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arXiv 2209.02258eess.SP
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面向6G的智能毫米波和太赫兹通信:计算机视觉辅助波束形成

Towards Intelligent Millimeter and Terahertz Communication for 6G: Computer Vision-aided Beamforming

Yongjun Ahn, Jinhong Kim, Seungnyun Kim, Kyuhong Shim, Jiyoung Kim, Sangtae Kim, Byonghyo Shim

摘要:

在毫米波(mmWave)频段,通过多输入多输出(MIMO)天线阵列实现的波束形成技术被广泛用于补偿严重的路径损耗。在5G NR系统中,采用波束扫描和波束细化来找出与移动台对准的最佳波束码字。由于码本的复杂握手和有限分辨率,当今基于5G的波束管理策略在数据速率、能耗以及处理延迟方面的各种场景中是无效的。本文的目的是介绍一种新型的基于计算机视觉(CV)技术的光束管理框架。在这个被称为计算机视觉辅助光束管理(CVBM)的框架中,连接到BS的摄像头捕获图像,然后基于深度学习的物体检测器识别移动设备的3D位置。由于基站可以直接设置波束方向而无需码本量化和反馈延迟,CVBM实现了显著的波束形成增益和延迟减少。使用专门设计的称为Vision Objects for Beam Management(VOBEM)的数据集,我们证明了CVBM在5G NR波束管理上实现了波束成形增益的40%以上的提高和波束训练开销的40%以上的减少。

英文摘要:

Beamforming technique realized by the multiple-input-multiple-output (MIMO) antenna arrays has been widely used to compensate for the severe path loss in the millimeter wave (mmWave) bands. In 5G NR system, the beam sweeping and beam refinement are employed to find out the best beam codeword aligned to the mobile. Due to the complicated handshaking and finite resolution of the codebook, today's 5G-based beam management strategy is ineffective in various scenarios in terms of the data rate, energy consumption, and also processing latency. An aim of this article is to introduce a new type of beam management framework based on the computer vision (CV) technique. In this framework referred to as computer vision-aided beam management (CVBM), a camera attached to the BS captures the image and then the deep learning-based object detector identifies the 3D location of the mobile. Since the base station can directly set the beam direction without codebook quantization and feedback delay, CVBM achieves the significant beamforming gain and latency reduction. Using the specially designed dataset called Vision Objects for Beam Management (VOBEM), we demonstrate that CVBM achieves more than 40% improvement in the beamforming gain and 40% reduction in the beam training overhead over the 5G NR beam management.

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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