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| 1 | Towards 6G wireless communication networks:vision,enabling technologies,and new paradigm shifts显示文摘The fifth generation(5G)wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized,such as mass connectivity,ultra-reliability,and guaranteed low latency.However,5G will not meet all requirements of the future in 2030 and beyond,and sixth generation(6G)wireless communication networks are expected to provide global coverage,enhanced spectral/energy/cost efficiency,better intelligence level and security,etc.To meet these requirements,6G networks will rely on new enabling technologies,i.e.,air interface and transmission technologies and novel network architecture,such as waveform design,multiple access,channel coding schemes,multi-antenna technologies,network slicing,cell-free architecture,and cloud/fog/edge computing.Our vision on 6G is that it will have four new paradigm shifts.First,to satisfy the requirement of global coverage,6G will not be limited to terrestrial communication networks,which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle(UAV)communication networks,thus achieving a space-airground-sea integrated communication network.Second,all spectra will be fully explored to further increase data rates and connection density,including the sub-6GHz,millimeter wave(mmWave),terahertz(THz),and optical frequency bands.Third,facing the big datasets generated by the use of extremely heterogeneous networks,diverse communication scenarios,large numbers of antennas,wide bandwidths,and new service requirements,6G networks will enable a new range of smart applications with the aid of artificial intelligence(AI)and big data technologies.Fourth,network security will have to be strengthened when developing 6G networks.This article provides a comprehensive survey of recent advances and future trends in these four aspects.Clearly,6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears. | Xiaohu YOU Cheng-Xiang WANG Jie HUANG Xiqi GAO Zaichen ZHANG Mao WANG Yongming HUANG Chuan ZHANG Yanxiang JIANG Jiaheng WANG Min ZHU Bin SHENG Dongming WANG Zhiwen PAN Pengcheng ZHU Yang YANG Zening LIU Ping ZHANG Xiaofeng TAO Shaoqian LI Zhi CHEN Xinying MA Chih-Lin I Shuangfeng HAN Ke LI Chengkang PAN Zhimin ZHENG Lajos HANZO Xuemin(Sherman)SHEN Yingjie Jay GUO Zhiguo DING Harald HAAS Wen TONG Peiying ZHU Ganghua YANG Jun WANG Erik GLARSSON Hien Quoc NGO Wei HONG Haiming WANG Debin HOU Jixin CHEN Zhe CHEN Zhangcheng HAO Geoffrey Ye LI Rahim TAFAZOLLI Yue GAO HVincent POOR Gerhard P.FETTWEIS Ying-Chang LIANG | 2021 | Science China(Information Sciences)2021，64，1： | 122 |
| 2 | Intelligent Wireless Communications Enabled by Cognitive Radio and Machine Learning显示文摘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. | Xiangwei Zhou Mingxuan Sun Geoffrey Ye Li Biing-Hwang (Fred) Juang | 2018 | China Communications2018，15，12： | 10 |
| 3 | Acquisition of channel state information for mm Wave massive MIMO: traditional and machine learning-based approaches显示文摘The accuracy of channel state information(CSI) acquisition directly affects the performance of millimeter wave(mmWave) communications. In this article, we provide an overview on CSI acquisition,including beam training and channel estimation for mmWave massive multiple-input multiple-output systems.The beam training can avoid the estimation of a high-dimension channel matrix, while the channel estimation can flexibly exploit advanced signal processing techniques. In addition to introducing the traditional and machine learning-based approaches in this article, we also compare different approaches in terms of spectral efficiency, computational complexity, and overhead. | Chenhao QI Peihao DONG Wenyan MA Hua ZHANG Zaichen ZHANG Geoffrey Ye LI | 2021 | Science China(Information Sciences)2021，64，8： | 3 |
