Millimeter Wave Base Stations with Cameras: Vision-Aided Beam and Blockage Prediction
Millimeter Wave Base Stations with Cameras: Vision-Aided Beam and Blockage Prediction
复制标题
带摄像头的毫米波基站：视觉辅助光束和遮挡预测
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
A. Alkhateeb
中科院分区：
文献类型：
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作者：
Muhammad Alrabeiah;Andrew Hredzak;A. Alkhateeb
本文研究了一种新的研究方向，即利用VISION来帮助克服关键的无线通信挑战。特别是，本文考虑的是毫米波通信系统，它们是5G及以后的主要组成部分。这些系统面临着两个重要的挑战：(I)与选择最佳波束相关的大的训练开销；(Ii)由于对链路阻塞的高度敏感性而带来的可靠性挑战。有趣的是，大多数采用毫米波阵列的设备可能也会使用摄像头，如5G手机、自动驾驶汽车和虚拟/增强现实耳机。因此，我们调查了在毫米波基站使用摄像机并利用它们的视觉数据来帮助克服波束选择和阻塞预测挑战的潜在收益。为此，本文利用计算机视觉和深度学习工具，直接从摄像机RGB图像和亚6 GHz通道预测毫米波波束和阻塞。实验结果揭示了对这些解决方案的有效性的有趣见解。例如，深度学习模型能够达到90%以上的波束预测精度，只需要抓拍场景和零开销。
This paper investigates a novel research direction that leverages vision to help overcome the critical wireless communication challenges. In particular, this paper considers millimeter wave (mmWave) communication systems, which are principal components of 5G and beyond. These systems face two important challenges: (i) the large training overhead associated with selecting the optimal beam and (ii) the reliability challenge due to the high sensitivity to link blockages. Interestingly, most of the devices that employ mmWave arrays will likely also use cameras, such as 5G phones, self-driving vehicles, and virtual/augmented reality headsets. Therefore, we investigate the potential gains of employing cameras at the mmWave base stations and leveraging their visual data to help overcome the beam selection and blockage prediction challenges. To do that, this paper exploits computer vision and deep learning tools to predict mmWave beams and blockages directly from the camera RGB images and the sub-6GHz channels. The experimental results reveal interesting insights into the effectiveness of such solutions. For example, the deep learning model is capable of achieving over 90% beam prediction accuracy, which only requires snapping a shot of the scene and zero overhead.
影响因子：
11.2
作者：
Ali, Anum;Gonzalez-Prelcic, Nuria;Ghosh, Amitava
通讯作者：
Ghosh, Amitava