Weighted Sum-Rate Maximization for Reconfigurable Intelligent Surface Aided Wireless Networks

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) downli...

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Bibliographic Details
Published in:IEEE transactions on wireless communications 2020-05, Vol.19 (5), p.3064-3076
Main Authors: Guo, Huayan, Liang, Ying-Chang, Chen, Jie, Larsson, Erik G.
Format: Article
Language:English
Subjects:
Algorithms
Approximation algorithms
Array signal processing
Beamforming
Channel estimation
Communications systems
fractional programming
Mathematical programming
MISO (control systems)
MISO communication
multiple-input-multiple-output (MIMO)
Numerical methods
Optimization
passive radio
Precoding
Reconfigurable intelligent surfaces
Reconfigurable intelligent surfaces (RIS)
Steering
stochastic successive convex approximation
Wireless communication
Wireless networks
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Summary: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%.
ISSN:1536-1276
1558-2248
1558-2248
DOI:10.1109/TWC.2020.2970061