helen W. Dodson,E. Ruth Hedemannsists of the Internet of vehicles (IoV) and the original IoT network. Because the speed of vehicle is very fast, the number of users is constantly changing in the IoT network, which leads the structure of the IoT network to change. We mainly consider how to obtain the channel state information (CSI
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Oksana Mandrikova,Nadezhda Fetisovathe convergence rate of learning automaton and has made significant achievements. However, the estimators perform poorly on estimating actions’ reward probabilities in the initial stage of the learning process. In this situation, a lot of rewards would be assigned to nonoptimal actions. Thus, numero
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