用深度学习预测Wi-Fi信道质量，提升工业网络稳定性。
Improving Wi-Fi Network Performance Prediction with Deep Learning Models
- 用卷积神经网络和LSTM分析实测信道数据预测帧交付率。
- 模型预测准确，卷积神经网络在资源消耗上更优。
- 适合嵌入式工业系统实时优化通信参数。
工业与关键任务应用对无线网络的鲁棒性、可靠性和确定性要求日益提高，推动了新方法的发展。本文利用机器学习技术，基于真实Wi-Fi环境多通道采集的数据，预测信道质量（以帧交付比衡量）。预测结果可用于运行时主动调整通信参数，优化工业网络性能。研究对比了卷积神经网络（CNN）和长短期记忆网络（LSTM）的预测精度与计算复杂度。结果显示，帧交付比可被可靠预测；尽管卷积神经网络略逊于其他模型，但其在CPU使用率和内存消耗方面表现更优，提升了在嵌入式与工业系统中的实用性。
原文摘要 · Abstract (English)
The increasing need for robustness, reliability, and determinism in wireless networks for industrial and mission-critical applications is the driver for the growth of new innovative methods. The study presented in this work makes use of machine learning techniques to predict channel quality in a Wi-Fi network in terms of the frame delivery ratio. Predictions can be used proactively to adjust communication parameters at runtime and optimize network operations for industrial applications. Methods including convolutional neural networks and long short-term memory were analyzed on datasets acquired from a real Wi-Fi setup across multiple channels. The models were compared in terms of prediction accuracy and computational complexity. Results show that the frame delivery ratio can be reliably predicted, and convolutional neural networks, although slightly less effective than other models, are more efficient in terms of CPU usage and memory consumption. This enhances the model's usability on embedded and industrial systems.
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