Joint Deployment and Multiple Access Design for Intelligent Reflecting Surface Assisted Networks

The fundamental intelligent reflecting surface (IRS) deployment problem is investigated for IRS-assisted networks, where one IRS is arranged to be deployed in a specific region for assisting the communication between an access point (AP) and multiple users. Specifically, three multiple access scheme...

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Published in:	IEEE transactions on wireless communications Vol. 20; no. 10; pp. 6648 - 6664
Main Authors:	Mu, Xidong, Liu, Yuanwei, Guo, Li, Lin, Jiaru, Schober, Robert
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.10.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Channel estimation Computational geometry Convexity Deployment design Downlink Frequency division multiple access intelligent reflecting surface MISO communication monotonic optimization NOMA non-orthogonal multiple access Optimization Reconfigurable intelligent surfaces Resource management Time Division Multiple Access Transmission line matrix methods Upper bounds Wireless communication
ISSN:	1536-1276, 1558-2248
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Abstract	The fundamental intelligent reflecting surface (IRS) deployment problem is investigated for IRS-assisted networks, where one IRS is arranged to be deployed in a specific region for assisting the communication between an access point (AP) and multiple users. Specifically, three multiple access schemes are considered, namely non-orthogonal multiple access (NOMA), frequency division multiple access (FDMA), and time division multiple access (TDMA). The weighted sum rate maximization problem for joint optimization of the deployment location and the reflection coefficients of the IRS as well as the power allocation at the AP is formulated. The non-convex optimization problems obtained for NOMA and FDMA are solved by employing monotonic optimization and semidefinite relaxation to find a performance upper bound. The problem obtained for TDMA is optimally solved by leveraging the time-selective nature of the IRS. Furthermore, for all three multiple access schemes, low-complexity suboptimal algorithms are developed by exploiting alternating optimization and successive convex approximation techniques, where a local region optimization method is applied for optimizing the IRS deployment location. Numerical results are provided to show that: 1) near-optimal performance can be achieved by the proposed suboptimal algorithms; 2) asymmetric and symmetric IRS deployment strategies are preferable for NOMA and FDMA/TDMA, respectively; 3) the performance gain achieved with IRS can be significantly improved by optimizing the deployment location.
AbstractList	The fundamental intelligent reflecting surface (IRS) deployment problem is investigated for IRS-assisted networks, where one IRS is arranged to be deployed in a specific region for assisting the communication between an access point (AP) and multiple users. Specifically, three multiple access schemes are considered, namely non-orthogonal multiple access (NOMA), frequency division multiple access (FDMA), and time division multiple access (TDMA). The weighted sum rate maximization problem for joint optimization of the deployment location and the reflection coefficients of the IRS as well as the power allocation at the AP is formulated. The non-convex optimization problems obtained for NOMA and FDMA are solved by employing monotonic optimization and semidefinite relaxation to find a performance upper bound. The problem obtained for TDMA is optimally solved by leveraging the time-selective nature of the IRS. Furthermore, for all three multiple access schemes, low-complexity suboptimal algorithms are developed by exploiting alternating optimization and successive convex approximation techniques, where a local region optimization method is applied for optimizing the IRS deployment location. Numerical results are provided to show that: 1) near-optimal performance can be achieved by the proposed suboptimal algorithms; 2) asymmetric and symmetric IRS deployment strategies are preferable for NOMA and FDMA/TDMA, respectively; 3) the performance gain achieved with IRS can be significantly improved by optimizing the deployment location.
Author	Guo, Li Lin, Jiaru Schober, Robert Mu, Xidong Liu, Yuanwei
Author_xml	– sequence: 1 givenname: Xidong orcidid: 0000-0001-8351-360X surname: Mu fullname: Mu, Xidong email: muxidong@bupt.edu.cn organization: Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China – sequence: 2 givenname: Yuanwei orcidid: 0000-0002-6389-8941 surname: Liu fullname: Liu, Yuanwei email: yuanwei.liu@qmul.ac.uk organization: School of Electronic Engineering and Computer Science, Queen Mary University of London, London, U.K – sequence: 3 givenname: Li orcidid: 0000-0002-9723-3294 surname: Guo fullname: Guo, Li email: guoli@bupt.edu.cn organization: Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China – sequence: 4 givenname: Jiaru orcidid: 0000-0001-6593-4419 surname: Lin fullname: Lin, Jiaru email: jrlin@bupt.edu.cn organization: Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China – sequence: 5 givenname: Robert surname: Schober fullname: Schober, Robert email: robert.schober@fau.de organization: Institute for Digital Communications, Friedrich-Alexander University of Erlangen-Nürnberg (FAU), Erlangen, Germany
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SubjectTerms	Algorithms Channel estimation Computational geometry Convexity Deployment design Downlink Frequency division multiple access intelligent reflecting surface MISO communication monotonic optimization NOMA non-orthogonal multiple access Optimization Reconfigurable intelligent surfaces Resource management Time Division Multiple Access Transmission line matrix methods Upper bounds Wireless communication
Title	Joint Deployment and Multiple Access Design for Intelligent Reflecting Surface Assisted Networks
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