用机器学习精准预测Wi-Fi信号质量，适合资源受限设备部署
Accurate and Efficient Prediction of Wi-Fi Link Quality Based on Machine Learning
- 基于指数移动平均的线性组合模型，降低计算复杂度
- 独立信道训练模型表现优异，支持设备厂商通用训练
- 在真实测试床验证，适用于工业场景提升连接可靠性
无线通信具有不可预测性，给维持稳定通信质量带来挑战。本文系统评估了多种基于机器学习的预测模型，重点实现低复杂度、高精度的Wi-Fi链路质量预测。所提方法采用指数移动平均的线性组合设计，便于在处理能力有限的硬件平台实现。通过真实世界Wi-Fi测试床数据验证，无论使用依赖信道还是独立信道的训练数据，模型均表现良好；其中，信道无关模型展现出竞争力，允许设备制造商进行通用训练。研究为工业环境中部署机器学习驱动的链路质量预测提供了实用参考。
原文摘要 · Abstract (English)
Wireless communications are characterized by their unpredictability, posing challenges for maintaining consistent communication quality. This paper presents a comprehensive analysis of various prediction models, with a focus on achieving accurate and efficient Wi-Fi link quality forecasts using machine learning techniques. Specifically, the paper evaluates the performance of data-driven models based on the linear combination of exponential moving averages, which are designed for low-complexity implementations and are then suitable for hardware platforms with limited processing resources. Accuracy of the proposed approaches was assessed using experimental data from a real-world Wi-Fi testbed, considering both channel-dependent and channel-independent training data. Remarkably, channel-independent models, which allow for generalized training by equipment manufacturers, demonstrated competitive performance. Overall, this study provides insights into the practical deployment of machine learning-based prediction models for enhancing Wi-Fi dependability in industrial environments.
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