Channel Estimation With Reconfigurable Intelligent Surfaces-A General Framework

Optimally extracting the advantages available from reconfigurable intelligent surfaces (RISs) in wireless communications systems requires estimation of the channels to and from the RIS. The process of determining these channels is complicated when the RIS is composed of passive elements without any...

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Vydáno v:	Proceedings of the IEEE Ročník 110; číslo 9; s. 1312 - 1338
Hlavní autoři:	Swindlehurst, A. Lee, Zhou, Gui, Liu, Rang, Pan, Cunhua, Li, Ming
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.09.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Antennas Availability Channel estimation Channel models Channels Data processing direction of arrival (DOA) estimation Direction-of-arrival estimation intelligent reflecting surface massive multi-input–multi-output (MIMO) MIMO communication Narrowband Orthogonal Frequency Division Multiplexing reconfigurable intelligent surface (RIS) Reconfigurable intelligent surfaces Uplink Wideband Wireless communication systems
ISSN:	0018-9219, 1558-2256
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Abstract	Optimally extracting the advantages available from reconfigurable intelligent surfaces (RISs) in wireless communications systems requires estimation of the channels to and from the RIS. The process of determining these channels is complicated when the RIS is composed of passive elements without any sensing or data processing capabilities, and thus, the channels must be estimated indirectly by a noncolocated device, typically a controlling base station (BS). In this article, we examine channel estimation for passive RIS-based systems from a fundamental viewpoint. We study various possible channel models and the identifiability of the models as a function of the available pilot data and behavior of the RIS during training. In particular, we will consider situations with and without line-of-sight propagation, single-antenna and multi-antenna configurations for the users and BS, correlated and sparse channel models, single-carrier and wideband orthogonal frequency-division multiplexing (OFDM) scenarios, availability of direct links between the users and BS, exploitation of prior information, as well as a number of other special cases. We further conduct simulations of representative algorithms and comparisons of their performance for various channel models using the relevant Cramér-Rao bounds.
AbstractList	Optimally extracting the advantages available from reconfigurable intelligent surfaces (RISs) in wireless communications systems requires estimation of the channels to and from the RIS. The process of determining these channels is complicated when the RIS is composed of passive elements without any sensing or data processing capabilities, and thus, the channels must be estimated indirectly by a noncolocated device, typically a controlling base station (BS). In this article, we examine channel estimation for passive RIS-based systems from a fundamental viewpoint. We study various possible channel models and the identifiability of the models as a function of the available pilot data and behavior of the RIS during training. In particular, we will consider situations with and without line-of-sight propagation, single-antenna and multi-antenna configurations for the users and BS, correlated and sparse channel models, single-carrier and wideband orthogonal frequency-division multiplexing (OFDM) scenarios, availability of direct links between the users and BS, exploitation of prior information, as well as a number of other special cases. We further conduct simulations of representative algorithms and comparisons of their performance for various channel models using the relevant Cramér-Rao bounds.
Author	Liu, Rang Li, Ming Swindlehurst, A. Lee Zhou, Gui Pan, Cunhua
Author_xml	– sequence: 1 givenname: A. Lee orcidid: 0000-0002-0521-3107 surname: Swindlehurst fullname: Swindlehurst, A. Lee email: swindle@uci.edu organization: Center for Pervasive Communications and Computing, University of California at Irvine, Irvine, CA, USA – sequence: 2 givenname: Gui orcidid: 0000-0003-1812-8830 surname: Zhou fullname: Zhou, Gui organization: School of Electronic Engineering and Computer Science, Queen Mary University of London, London, U.K – sequence: 3 givenname: Rang orcidid: 0000-0001-7151-7224 surname: Liu fullname: Liu, Rang organization: School of Information and Communication Engineering, Dalian University of Technology, Dalian, China – sequence: 4 givenname: Cunhua orcidid: 0000-0001-5286-7958 surname: Pan fullname: Pan, Cunhua organization: National Mobile Communications Research Laboratory, Southeast University, Nanjing, China – sequence: 5 givenname: Ming orcidid: 0000-0003-1849-8241 surname: Li fullname: Li, Ming organization: School of Information and Communication Engineering, Dalian University of Technology, Dalian, China
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Snippet	Optimally extracting the advantages available from reconfigurable intelligent surfaces (RISs) in wireless communications systems requires estimation of the...
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SubjectTerms	Algorithms Antennas Availability Channel estimation Channel models Channels Data processing direction of arrival (DOA) estimation Direction-of-arrival estimation intelligent reflecting surface massive multi-input–multi-output (MIMO) MIMO communication Narrowband Orthogonal Frequency Division Multiplexing reconfigurable intelligent surface (RIS) Reconfigurable intelligent surfaces Uplink Wideband Wireless communication systems
Title	Channel Estimation With Reconfigurable Intelligent Surfaces-A General Framework
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