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基于联合有源和无源波束赋形的LoS-D环境下可重构智能表面最优部署策略

Optimal Deployment Strategy for Reconfigurable Intelligent Surface under LoSD via Joint Active and Passive Beamforming.

作者信息

Zhao Ke, Song Zhiqun, Xiong Jun

机构信息

The 54th Research Institute of CETC, Shijiazhuang 050081, China.

Science and Technology on Communication Networks Laboratory, Shijiazhuang 050081, China.

出版信息

Entropy (Basel). 2023 Jul 17;25(7):1073. doi: 10.3390/e25071073.

Abstract

A reconfigurable intelligent surface (RIS) is a new and revolutionizing technology to achieve spectrum-efficient (SE) and energy-efficient (EE) wireless networks. In this paper, we study an optimal deployment strategy of RIS in a line-of-sight domain (LoSD) based on an actual deployment scenario, which jointly considers path loss, transmit power and the energy efficiency of the system. Furthermore, we aim to minimize the transmit power via jointly optimizing its transmit beamforming and the reflect phase shifts of RIS, subject to the quality-of-service (QoS) constraint, namely, the signal-to-noise ratio (SNR) constraint at the user. However, this optimization problem is non-convex with intricately coupled variables. To tackle this challenge, we first apply proper transformation on the QoS constraint and then propose an efficient alternating optimization (AO) algorithm. Simulation results demonstrate that compared to a conventional endpoint deployment strategy that simply deploys RIS at the transceiver ends, our proposed LoSD deployment strategy significantly reduces the transmit power by optimizing the available LoS links when a single RIS is relayed. The impact of the number of reflect elements on the system EE is also unveiled.

摘要

可重构智能表面(RIS)是一种实现频谱高效(SE)和能量高效(EE)无线网络的新型变革性技术。在本文中,我们基于实际部署场景研究了RIS在视距域(LoSD)中的最优部署策略,该策略联合考虑了路径损耗、发射功率和系统的能量效率。此外,我们旨在通过联合优化其发射波束成形和RIS的反射相移来最小化发射功率,同时满足服务质量(QoS)约束,即用户端的信噪比(SNR)约束。然而,这个优化问题是非凸的,变量之间存在复杂的耦合。为应对这一挑战,我们首先对QoS约束进行适当变换,然后提出一种高效的交替优化(AO)算法。仿真结果表明,与仅在收发两端简单部署RIS的传统端点部署策略相比,当单个RIS进行中继时,我们提出的LoS D部署策略通过优化可用的视距链路显著降低了发射功率。还揭示了反射元件数量对系统能量效率的影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a93f/10378362/13c215a63c30/entropy-25-01073-g001.jpg

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