文章基本信息

标题：Reinforcement Learning Based Anti-Jamming Schedule in Cyber-Physical Systems
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作者：Ruimeng Gan ; Yue Xiao ; Jinliang Shao 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：2501-2506
DOI：10.1016/j.ifacol.2020.12.221
语种：English
出版社：Elsevier
摘要：AbstractIn this paper, the security issue of cyber-physical systems is investigated, where the observation data is transmitted from a sensor to an estimator through wireless channels disturbed by an attacker. The failure of this data transmission occurs, when the sensor accesses the channel that happens to be attacked by the jammer. Since the system performance measured by the estimation error depends on whether the data transmission is a success, the problem of selecting the channel to alleviate the attack effect is studied. Moreover, the state of each channel is time-variant due to various factors, such as path loss and shadowing. Motivated by energy conservation, the problem of selecting the channel with the best state is also considered. With the help of cognitive radio technique, the sensor has the ability of selecting a sequence of channels dynamically. Based on this, the problem of selecting the channel is resolved by means of reinforcement learning to jointly avoid the attack and enjoy the channel with the best state. A corresponding algorithm is presented to obtain the sequence of channels for the sensor, and its effectiveness is proved analytically. Numerical simulations further verify the derived results.
关键词：KeywordsCyber-physical systems (CPSs)reinforcement learningcognitive radiosoftmax method