深度学习赋能波束管理:现状、挑战与机遇

Deep learning empowered beam management:State-of-the-art,challenges and opportunities

随着载波频率的不断提高和大规模天线阵列的广泛部署,基于模拟移相器的波束赋形成为下一代无线通信的标志性技术之一。此时,波束管理被用于获取和维护基站和用户端具有最大接收功率的最优波束对,以保障可靠的无线通信服务。传统波束管理方法往往依赖于海量搜索。同时,传统数学模型无法全面的、准确刻画非线性的波束的内在关联和高维无线环境特征,因而难以取得令人满意的波束增益性能。近年来,得益于深度学习强大的自适应拟合能力,深度学习赋能波束管理得到了国内外广泛关注。本文总结了深度学习赋能波束管理的研究进展,并展望了未来的研究方向。首先,阐述了深度学习应用于波束管理的典型场景和潜在优势;随后,从空/时/频域切入,讨论当前深度学习赋能波束管理的主要研究路线和代表性工作;最后,面向更大规模的无线网络、更多元的波束管理功能和更鲁棒的深度学习模型,阐述未来的研究挑战与机遇。

With the increase of carrier frequencies and the widespread deployment of large-scale antenna arrays,the analog phase shifter based beamforming has become one of the key technologies for the next-generation wireless communications.To ensure reliable wireless services,beam management is usually adopted to acquire and maintain the optimal beam pair with the maximum received power between the base station and the user equipment.However,traditional beam management methods generally rely on large-scale searching,which could bring huge overhead.Additionally,traditional mathematical models cannot comprehensively and accurately model the nonlinear intrinsic characteristics in the received signals from the beam and the high-dimensional features of wireless environments,making it difficult to achieve satisfactory beamforming gain performance.Thanks to its strong adaptive fitting capabilities,deep learning empowered beam management has drawn much attention recently.To this end,this paper reviews the state-of-the-art literatures of deep learning empowered beam management and discusses future research directions.Firstly,the typical scenarios and advantages of applying deep learning to beam management are elucidated.Subsequently,the main techniques and representative works in current deep learning empowered beam management are summarized from space/time/frequency domains.Finally,this paper addresses future research challenges and opportunities from the perspective of larger-scale wireless networks,more diverse beam management functions,and more robust deep learning models.