On the Prediction of Wi-Fi Performance through Deep Learning

2025-11-28

AI Summary

To address unreliable communication and unpredictable latency caused by dynamic frame delivery rate (FDR) variations in industrial Wi-Fi networks, this paper proposes a lightweight deep learning method that predicts FDR solely from binary success/failure transmission sequences. Departing from conventional multi-dimensional channel feature extraction, our approach directly models the temporal evolution of FDR. We comparatively evaluate convolutional neural networks (CNNs) and long short-term memory (LSTM) networks on real-world industrial Wi-Fi data. Results show both architectures achieve high-accuracy FDR trend prediction; notably, the CNN attains 98.2% accuracy while reducing inference latency by 67% compared to LSTM and decreasing model parameters by 83%. This makes the CNN significantly more suitable for resource-constrained edge industrial devices. Our work establishes a novel paradigm for low-overhead, high-real-time wireless channel quality forecasting in industrial settings.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution

Abstract

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.

Problem

• Predict Wi-Fi Frame Delivery Ratio using deep learning models
• Compare CNN and LSTM for accuracy and computational complexity
• Enable adaptive strategies in industrial Wi-Fi with performance prediction

Innovation

• CNN and LSTM models predict Wi-Fi Frame Delivery Ratio
• Models use binary time sequences for performance prediction
• CNN offers lower latency with minimal accuracy loss