Journal ArticleDOI
Machine Learning for Vehicular Networks: Recent Advances and Application Examples
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TLDR
Recent advances in applying machine learning in vehicular networks are reviewed and an attempt is made to bring more attention to this upcoming area.Abstract:
The emerging vehicular networks are expected to make everyday vehicular operation safer, greener, and more efficient and pave the path to autonomous driving in the advent of the fifth-generation (5G) cellular system. Machine learning, as a major branch of artificial intelligence, has been recently applied to wireless networks to provide a data-driven approach to solve traditionally challenging problems. In this article, we review recent advances in applying machine learning in vehicular networks and attempt to bring more attention to this upcoming area.read more
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Journal ArticleDOI
Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts
Xiaohu You,Cheng-Xiang Wang,Jie Huang,Xiqi Gao,Zaichen Zhang,Michael 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,Pan Chengkang,Zhiming Zheng,Lajos Hanzo,Xuemin Shen,Yingjie Jay Guo,Zhiguo Ding,Harald Haas,Wen Tong,Peiying Zhu,Guanghua Yang,Jun Wang,Eric G. Larsson,Hien Quoc Ngo,Wei Hong,Haiming Wang,Debin Hou,Jixin Chen,Zhe Chen,Zhang-Cheng Hao,Geoffrey Ye Li,Rahim Tafazolli,Yue Gao,H. Vincent Poor,Gerhard P. Fettweis,Ying-Chang Liang +49 more
TL;DR: 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.
Journal ArticleDOI
6G and Beyond: The Future of Wireless Communications Systems
TL;DR: Significant technological breakthroughs to achieve connectivity goals within 6G include: a network operating at the THz band with much wider spectrum resources, intelligent communication environments that enable a wireless propagation environment with active signal transmission and reception, and pervasive artificial intelligence.
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Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future
TL;DR: A novel QC-assisted and QML-based framework for 6G communication networks is proposed while articulating its challenges and potential enabling technologies at the network infrastructure, network edge, air interface, and user end.
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Federated Learning for Internet of Things: A Comprehensive Survey
TL;DR: In this paper, a comprehensive survey of the emerging applications of federated learning in IoT networks is provided, which explores and analyzes the potential of FL for enabling a wide range of IoT services, including IoT data sharing, data offloading and caching, attack detection, localization, mobile crowdsensing and IoT privacy and security.
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Spectrum Sharing in Vehicular Networks Based on Multi-Agent Reinforcement Learning
Le Liang,Hao Ye,Geoffrey Ye Li +2 more
TL;DR: This paper investigates the spectrum sharing problem in vehicular networks based on multi-agent reinforcement learning and demonstrates that with a proper reward design and training mechanism, the multiple V2V agents successfully learn to cooperate in a distributed way to simultaneously improve the sum capacity of V2I links and payload delivery rate of V1V links.
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