论文标题
摄像机协助可重新配置的智能表面：基于计算机视觉的快速光束选择
Camera Aided Reconfigurable Intelligent Surfaces: Computer Vision Based Fast Beam Selection
论文作者
论文摘要
可重新配置的智能表面（RISS）由于能够提高毫米波（MMWave）通信系统的覆盖范围，可靠性和能源效率而引起了越来越多的兴趣。但是，设计RIS光束通常需要大型通道估计或横梁训练开销，从而降低了这些系统的效率。在本文中，我们建议为RIS表面配备视觉传感器（相机），以获取有关周围环境和用户/底座位置的传感信息，指导RIS光束选择，并减少梁训练的开销。我们开发了一个机器学习（ML）框架，该框架利用此视觉传感信息有效地选择了反映基地用户和移动用户之间信号的最佳RIS反射光束。为了评估开发的方法，我们构建了一个高保真合成数据集，该数据集包括共存的无线和视觉数据。基于此数据集，结果表明，拟议的视觉辅助机器学习解决方案可以准确预测RIS梁并达到近乎最佳的可实现速率，同时显着降低了光束训练开销。
Reconfigurable intelligent surfaces (RISs) have attracted increasing interest due to their ability to improve the coverage, reliability, and energy efficiency of millimeter wave (mmWave) communication systems. However, designing the RIS beamforming typically requires large channel estimation or beam training overhead, which degrades the efficiency of these systems. In this paper, we propose to equip the RIS surfaces with visual sensors (cameras) that obtain sensing information about the surroundings and user/basestation locations, guide the RIS beam selection, and reduce the beam training overhead. We develop a machine learning (ML) framework that leverages this visual sensing information to efficiently select the optimal RIS reflection beams that reflect the signals between the basestation and mobile users. To evaluate the developed approach, we build a high-fidelity synthetic dataset that comprises co-existing wireless and visual data. Based on this dataset, the results show that the proposed vision-aided machine learning solution can accurately predict the RIS beams and achieve near-optimal achievable rate while significantly reducing the beam training overhead.
