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| 1 | 6G Visions:Mobile Ultra-Broadband,Super Internet-of-Things,and Artificial Intelligence显示文摘With a ten-year horizon from concept to reality, it is time now to start thinking about what will the sixth-generation(6G) mobile communications be on the eve of the fifth-generation(5G) deployment. To pave the way for the development of 6G and beyond, we provide 6G visions in this paper. We first introduce the state-of-the-art technologies in 5G and indicate the necessity to study 6G. By taking the current and emerging development of wireless communications into consideration, we envision 6G to include three major aspects, namely, mobile ultra-broadband, super Internet-of-Things(IoT), and artificial intelligence(AI). Then, we review key technologies to realize each aspect. In particular, teraherz(THz) communications can be used to support mobile ultra-broadband, symbiotic radio and satellite-assisted communications can be used to achieve super IoT, and machine learning techniques are promising candidates for AI. For each technology, we provide the basic principle, key challenges, and state-of-the-art approaches and solutions. | Lin Zhang Ying-Chang Liang Dusit Niyato | 2019 | China Communications2019，16，8： | 52 |
| 2 | 智能无线通信技术研究概况显示文摘近年来,人工智能技术已被应用于无线通信领域,以解决传统无线通信技术面对信息爆炸和万物互联等新发展趋势所遇到的瓶颈问题。首先介绍深度学习、深度强化学习和联邦学习三类具有代表性的人工智能技术;然后通过对这三类技术在无线通信中的无线传输、频谱管理、资源配置、网络接入、网络及系统优化5个方面的应用进行综述,分析和总结它们在解决无线通信问题时的原理、适用性、设计方法和优缺点;最后围绕存在的局限性指出智能无线通信技术的未来发展趋势和研究方向,期望为无线通信领域的后续研究提供帮助和参考。 | 梁应敞 谭俊杰 Dusit Niyato | 2020 | 通信学报2020，41，7： | 23 |
| 3 | Federated Learning for 6G Communications:Challenges,Methods,and Future Directions显示文摘As the 5G communication networks are being widely deployed worldwide,both industry and academia have started to move beyond 5G and explore 6G communications.It is generally believed that 6G will be established on ubiquitous Artificial Intelligence(AI)to achieve data-driven Machine Learning(ML)solutions in heterogeneous and massive-scale networks.However,traditional ML techniques require centralized data collection and processing by a central server,which is becoming a bottleneck of large-scale implementation in daily life due to significantly increasing privacy concerns.Federated learning,as an emerging distributed AI approach with privacy preservation nature,is particularly attractive for various wireless applications,especially being treated as one of the vital solutions to achieve ubiquitous AI in 6G.In this article,we first introduce the integration of 6G and federated learning and provide potential federated learning applications for 6G.We then describe key technical challenges,the corresponding federated learning methods,and open problems for future research on federated learning in the context of 6G communications. | Yi Liu Xingliang Yuan Zehui Xiong Jiawen Kang Xiaofei Wang Dusit Niyato | 2020 | China Communications2020，17，9： | 21 |
| 4 | 基于多智能体强化学习的区块链赋能车联网中的安全数据共享显示文摘针对基于委托权益证明(Delegated Proof-of-Stake,DPoS)共识算法的区块链赋能车联网系统中区块验证的安全性与可靠性问题,矿工通过引入轻节点(如智能手机等边缘节点)共同参与区块验证,提高区块验证的安全性和可靠性。为了激励矿工主动引入轻节点,采用了斯坦伯格(Stackelberg)博弈模型对区块链用户与矿工进行建模,实现区块链用户的效用和矿工的个人利润最大化。作为博弈主方的区块链用户设定最优的区块验证的交易费,而作为博弈从方的矿工决定最优的招募验证者(即轻节点)的数量。为了找到所设计Stackelberg博弈的纳什均衡,设计了一种基于多智能体强化学习算法来搜索接近最优的策略。最后对本文方案进行验证,结果表明该方案既能实现区块链用户和矿工效益最大化,也能保证区块验证的安全性与可靠性。 | 李明磊 章阳 康嘉文 徐敏锐 Dusit Niyato | 2021 | 广东工业大学学报2021，38，6： | 5 |
