Paper Info
Title
Reconfigurable Intelligent Surfaces: Principles and Opportunities
Abstract
Reconfigurable intelligent surfaces (RISs), also known as intelligent reflecting surfaces (IRSs), or large intelligent surfaces (LISs), <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> have received significant attention for their potential to enhance the capacity and coverage of wireless networks by smartly reconfiguring the wireless propagation environment. Therefore, RISs are considered a promising technology for the sixth-generation (6G) of communication networks. In this context, we provide a comprehensive overview of the state-of-the-art on RISs, with focus on their operating principles, performance evaluation, beamforming design and resource management, applications of machine learning to RIS-enhanced wireless networks, as well as the integration of RISs with other emerging technologies. We describe the basic principles of RISs both from physics and communications perspectives, based on which we present performance evaluation of multiantenna assisted RIS systems. In addition, we systematically survey existing designs for RIS-enhanced wireless networks encompassing performance analysis, information theory, and performance optimization perspectives. Furthermore, we survey existing research contributions that apply machine learning for tackling challenges in dynamic scenarios, such as random fluctuations of wireless channels and user mobility in RIS-enhanced wireless networks. Last but not least, we identify major issues and research opportunities associated with the integration of RISs and other emerging technologies for applications to next-generation networks. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Without loss of generality, we use the name of RIS in the remainder of this paper.
Year
DOI
Venue
2021
10.1109/COMST.2021.3077737
IEEE Communications Surveys & Tutorials
Keywords
DocType
Volume
6G,intelligent reflecting surfaces (IRSs),large intelligent surfaces (LISs),machine learning,performance optimization,reconfigurable intelligent surfaces (RISs),wireless networks
Journal
23
Issue
Citations 
PageRank 
3
64
1.47
References 
Authors
0
8
Name
Order
Citations
PageRank
Yuanwei Liu12162131.65
Xiao Liu228441.90
Xidong Mu326511.60
Tianwei Hou4641.81
Jiaqi Xu51069.50
Zhijin Qin662334.85
Marco Di Renzo74721269.75
Naofal Al-Dhahir82755319.65