用深度学习预测Wi-Fi传输成功率，提升工业系统可靠性
On the Prediction of Wi-Fi Performance through Deep Learning
- 基于二进制成功/失败序列，用CNN与LSTM预测帧交付率
- 两者均实现高精度预测，CNN延迟更低，适合资源受限场景
- 仅需单个二进制序列输入，适用于实时自适应系统
在依赖Wi-Fi网络的现代工业系统中，确保通信的可靠性和可预测性至关重要，尤其在需要连续运行和低延迟的场景下。预测无线信道质量的变化可支持自适应策略，显著提升系统鲁棒性。本文聚焦于帧交付率（FDR）的预测，该指标表示成功传输的比例，输入为真实场景中采集的时间序列二进制结果（成功/失败）。比较了两种深度学习模型：卷积神经网络（CNN）和长短期记忆网络（LSTM），二者均擅长处理时间序列预测。评估标准包括预测准确率与计算复杂度，以判断其在资源受限系统中的适用性。初步结果显示，两种模型均可实现良好的FDR演化预测，即使仅使用单一二进制序列作为输入。其中，CNN表现出显著更低的推理延迟，准确率仅小幅低于LSTM。
原文摘要 · Abstract (English)
Ensuring reliable and predictable communications is one of the main goals in modern industrial systems that rely on Wi-Fi networks, especially in scenarios where continuity of operation and low latency are required. In these contexts, the ability to predict changes in wireless channel quality can enable adaptive strategies and significantly improve system robustness. This contribution focuses on the prediction of the Frame Delivery Ratio (FDR), a key metric that represents the percentage of successful transmissions, starting from time sequences of binary outcomes (success/failure) collected in a real scenario. The analysis focuses on two models of deep learning: a Convolutional Neural Network (CNN) and a Long Short-Term Memory network (LSTM), both selected for their ability to predict the outcome of time sequences. Models are compared in terms of prediction accuracy and computational complexity, with the aim of evaluating their applicability to systems with limited resources. Preliminary results show that both models are able to predict the evolution of the FDR with good accuracy, even from minimal information (a single binary sequence). In particular, CNN shows a significantly lower inference latency, with a marginal loss in accuracy compared to LSTM.
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