Recently, inspired by successful applications in many fields, deep learning (DL) technologies for CSI acquisition have received considerable research interest from both academia and industry. Considering the practical feedback mechanism of 5th generation (5G) New radio (NR) networks, we propose two implementation schemes for artificial intelligence for CSI (AI4CSI), the DL-based receiver and end-to-end design, respectively. The proposed AI4CSI schemes were evaluated in 5G NR networks in terms of spectrum efficiency (SE), feedback overhead, and computational complexity, and compared with legacy schemes. To demonstrate whether these schemes can be used in real-life scenarios, both the modeled-based channel data and practically measured channels were used in our investigations. When DL-based CSI acquisition is applied to the receiver only, which has little air interface impact, it provides approximately 25\% SE gain at a moderate feedback overhead level. It is feasible to deploy it in current 5G networks during 5G evolutions. For the end-to-end DL-based CSI enhancements, the evaluations also demonstrated their additional performance gain on SE, which is 6% -- 26% compared with DL-based receivers and 33% -- 58% compared with legacy CSI schemes. Considering its large impact on air-interface design, it will be a candidate technology for 6th generation (6G) networks, in which an air interface designed by artificial intelligence can be used.
翻译:最近,在很多领域的成功应用的启发下,用于获取集装箱安全倡议的深层次学习(DL)技术在学术界和工业界都引起了相当大的研究兴趣。考虑到第五代(5G)新无线电(NR)网络的实用反馈机制,我们提出了两个分别用于集装箱安全倡议(AI4CSI)、基于DL的接收器和端到端设计人工智能的实施计划。拟议的AI4CSI计划在5G NRC网络中从频谱效率(SE)、反馈间接费用和计算复杂性方面进行了评价,并与遗留方案进行比较。为了证明这些计划能否在现实生活中使用,在我们的调查中使用了基于模型的频道数据和实际计量的渠道。当基于DLSI的获取仅用于接收器(AI4CSI)的人工智能(AI4CSI)系统(AI4CSI)和端到端到端到端设计设计,分别提供了大约25°SE的中度反馈水平。在5G的演进过程中,在目前的5G网络中可以将其部署到端端际网络。对于基于DL的终端接口的强化,评价还表明它们在SE上的额外业绩收益收益增加6 %,而DL的网络将使用为58%,而应用为C-33级的模型的模型的模型将使用。