AIRIS: Artificial Intelligence Enhanced Signal Processing in Reconfigurable Intelligent Surface Communications

被引:0
|
作者
Shun Zhang [1 ]
Muye Li [1 ]
Mengnan Jian [2 ]
Yajun Zhao [2 ]
Feifei Gao [3 ]
机构
[1] State Key Laboratory of Integrated Services Networks, Xidian University
[2] Algorithm Department of Wireless Product R&D Institute, ZTE Corporation
[3] Institute for Artificial Intelligence, Tsinghua University (THUAI), State Key Lab of Intelligent Technologies and Systems, Tsinghua University, Beijing National Research Center for Information Science and Technology(BNRist) , Department of Automation, Tsin
基金
中国国家自然科学基金;
关键词
reconfigurable intelligent surface; artificial intelligence; deep learning; deep reinforcement learning; signal processing;
D O I
暂无
中图分类号
TP18 [人工智能理论]; TN911.7 [信号处理];
学科分类号
0711 ; 080401 ; 080402 ; 081104 ; 0812 ; 0835 ; 1405 ;
摘要
Reconfigurable intelligent surface(RIS)is an emerging meta-surface that can provide additional communications links through reflecting the signals, and has been recognized as a strong candidate of6 G mobile communications systems. Meanwhile, it has been recently admitted that implementing artificial intelligence(AI) into RIS communications will extensively benefit the reconfiguration capacity and enhance the robustness to complicated transmission environments. Besides the conventional model-driven approaches, AI can also deal with the existing signal processing problems in a data-driven manner via digging the inherent characteristic from the real data.Hence, AI is particularly suitable for the signal processing problems over RIS networks under unideal scenarios like modeling mismatching, insufficient resource, hardware impairment, as well as dynamical transmissions. As one of the earliest survey papers,we will introduce the merging of AI and RIS, called AIRIS, over various signal processing topics, including environmental sensing, channel acquisition, beamforming design, and resource scheduling, etc. We will also discuss the challenges of AIRIS and present some interesting future directions.
引用
收藏
页码:158 / 171
页数:14
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