基于摄像机图像的在线可训练无线链路质量预测系统
Online Trainable Wireless Link Quality Prediction System using Camera Imagery
摘要：
未来无线链路质量的基于机器学习的预测是一种新兴技术，其可以潜在地提高无线通信的可靠性，特别是在较高频率（例如，毫米波和太赫兹技术），通过预测切换和波束成形来解决视线（LOS）阻塞问题。在这项研究中，提出了一个实时在线可训练的无线链路质量预测系统，该系统与商用笔记本电脑实现。所提出的系统收集数据集，更新模型，并实时推断接收功率。使用5 GHz Wi-Fi进行实验评估，其中当LOS路径被大型障碍物阻挡时，接收信号强度可能会降低10 dB。实验结果表明，该预测模型能够实时更新，适应环境变化，准确预测时变Wi-Fi接收功率。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
