arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2003.01306eess.SP
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智能反射面辅助MmWave网络的机器学习增强波束管理

Machine Learning Empowered Beam Management for Intelligent Reflecting Surface Assisted MmWave Networks

Chenglu Jia, Hui Gao, Na Chen, Yuan He

摘要:

最近,智能反射表面(IRS)辅助的毫米波网络正在出现,其具有以更具成本效益的方式解决毫米波(mmWave)通信的阻塞问题的潜力。特别地,IRS由无源和可编程电磁元件构建,这些电磁元件可以通过明智的联合BS-IRS传输设计将毫米波传播信道操纵到更有利的无阻塞条件。然而,IRS和毫米波BS的共存使网络架构复杂化,并且因此对高效波束管理(BM)提出了巨大挑战,高效波束管理是高性能毫米波网络的一个关键先决条件。在本文中,我们系统地评估了IRS辅助毫米波网络BM的关键问题和挑战,为未来的网络设计带来见解。具体来说,我们仔细分类和讨论传统毫米波的现有BM向IRS辅助的新范例的可扩展性和局限性。此外,我们提出了一种新的机器学习授权BM框架的IRS辅助网络与代表性的展示,处理环境和移动性的意识,以实现高效的BM显着降低系统开销。最后,我们也提出一些未来的研究方向。

英文摘要:

Recently, intelligent reflecting surface (IRS) assisted mmWave networks are emerging, which bear the potential to address the blockage issue of the millimeter wave (mmWave) communication in a more cost-effective way. In particular, IRS is built by passive and programmable electromagnetic elements that can manipulate the mmWave propagation channel into a more favorable condition that is free of blockage via judicious joint BS-IRS transmission design. However, the coexistence of IRSs and mmWave BSs complicates the network architecture, and thus poses great challenges for efficient beam management (BM) that is one critical prerequisite for high performance mmWave networks. In this paper, we systematically evaluate the key issues and challenges of BM for IRS-assisted mmWave networks to bring insights into the future network design. Specifically, we carefully classify and discuss the extensibility and limitations of the existing BM of conventional mmWave towards the IRS-assisted new paradigm. Moreover, we propose a novel machine learning empowered BM framework for IRS-assisted networks with representative showcases, which processes environmental and mobility awareness to achieve highly efficient BM with significantly reduced system overhead. Finally, some interesting future directions are also suggested to inspire further researches.

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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