Intelligent Wireless Communications Enabled by Cognitive Radio and Machine Learning

查看全文 作　　者：Xiangwei [1]Zhou;Mingxuan [1]Sun;Geoffrey Ye [2]Li;Biing-Hwang (Fred) [2]Juang

机构地区：[1]School of EECS, Louisiana State University;[2]School of ECE, Georgia Institute of Technology

出　　处：《China Communications》索引2018年第15卷第12期,共33页

基　　金：support from the National Science Foundation under Grants 1443894,1560437,and 1731017;Louisiana Board of Regents under Grant LEQSF(2017-20)-RD-A-29;a research gift from Intel Corporation 摘　　要：The ability to intelligently utilize resources to meet the need of growing diversity in services and user behavior marks the future of wireless communication systems. Intelligent wireless communications aims at enabling the system to perceive and assess the available resources, to autonomously learn to adapt to the perceived wireless environment, and to reconfigure its operating mode to maximize the utility of the available resources. The perception capability and reconfigurability are the essential features of cognitive radio while modern machine learning techniques project great potential in system adaptation. In this paper, we discuss the development of the cognitive radio technology and machine learning techniques and emphasize their roles in improving spectrum and energy utility of wireless communication systems. We describe the state-of-the-art of relevant techniques, covering spectrum sensing and access approaches and powerful machine learning algorithms that enable spectrum and energy-efficient communications in dynamic wireless environments. We also present practical applications of these techniques and identify further research challenges in cognitive radio and machine learning as applied to the existing and future wireless communication systems. 关键词：COGNITIVE RADIO energy EFFICIENCY machine learning RECONFIGURATION spectrum EFFICIENCY

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Intelligent Wireless Communications Enabled by Cognitive Radio and Machine Learning

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