论文标题
机器学习授权的光束管理，用于智能反射地面辅助MMWave网络
Machine Learning Empowered Beam Management for Intelligent Reflecting Surface Assisted MmWave Networks
论文作者
论文摘要
最近，智能反射表面（IRS）辅助MMWave网络正在出现，这有可能以更具成本效益的方式解决毫米波（MMWave）通信的阻塞问题。特别是，IRS是由被动和可编程的电磁元素构建的，可以将MMWave繁殖通道操纵为更有利的条件，该条件通过出色的关节BS-IRS传输设计无障碍。但是，IRSS和MMWave BSS的共存使网络体系结构复杂化，因此对有效的光束管理（BM）构成了巨大的挑战，这是高性能MMWave网络的关键先决条件。在本文中，我们系统地评估了IRS辅助MMWave网络的BM的关键问题和挑战，以将洞察力带入未来的网络设计中。具体而言，我们仔细分类并讨论了常规MMWave现有BM对IRS辅助新范式的可扩展性和局限性。此外，我们提出了一种新型的机器学习授权的IRS辅助网络的BM框架，该网络具有代表性的展示，该网络会处理环境和移动性意识，以实现高效的BM，并显着降低了系统开销。最后，还建议一些有趣的未来方向激发进一步的研究。
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.
