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Reconfigurable Intelligent Surfaces for Energy Efficiency in Wireless Communication

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arxiv 1810.06934 v5 pith:DSJPHEQM submitted 2018-10-16 cs.IT math.IT

classification cs.ITmath.IT
keywords allocationfractionalphasepowerprogrammingalgorithmcommunicationdescent
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abstract

The adoption of a Reconfigurable Intelligent Surface (RIS) for downlink multi-user communication from a multi-antenna base station is investigated in this paper. We develop energy-efficient designs for both the transmit power allocation and the phase shifts of the surface reflecting elements, subject to individual link budget guarantees for the mobile users. This leads to non-convex design optimization problems for which to tackle we propose two computationally affordable approaches, capitalizing on alternating maximization, gradient descent search, and sequential fractional programming. Specifically, one algorithm employs gradient descent for obtaining the RIS phase coefficients, and fractional programming for optimal transmit power allocation. Instead, the second algorithm employs sequential fractional programming for the optimization of the RIS phase shifts. In addition, a realistic power consumption model for RIS-based systems is presented, and the performance of the proposed methods is analyzed in a realistic outdoor environment. In particular, our results show that the proposed RIS-based resource allocation methods are able to provide up to $300\%$ higher energy efficiency, in comparison with the use of regular multi-antenna amplify-and-forward relaying.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Optimizations with Intelligent Reflecting Surfaces (IRSs) in 6G Wireless Networks: Power Control, Quality of Service, Max-Min Fair Beamforming for Unicast, Broadcast, and Multicast with Multi-antenna Mobile Users and Multiple IRSs

    cs.IT 2019-08 conditional novelty 4.0 of 10

    The paper extends IRS beamforming optimization to broadcast, multicast, max-min fairness, and multi-IRS/multi-antenna settings using standard SDR alternating optimization, without numerical validation.

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