论文标题
通过无线传感中的概率机器学习建模来量化不确定性
Quantifying Uncertainty with Probabilistic Machine Learning Modeling in Wireless Sensing
论文作者
论文摘要
多年来，机器学习（ML）技术在无线通信领域中的应用已巨大的增长，尤其是在无线传感域中。但是，围绕ML模型的推理可靠性以及与其预测相关的不确定性的问题从未得到正确的回答或通信。这本身就这些ML系统的透明度提出了很多问题。开发具有概率建模的ML系统可以轻松解决此问题，在这种情况下，人们可以量化它是由数据（不可还原误差或Aleotoric不确定性）或模型本身（可还原性或认知不确定性）引起的不确定性。本文详细介绍了这些类型的不确定性量化背后的想法，并使用了基于WIFI通道状态信息（CSI）的真实示例进行运动/无动案例，以证明不确定性建模。这项工作将用作模板，以模拟预测中的不确定性，不仅是WiFi传感的预测，而且对于大多数无线传感应用程序，从WiFi到毫米波雷达的传感，利用AI/ML模型。
The application of machine learning (ML) techniques in wireless communication domain has seen a tremendous growth over the years especially in the wireless sensing domain. However, the questions surrounding the ML model's inference reliability, and uncertainty associated with its predictions are never answered or communicated properly. This itself raises a lot of questions on the transparency of these ML systems. Developing ML systems with probabilistic modeling can solve this problem easily, where one can quantify uncertainty whether it is arising from the data (irreducible error or aleotoric uncertainty) or from the model itself (reducible or epistemic uncertainty). This paper describes the idea behind these types of uncertainty quantification in detail and uses a real example of WiFi channel state information (CSI) based sensing for motion/no-motion cases to demonstrate the uncertainty modeling. This work will serve as a template to model uncertainty in predictions not only for WiFi sensing but for most wireless sensing applications ranging from WiFi to millimeter wave radar based sensing that utilizes AI/ML models.
