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Weighted Sum-Rate Maximization for Reconfigurable Intelligent Surface Aided Wireless Networks
Univ Elect Sci and Technol China, Peoples R China; Hong Kong Univ Sci and Technol HKUST, Peoples R China; Hong Kong Univ Sci and Technol HKUST, Peoples R China.
Univ Elect Sci and Technol China UESTC, Peoples R China.
Univ Elect Sci and Technol China, Peoples R China; Univ Elect Sci and Technol China UESTC, Peoples R China.
Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-7599-4367
2020 (English)In: IEEE Transactions on Wireless Communications, ISSN 1536-1276, E-ISSN 1558-2248, Vol. 19, no 5, p. 3064-3076Article in journal (Refereed) Published
Abstract [en]

Reconfigurable intelligent surfaces (RIS) is a promising solution to build a programmable wireless environment via steering the incident signal in fully customizable ways with reconfigurable passive elements. In this paper, we consider a RIS-aided multiuser multiple-input single-output (MISO) downlink communication system. Our objective is to maximize the weighted sum-rate (WSR) of all users by joint designing the beamforming at the access point (AP) and the phase vector of the RIS elements, while both the perfect channel state information (CSI) setup and the imperfect CSI setup are investigated. For perfect CSI setup, a low-complexity algorithm is proposed to obtain the stationary solution for the joint design problem by utilizing the fractional programming technique. Then, we resort to the stochastic successive convex approximation technique and extend the proposed algorithm to the scenario wherein the CSI is imperfect. The validity of the proposed methods is confirmed by numerical results. In particular, the proposed algorithm performs quite well when the channel uncertainty is smaller than 10%.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2020. Vol. 19, no 5, p. 3064-3076
Keywords [en]
Optimization; MISO communication; Wireless communication; Precoding; Array signal processing; Approximation algorithms; Channel estimation; Reconfigurable intelligent surfaces (RIS); passive radio; multiple-input-multiple-output (MIMO); fractional programming; stochastic successive convex approximation
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-166490DOI: 10.1109/TWC.2020.2970061ISI: 000536297700012OAI: oai:DiVA.org:liu-166490DiVA, id: diva2:1444124
Note

Funding Agencies|National Natural Science Foundation of ChinaNational Natural Science Foundation of China [61631005, U1801261]; National Key Research and Development Program of China [2018YFB1801105]; 111 ProjectMinistry of Education, China - 111 Project [B20064]; Swedish Research Council (VR)Swedish Research Council; ELLIIT

Available from: 2020-06-20 Created: 2020-06-20 Last updated: 2020-06-20

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