LIDAR Data for Deep Learning-Based mmWave Beam-Selection

被引：131

作者：

Klautau, Aldebaro ^{[1
]}

Gonzalez-Prelcic, Nuria ^{[2
]}

Heath, Robert W., Jr. ^{[2
]}

机构：

[1] Univ Fed Para, Comp & Telecommun Dept, BR-66075 Belem, Para, Brazil

[2] Univ Texas Austin, Wireless Networking & Commun Grp, Austin, TX 78712 USA

来源：

IEEE WIRELESS COMMUNICATIONS LETTERS | 2019年 / 8卷 / 03期

基金：

美国国家科学基金会;

关键词：

LIDAR; mmWave; machine learning; deep learning; convolutional networks;

D O I：

10.1109/LWC.2019.2899571

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Millimeter wave (mmWave) communication systems can leverage information from sensors to reduce the overhead associated with link configuration. Light detection and ranging (LIDAR) is one sensor widely used in autonomous driving for high resolution mapping and positioning. This letter shows how LIDAR data can be used for line-of-sight detection and to reduce the overhead in mmWave beam-selection. In the proposed distributed architecture, the base station broadcasts its position. The connected vehicle leverages its LIDAR data to suggest a set of beams selected via a deep convolutional neural network. Co-simulation of communications and LIDAR in a vehicle-to-infrastructure (V2I) scenario confirm that LIDAR can help configuring mmWave V2I links.

引用

页码：909 / 912

页数：4

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