| 4 | Atmospheric Ducting Effect in Wireless Communications:Challenges and Opportunities显示文摘Atmospheric ducting has a significant impact on electromagnetic wave propagation.Radio signals that are trapped and guided by the atmospheric duct can travel a much longer distance over the horizon with lower attenuation since the signal power does not spread isotropically through the atmosphere.Atmospheric ducting brings both challenges and opportunities to wireless communications.On one hand,the signals propagating in the atmospheric duct may interfere with a receiver far away as remote co-channel interference.On the other hand,a point-to-point link can be established directly through the atmospheric duct to enable beyond line-of-sight communications.In this article,the formation of the atmospheric duct and its effects on radio wave propagation are first overviewed.Then solutions and standardization activities in the 3rd Generation Partnership Project(3GPP)to mitigate atmospheric duct induced remote interference are presented.Finally,the applications and design challenges of atmospheric duct enabled beyond line-of-sight communications are reviewed and future research directions are suggested. | Fangfang Liu Jiaxi Pan Xiangwei Zhou Geoffrey Ye Li | 2021 | Journal of Communications and Information Networks2021，6，2： | 2 |
| 5 | Energy-Efficient Wireless Communications : Tutorial, Survey, and Open Issues 显示文摘 | LI GEOFFREY YE XU Zhikun XIONG Cong | 2011 | IEEE Wireless Communications2011，18，6： | 1 |
| 6 | Energy-Efficient Spectrum Access in Cognitive Radios 显示文摘 | XIONG Cong LU Lu LI GEOFFREY YE | 2014 | IEEE Journal on Selected Areas in Communications2014，32，3： | 1 |
| 7 | Energy- efficient link adaptation in frequency-selective channels 显示文摘 | Miao Guowang Himayat N Li Geoffrey Ye | 2010 | IEEE Trans Commun2010，58，2： | 1 |
| 8 | Transmitter diversity for OFDM systems and its impact on high-rate data wireless networks显示文摘 | Justin C Chuang Nelson R Sollenberger | 1999 | IEEE Journal on SAC1999，17，7： | 1 |
| 9 | Improved space-time coding for MIMO-OFDM wireless communications显示文摘 | Blum R S Li Ye(Geoffrey) Winters J H | 2001 | IEEE Trans on Com2001，49，11： | 1 |
| 10 | Adaptive antenna arrays for OFDM systems with co-channel interference显示文摘 | Nelson R Sollenberger | 1999 | IEEE Trans on Com1999，47，2： | 1 |
| 11 | Practical Approaches to Channel Estimation and Interference Suppression for OFDM-Based UWB Communications显示文摘 | MOLISCH ANDREAS F ZHANG Jinyun | 2006 | IEEE Trans on Wireless Communications2006，5，9： | 1 |
| 12 | Signal Processing in Cognitive Radio显示文摘 | Jun Ma Geoffrey Ye Li | 2009 | IEEE2009，，5： | 1 |
| 13 | Iterative Receivers for Space-Time Block-Coded OFDM Systems in Dispersive Fading Channels显示文摘 | Ben Lu Xiaodong Wang Ye (Geoffrey) Li | 2002 | IEEE Transaction on Wireless Communications2002，1，2： | 1 |
| 14 | Adaptive blind multichannel equalization for multiple signal separation显示文摘 | Ye (Geoffrey) Li and K J R Liu | 1998 | IEEE Transactions on Information Theory1998，44，6： | 1 |
| 15 | Cognitive radio networking and communications:An overview显示文摘 | YING-CHANG LIANG KWANG-CHENG CHEN GEOFFREY YE LI | 2011 | IEEE Transactions On Vehicular Technology2011，60，7： | 1 |
| 16 | Practical Considerations on Channel Estimation for Up-Link MC-CDMA Systems显示文摘 | Hua Zhang Ye(Geoffrey) Li Yi Yuan-Wu | 2008 | Ieee transactions on wireless communications2008，7，11： | 1 |
| 17 | Robust channel estimation for OFDM systems with rapid dispersive fading channels 显示文摘 | Ye (Geoffrey) Li | 1998 | IEEE Transactions on Communications1998，46，7： | 1 |
| 18 | Channel estimation for OFDM systems with transmitter diversity in mobile wireless channels 显示文摘 | Ye(Geoffrey)Li | 1999 | IEEE Journal on Selected Areas in Communicaition1999，17，3： | 1 |
| 19 | Energy- efficient wireless communications: tutorial, survey, and open issues显示文摘 | Geoffrey Ye Li Zhikun Xu Cong Xiong | | IEEE Wireless Communicafons0，2，6： | 1 |
| 20 | Cross-layer optimization for energy-efficient wireless corn munications: a survey显示文摘 | M1AO Guowang HIMAYAT N LI Geoffrey Ye | 2009 | Wireless Communications Mo- bile Comput2009，9，4： | 1 |
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