# Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance with Machine Learning

arXiv: https://arxiv.org/abs/2109.04786 · HTML: https://mlsanity.com/paper/2109.04786/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2021.

## Built on by (most cited first, 21 of 21)

- [Linear Combination of Exponential Moving Averages for Wireless Channel Prediction](https://mlsanity.com/paper/2312.07945/) (2023)
- [Predicting Wireless Channel Quality by means of Moving Averages and Regression Models](https://mlsanity.com/paper/2306.08634/) (2023)
- [AI/ML-based Load Prediction in IEEE 802.11 Enterprise Networks](https://mlsanity.com/paper/2310.07467/) (2023)
- [Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence](https://mlsanity.com/paper/2407.15320/) (2024)
- [Federated Spatial Reuse Optimization in Next-Generation Decentralized IEEE 802.11 WLANs](https://mlsanity.com/paper/2203.10472/) (2022)
- [ML Framework for Wireless MAC Protocol Design](https://mlsanity.com/paper/2402.07208/) (2024)
- [Deep Reinforcement Learning for Power Control in Next-Generation WiFi Network Systems](https://mlsanity.com/paper/2211.01107/) (2022)
- [A Federated Reinforcement Learning Framework for Link Activation in Multi-link Wi-Fi Networks](https://mlsanity.com/paper/2304.14720/) (2023)
- [Distributed Learning for Wi-Fi AP Load Prediction](https://mlsanity.com/paper/2405.05140/) (2024)
- [Machine Learning & Wi-Fi: Unveiling the Path Towards AI/ML-Native IEEE 802.11 Networks](https://mlsanity.com/paper/2405.11504/) (2024)
- [MAC Revivo: Artificial Intelligence Paves the Way](https://mlsanity.com/paper/2410.15820/) (2024)
- [Improving Wi-Fi Network Performance Prediction with Deep Learning Models](https://mlsanity.com/paper/2507.11168/) (2025)
- [Graph Representation Learning for Contention and Interference Management in Wireless Networks](https://mlsanity.com/paper/2402.00879/) (2024)
- [Mixing Neural Networks and Exponential Moving Averages for Predicting Wireless Links Behavior](https://mlsanity.com/paper/2411.11185/) (2024)
- [On the Accuracy and Precision of Moving Averages to Estimate Wi-Fi Link Quality](https://mlsanity.com/paper/2411.12265/) (2024)
- [Cross-Technology Interference: Detection, Avoidance, and Coexistence Mechanisms in the ISM Bands](https://mlsanity.com/paper/2501.06446/) (2025)
- [Accurate and Efficient Prediction of Wi-Fi Link Quality Based on Machine Learning](https://mlsanity.com/paper/2509.18933/) (2025)
- [AI-Enhanced Distributed Channel Access for Collision Avoidance in Future Wi-Fi 8](https://mlsanity.com/paper/2509.23154/) (2025)
- [On the Prediction of Wi-Fi Performance through Deep Learning](https://mlsanity.com/paper/2512.00211/) (2025)
- [Evidence-Grounded Ensemble Diagnosis of 802.11 Packet Captures: A Multi-Stage Pipeline with Deterministic Reliability Scoring](https://mlsanity.com/paper/2606.06871/) (2026)
- [An Informativeness-based Clustered Federated Learning Method for Reliable Traffic Prediction in Managed Wi-Fi Networks](https://mlsanity.com/paper/2607.26682/) (2026)

## Abstract (arXiv)

Wireless local area networks (WLANs) empowered by IEEE 802.11 (Wi-Fi) hold a dominant position in providing Internet access thanks to their freedom of deployment and configuration as well as the existence of affordable and highly interoperable devices. The Wi-Fi community is currently deploying Wi-Fi 6 and developing Wi-Fi 7, which will bring higher data rates, better multi-user and multi-AP support, and, most importantly, improved configuration flexibility. These technical innovations, including the plethora of configuration parameters, are making next-generation WLANs exceedingly complex as the dependencies between parameters and their joint optimization usually have a non-linear impact on network performance. The complexity is further increased in the case of dense deployments and coexistence in shared bands. While classical optimization approaches fail in such conditions, machine learning (ML) is able to handle complexity. Much research has been published on using ML to improve Wi-Fi performance and solutions are slowly being adopted in existing deployments. In this survey, we adopt a structured approach to describe the various Wi-Fi areas where ML is applied. To this end, we analyze over 250 papers in the field, providing readers with an overview of the main trends. Based on this review, we identify specific open challenges and provide general future research directions.