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Intelligent Reflecting Surface Enhanced Wireless Network via Joint Active and Passive Beamforming

IEEE Transactions on Wireless Communications · 2019 · Vol. 18(11) · pp. 5394–5409
Qingqing WuRui Zhang

Abstract

Intelligent reflecting surface (IRS) is a revolutionary and transformative technology for achieving spectrum and energy efficient wireless communication cost-effectively in the future. Specifically, an IRS consists of a large number of low-cost passive elements each being able to reflect the incident signal independently with an adjustable phase shift so as to collaboratively achieve three-dimensional (3D) passive beamforming without the need of any transmit radio-frequency (RF) chains. In this paper, we study an IRS-aided single-cell wireless system where one IRS is deployed to assist in the communications between a multi-antenna access point (AP) and multiple single-antenna users. We formulate and solve new problems to minimize the total transmit power at the AP by jointly optimizing the transmit beamforming by active antenna array at the AP and reflect beamforming by passive phase shifters at the IRS, subject to users' individual signal-to-interference-plus-noise ratio (SINR) constraints. Moreover, we analyze the asymptotic performance of IRS's passive beamforming with infinitely large number of reflecting elements and compare it to that of the traditional active beamforming/relaying. Simulation results demonstrate that an IRS-aided MIMO system can achieve the same rate performance as a benchmark massive MIMO system without using IRS, but with significantly reduced active antennas/RF chains. We also draw useful insights into optimally deploying IRS in future wireless systems.

Advanced Wireless Communication TechnologiesAntenna Design and AnalysisAdvanced Antenna and Metasurface TechnologiesBeamformingMIMOComputer scienceTransmitter power outputWirelessElectronic engineeringTelecommunicationsSignal-to-noise ratio (imaging)Interference (communication)Computer network
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IEEE Transactions on Vehicular Technology · 2004 · 1,209 citations
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IEEE Transactions on Signal Processing · 2018 · 942 citations
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