用神经网络预测工业场景下无线链路质量，效果优于传统方法。
Mixing Neural Networks and Exponential Moving Averages for Predicting Wireless Links Behavior
- 结合神经网络与指数移动平均，捕捉阴影和多径效应等复杂通信模式。
- 在密集室内环境中，预测准确率显著高于传统基于指数移动平均的方法。
- 适合关注工业无线通信可靠性与确定性的研究人员和工程师。
在密集的室内环境中，预测无线链路的质量（如帧传输成功率）对优化工业无线通信系统的性能至关重要。由于工业应用通常对可靠性和端到端延迟有严格要求，信道质量下降会严重影响系统表现。本文研究了两种神经网络模型在Wi-Fi链路质量预测中的应用。实验结果表明，这些模型的准确性优于基于指数移动平均的传统方法，原因在于其能够捕捉复杂的通信模式，包括阴影效应和多径传播，这些现象在工业场景中尤为显著。这表明神经网络在复杂工况下预测频谱行为具有潜力，有助于提升无线通信的确定性与可靠性，推动其在工业领域的实际应用。
原文摘要 · Abstract (English)
Predicting the behavior of a wireless link in terms of, e.g., the frame delivery ratio, is a critical task for optimizing the performance of wireless industrial communication systems. This is because industrial applications are typically characterized by stringent dependability and end-to-end latency requirements, which are adversely affected by channel quality degradation. In this work, we studied two neural network models for Wi-Fi link quality prediction in dense indoor environments. Experimental results show that their accuracy outperforms conventional methods based on exponential moving averages, due to their ability to capture complex patterns about communications, including the effects of shadowing and multipath propagation, which are particularly pronounced in industrial scenarios. This highlights the potential of neural networks for predicting spectrum behavior in challenging operating conditions, and suggests that they can be exploited to improve determinism and dependability of wireless communications, fostering their adoption in the industry.
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