Improving Wi-Fi Network Performance Prediction with Deep Learning Models

TL;DR

This paper explores deep learning models, specifically CNNs and LSTMs, to predict Wi-Fi channel quality for industrial applications, enabling proactive network adjustments and improved reliability.

Contribution

It introduces a machine learning approach using CNNs and LSTMs to predict Wi-Fi frame delivery ratio, optimizing network performance in industrial settings.

Findings

CNNs are more efficient in CPU and memory usage.

Deep learning models can reliably predict frame delivery ratio.

CNNs, while slightly less accurate, offer better computational efficiency.

Abstract

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.