论文标题
使用摄像头图像在线可训练的无线链接质量预测系统
Online Trainable Wireless Link Quality Prediction System using Camera Imagery
论文作者
论文摘要
基于机器学习的未来无线链路质量的预测是一种新兴技术，可以通过预测性移交和光束成形来解决无线通信的可靠性，尤其是在较高频率（例如，毫米波和Terahertz技术）的可靠性，从而解决了隔离线（LOS）阻滞问题。在这项研究中，提出了实时可在线可训练的无线链接质量预测系统。该系统是通过市售的笔记本电脑实施的。提出的系统收集数据集，更新模型并实时渗透收到的功率。实验评估是使用5 GHz Wi-Fi进行的，当LOS路径被大障碍物阻塞时，接收到的信号强度可能会被10 dB降解。实验结果表明，预测模型是实时更新的，可以适应环境的变化，并预测随时间变化的Wi-Fi准确接收的功率。
Machine-learning-based prediction of future wireless link quality is an emerging technique that can potentially improve the reliability of wireless communications, especially at higher frequencies (e.g., millimeter-wave and terahertz technologies), through predictive handover and beamforming to solve line-of-sight (LOS) blockage problem. In this study, a real-time online trainable wireless link quality prediction system was proposed; the system was implemented with commercially available laptops. The proposed system collects datasets, updates a model, and infers the received power in real-time. The experimental evaluation was conducted using 5 GHz Wi-Fi, where received signal strength could be degraded by 10 dB when the LOS path was blocked by large obstacles. The experimental results demonstrate that the prediction model is updated in real-time, adapts to the change in environment, and predicts the time-varying Wi-Fi received power accurately.
