LIDAR Data for Deep Learning-Based mmWave Beam-Selection
LIDAR Data for Deep Learning-Based mmWave Beam-Selection
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DOI：
10.1109/lwc.2019.2899571
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发表时间：
2019-02
影响因子：
6.3
通讯作者：
A. Klautau;N. González-Prelcic;R. Heath
中科院分区：
文献类型：
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作者：
A. Klautau;N. González-Prelcic;R. Heath
毫米波(毫米波)通信系统可以利用来自传感器的信息来减少与链路配置相关的开销。光探测和测距(LIDAR)是一种广泛应用于自动驾驶的高分辨率测绘和定位传感器。这封信展示了激光雷达数据如何用于视线探测和减少毫米波波束选择的开销。在所提出的分布式架构中，基站广播其位置。联网的车辆利用其激光雷达数据来建议通过深度卷积神经网络选择的一组波束。在车辆到基础设施(V2I)场景中对通信和LIDAR进行的联合模拟证实，LIDAR可以帮助配置MmWave V2I链路。
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.