| 5 | 6G-Enabled Edge AI for Metaverse:Challenges, Methods,and Future Research Directions显示文摘Sixth generation(6G)enabled edge intelligence opens up a new era of Internet of everything and makes it possible to interconnect people-devices-cloud anytime,anywhere.More and more next-generation wireless network smart service applications are changing our way of life and improving our quality of life.As the hottest new form of next-generation Internet applications,Metaverse is striving to connect billions of users and create a shared world where virtual and reality merge.However,limited by resources,computing power,and sensory devices,Metaverse is still far from realizing its full vision of immersion,materialization,and interoperability.To this end,this survey aims to realize this vision through the organic integration of 6G-enabled edge artificial intelligence(AI)and Metaverse.Specifically,we first introduce three new types of edge-Metaverse architectures that use 6G-enabled edge AI to solve resource and computing constraints in Metaverse.Then we summarize technical challenges that these architectures face in Metaverse and the existing solutions.Furthermore,we explore how the edge-Metaverse architecture technology helps Metaverse to interact and share digital data.Finally,we discuss future research directions to realize the true vision of Metaverse with 6G-enabled edge AI. | Luyi Chang Zhe Zhang Pei Li Shan Xi Wei Guo Yukang Shen Zehui Xiong Jiawen Kang Dusit Niyato Xiuquan Qiao Yi Wu | 2022 | Journal of Communications and Information Networks2022，7，2： | 2 |
| 6 | Auction-Based Resource Allocation in Cognitive Radio Systems显示文摘 | ZHANG Yang NIYATO D WANG Ping | 2012 | IEEE Communications Magazine2012，50，11： | 1 |
| 7 | Channel status prediction for cognitive radio networks 显示文摘 | TUMULURU V K WANG P NIYATO D | 2010 | Wireless Communications and Mobile Computing2010，12，10： | 1 |
| 8 | Optimal channel access man- agement with QoS support for cognitive vehicular networks显示文摘 | NIYATO D HOSSAIN E WANG P | 2011 | IEEE Transactions on Mobile Computing2011，10，5： | 1 |
| 9 | Competitive Spectrum Shaaring in Cognitive Radio Netwrks:A Dynamic Game Approach显示文摘 | Niyato D Hossain E | 2008 | IEEE Transac- tions on Wireless Communieations2008，7，7： | 1 |
| 10 | Optimization of resourceprovisioning cost in cloud computing 显示文摘 | Chaisiri S Lee B S Niyato D | 2012 | IEEE Trans onServices Computing2012，5，2： | 1 |
| 11 | Competitive pricing for spectrum sharing in cognitive radio networks: dynamic game, inefficiency of Nash equilibrium, and collusion 显示文摘 | Niyato D Hossain E | 2008 | IEEE Journal on Selected Areas in Communications2008，26，1： | 1 |
| 12 | Dynamics of network selection in heterogeneous wireless networks:an evolutionary game approach显示文摘 | NIYATO D HOSSAIN E | 2009 | IEEE Transactions on Vehicular Technology2009，58，4： | 1 |
| 13 | Call-level and packet-level quality of service and user utility in rate-adaptive cellular CDMA networks :a queuing anal ysis显示文摘 | Niyato D Hossain E | 2006 | IEEE Transactions on Mobile Computing2006，5，12： | 1 |
| 14 | Competitive Spectrum Sharing inCognitive Radio Networks: A Dynamic Game Approach显示文摘 | Niyato D Hossain E | 2008 | IEEE Transactions on Wireless Communications2008，7，7： | 1 |
| 15 | Game theoretic analysis for spectrum sharing with multi-hop relaying显示文摘 | Xiao Y 13i G Niyato D | 2011 | IEEE Transactions on Wireless Communications2011，10，5： | 1 |
| 16 | Game-theoretic resource allocation methods for device-to-device communication显示文摘 | Song L Niyato D Han D | 2014 | IEEE Wireless Communications2014，21，3： | 1 |
| 17 | Non-cooperative game-theoretic framework for radio resource management in 4G heterogeneous wireless access networks 显示文摘 | Niyato D Hossain E A | 2008 | 1EEE Transactions on Mobile Computing2008，7，3： | 1 |
| 18 | Machine to machine communications for home energy management system in smart grid显示文摘 | Niyato D Lu Xiao Wang Ping | 2011 | IEEE Communications Magazine2011，49，4： | 1 |
| 19 | Pricing, spectrum sha-ring ,and service selection in two-tier small cell net-works :a hierarchical dynamic game approach 显示文摘 | Zhu Kun Hossain E Niyato D | 2014 | IEEE Transactions on Mobile Computing2014，13，8： | 1 |
| 20 | Channel status prediction for cognitive radio networks显示文摘 | Tumuluru V K Wang P Niyato D | 2012 | Wireless Communications and Mobile Computing2012，12，10： | 1 |
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