# Accurate and Efficient Prediction of Wi-Fi Link Quality Based on Machine Learning

arXiv: https://arxiv.org/abs/2509.18933 · HTML: https://mlsanity.com/paper/2509.18933/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 53 references that are in the map.
- First posted on arXiv 23 Sep 2025.

## Builds on (most cited first, 14 of 14)

- [Adversarial Machine Learning in Wireless Communications using RF Data: A Review](https://mlsanity.com/paper/2012.14392/) (2020)
- [Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance with Machine Learning](https://mlsanity.com/paper/2109.04786/) (2021)
- [What Will Wi-Fi 8 Be? A Primer on IEEE 802.11bn Ultra High Reliability](https://mlsanity.com/paper/2303.10442/) (2023)
- [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)
- [LIMITS: Lightweight Machine Learning for IoT Systems with Resource Limitations](https://mlsanity.com/paper/2001.10189/) (2020)
- [Seamless Link-level Redundancy to Improve Reliability of Industrial Wi-Fi Networks](https://mlsanity.com/paper/2211.03575/) (2022)
- [Predicting Wireless Channel Quality by means of Moving Averages and Regression Models](https://mlsanity.com/paper/2306.08634/) (2023)
- [Throughput Analysis of IEEE 802.11bn Coordinated Spatial Reuse](https://mlsanity.com/paper/2309.09169/) (2023)
- [A Functional Architecture for 6G Special Purpose Industrial IoT Networks](https://mlsanity.com/paper/2207.00264/) (2022)
- [Machine Learning for QoS Prediction in Vehicular Communication: Challenges and Solution Approaches](https://mlsanity.com/paper/2302.11966/) (2023)
- [Multi-Link Operation and Wireless Digital Twin to Support Enhanced Roaming in Next-Gen Wi-Fi](https://mlsanity.com/paper/2404.18313/) (2024)
- [Improving Wi-Fi Network Performance Prediction with Deep Learning Models](https://mlsanity.com/paper/2507.11168/) (2025)

## Abstract (arXiv)

Wireless communications are characterized by their unpredictability, posing challenges for maintaining consistent communication quality. This paper presents a comprehensive analysis of various prediction models, with a focus on achieving accurate and efficient Wi-Fi link quality forecasts using machine learning techniques. Specifically, the paper evaluates the performance of data-driven models based on the linear combination of exponential moving averages, which are designed for low-complexity implementations and are then suitable for hardware platforms with limited processing resources. Accuracy of the proposed approaches was assessed using experimental data from a real-world Wi-Fi testbed, considering both channel-dependent and channel-independent training data. Remarkably, channel-independent models, which allow for generalized training by equipment manufacturers, demonstrated competitive performance. Overall, this study provides insights into the practical deployment of machine learning-based prediction models for enhancing Wi-Fi dependability in industrial environments.