Intelligent Wireless Communications Enabled by Cognitive Radio and Machine Learning

作     者：Xiangwei Zhou Mingxuan Sun Geoffrey Ye Li Biing-Hwang (Fred) Juang 

作者机构：School of EECS Louisiana State University School of ECE Georgia Institute of Technology 

出 版 物：《China Communications》 (中国通信（英文版）)

年 卷 期：2018年第15卷第12期

页      面：16-48页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 0839[工学-网络空间安全] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

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 

主　　题：cognitive radio energy efficiency machine learning reconfiguration spectrum efficiency 

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.