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arXiv 2009.02540eess.SPcs.ITcs.LGmath.IT
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面向智能反射面的深度学习架构综述

A Survey of Deep Learning Architectures for Intelligent Reflecting Surfaces

Department of Electrical and Electronics Engineering, Istinye University(伊斯坦布尔大学电气与电子工程系);University of Luxembourg(卢森堡大学);United States DEVCOM Army Research Laboratory(美国国防部高级研究实验室)

Ahmet M. Elbir, Kumar Vijay Mishra

AI总结:

本文针对智能反射面部署中的多信道链路、波束成形器联合设计与元件快速重配置挑战,综述了监督、无监督、强化学习等深度学习架构在IRS辅助无线系统物理层相关任务中的应用。

摘要:

智能反射面(Intelligent reflecting surfaces, IRSs)因能够实现无线传播环境的可控性,近来在6G无线通信领域受到广泛关注。IRS的应用还能降低传统大型天线阵列的硬件复杂度、物理尺寸、重量以及成本。然而,IRS的部署需要应对基站(base station, BS)与用户之间的多条信道链路问题。此外,基站与IRS的波束成形器需要联合设计,且IRS元件必须能够快速重配置。以深度学习(deep learning, DL)为代表的数据驱动技术是应对这些挑战的关键。DL具备计算耗时低、无模型的特性,使其对数据缺陷和环境变化具有鲁棒性。在物理层,DL已被证明可有效应用于IRS信号检测、信道估计以及有源/无源波束成形,所采用的架构包括监督学习、无监督学习和强化学习。本文概述了这些用于设计基于DL的IRS辅助无线系统的技术。

英文摘要:

Intelligent reflecting surfaces (IRSs) have recently received significant attention for 6G wireless communications as they enable the control of the wireless propagation environment. The use of IRS also provides reducing the hardware complexity, physical size, weight as well as cost of conventional large antenna arrays. However, deployment of the IRS entails dealing with multiple channel links between the base station (BS) and the users. Further, the BS and IRS beamformers require a joint design, wherein the IRS elements must be rapidly reconfigured. Data-driven techniques, such as deep learning (DL), are critical in addressing these challenges. The lower computation time and model-free nature of DL make it robust against data imperfections and environmental changes. At the physical layer, DL has been shown to be effective for IRS signal detection, channel estimation, and active/passive beamforming using architectures such as supervised, unsupervised, and reinforcement learning. This article provides a synopsis of these techniques for designing DL-based IRS-assisted wireless systems.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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