# Improving Wi-Fi Network Performance Prediction with Deep Learning Models

arXiv: https://arxiv.org/abs/2507.11168 · HTML: https://mlsanity.com/paper/2507.11168/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 22 references that are in the map.
- First posted on arXiv 15 Jul 2025.

## Builds on (most cited first, 6 of 6)

- [Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance with Machine Learning](https://mlsanity.com/paper/2109.04786/) (2021)
- [A Comparison of Neural Networks for Wireless Channel Prediction](https://mlsanity.com/paper/2308.14020/) (2023)
- [Linear Combination of Exponential Moving Averages for Wireless Channel Prediction](https://mlsanity.com/paper/2312.07945/) (2023)
- [ML-based Handover Prediction and AP Selection in Cognitive Wi-Fi Networks](https://mlsanity.com/paper/2111.13879/) (2021)
- [Wireless Link Quality Estimation Using LSTM Model](https://mlsanity.com/paper/2410.15357/) (2024)
- [Real-Time Performance of Industrial IoT Communication Technologies: A Review](https://mlsanity.com/paper/2311.08852/) (2023)

## Built on by (most cited first, 1 of 1)

- [Accurate and Efficient Prediction of Wi-Fi Link Quality Based on Machine Learning](https://mlsanity.com/paper/2509.18933/) (2025)

## Abstract (arXiv)

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.