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.

翻译：确保可靠且可预测的通信是现代依赖Wi-Fi网络的工业系统的主要目标之一，尤其是在需要连续运行和低延迟的场景中。在这些背景下，预测无线信道质量变化的能力能够实现自适应策略，并显著提升系统鲁棒性。本研究聚焦于帧传输成功率（FDR）的预测，该关键指标代表成功传输的百分比，基于真实场景中收集的二进制结果（成功/失败）时间序列进行分析。研究重点评估两种深度学习模型：卷积神经网络（CNN）和长短期记忆网络（LSTM），两者均因其对时间序列结果的预测能力而被选用。模型在预测精度和计算复杂度方面进行了比较，旨在评估其在资源受限系统中的适用性。初步结果表明，即使基于最小信息量（单一二进制序列），两种模型均能以较高精度预测FDR的变化趋势。特别地，CNN在推理延迟方面显著低于LSTM，而精度损失相对有限。