基于可重构智能表面的6G通信技术

Techniques for Reconfigurable Intelligent Surface-Aided 6G Communication Network:An Overview

可重构智能表面技术是面向下一代6G无线通信网络的关键技术之一。通过在天线阵列上集成大量无源反射超材料的天线元件,可重构智能表面能动态调整入射信号相位,重构信号的传播环境,实现人工可控的无线电磁环境。然而,可重构智能表面的引入也使无线通信系统的设计变得复杂,传统技术面临诸多新挑战。针对可重构智能表面的特性与6G新应用场景,需要创新设计新的传输技术,充分发挥可重构智能表面的优势。本文基于可重构智能表面6G通信技术,首先对其基本技术原理与常见应用场景展开介绍。接着,从信道建模与信道估计、混合波束赋形,以及与基于人工智能的融合设计三个方面阐述技术的研究现状,探讨了当前面临的技术瓶颈。最后,对该技术的未来发展进行了展望。

Reconfigurable intelligent surface(RIS)is viewed as a key technology for the next-generation 6G wireless communication system.By integrating a large number of low-cost passive reflecting elements on the antenna array,RIS can adjust the incident signals dynamically and reshape the wireless propagation environment,in which case an artificially controllable electromagnetic environment becomes a reality.Higher rate data traffic and network capacity,and the more intelligent network paradigms are the key requirements for 6G networks.The construction of smart radio environments can further help greatly improve the performance in terms of spectrum and energy efficiency,and support more intelligent designs in 6G networks.However,the introduction of RIS also significantly complicates the design of wireless communication networks,and the conventional techniques face much more new challenges and are even no longer applicable.In view of the characteristics and different application scenarios of RIS in 6G network,it is necessary to innovate and design new transmission techniques to fully unlock the potential of RIS deployments.Hence,we first introduced the basic concepts and the hardware framework of RIS,as well as its classification.In addition,the current various applications of RIS in wireless communication systems was further classified,and its five main application scenarios were summarized.Then,from the perspective of the techniques of the RIS-aided 6G network,we surveyed from the three aspects of channel modeling and channel estimation,hybrid beamforming,and the integration of artificial intelligence(AI)with RIS in 6G.The common RIS-related channel and path loss modeling methods were fully introduced.We then introduced the conventional techniques and the new ones,especially the AI based techniques,for channel estimation and hybrid beamforming in detail,together with some main challenges.Moreover,we focused on the design of RIS-aided systems with integrated sensing and communication(ISAC)and the deep integration of RIS and AI technology,as well as the construction of the RIS-assisted intelligent networks.Finally,the current application of RIS in practical systems and crucial problems to address were discussed and the potential future developments of the RIS technology were presented.