面向6G太赫兹无线通信的关键机器学习技术综述(特邀)
An Overview of Key Machine Learning Technologies in 6G-Oriented Terahertz Wireless Communication Systems(Invited)
周雯 1徐思聪1
作者信息
- 1. 复旦大学通信科学与工程系电磁波信息科学教育部重点实验室,上海 200433
- 折叠
摘要
太赫兹技术作为6G时代的核心通信技术,可以有效解决频段资源日益稀缺的窘境,以满足流量、连接数急剧增长的需求,实现更大的传输带宽.现有机器学习算法如深度神经网络、卷积神经网络和长短期记忆网络等,均可以有效缓解6G传输系统中的强非线性效应,是实现6G太赫兹无线通信的重要手段.介绍了应用于光子毫米波和太赫兹无线传输系统的不同深度学习范式,讨论了应用光子技术办法产生超高速THz波无线信号的国内外主要进展及技术路线对比,并且对较为经典的或是目前最新的应用于太赫兹通信系统中的人工智能技术进行了介绍,同时对未来大速度、高容量的太赫兹通信技术的发展提出了展望.
Abstract
As the core communication technology in the 6G era,terahertz technology can effectively address the challenge of increasingly diminishing frequency band resources.It caters to the rapidly growing demand for traffic and connections while enabling large transmission bandwidth.Machine learning algorithms,such as deep neural networks,convolutional neural networks,and short-term memory networks,play a pivotal role in mitigating strong nonlinear effects within 6G transmission systems and are crucial tools for realizing 6G terahertz wireless communication.This review delves into diverse deep learning paradigms implemented in the photonic millimeter wave and terahertz wireless transmission systems.It highlights the notable strides made in leveraging photonic technology for generating ultra-high-speed terahertz wave wireless signals on domestic and international fronts.The paper provides a comparative analysis of different technical approaches.Additionally,the review offers a comprehensive view of traditional and emerging artificial intelligence technologies applied to terahertz communication systems.Finally,it outlines future development directions for terahertz communication technology,focusing on achieving high-speed and high-capacity performance.
关键词
太赫兹无线通信/6G/人工智能/机器学习/神经网络/光纤通信Key words
terahertz wireless communication/6G/artificial intelligence/machine learning/neural network/optical fiber communication引用本文复制引用
基金项目
国家重点研发计划(2018YFB1801703)
国家自然科学基金(62127802)
出版年
2024
NETL
NSTL
万方数据