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

Abstract

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.

54 pages, 23 figures, 384 references

References in corpus (7)

• Towards Explainable Artificial Intelligence
• Rainbow: Combining Improvements in Deep Reinforcement Learning
• Machine Learning on Camera Images for Fast mmWave Beamforming
• Machine Learning for the Detection and Identification of Internet of Things (IoT) Devices: A Survey
• The Future is Unlicensed: Coexistence in the Unlicensed Spectrum for 5G
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Cited by in corpus (11)

• A Survey of Beam Management for mmWave and THz Communications Towards 6G
• A Survey on Multi-AP Coordination Approaches over Emerging WLANs: Future Directions and Open Challenges
• Wireless MAC Protocol Synthesis and Optimization with Multi-Agent Distributed Reinforcement Learning
• Predicting Wireless Channel Quality by means of Moving Averages and Regression Models
• Linear Combination of Exponential Moving Averages for Wireless Channel Prediction
• ML Framework for Wireless MAC Protocol Design
• FTMRate: Collision-Immune Distance-based Data Rate Selection for IEEE 802.11 Networks
• Mixing Neural Networks and Exponential Moving Averages for Predicting Wireless Links Behavior
• A Software Platform for Testing Multi-Link Operation in Industrial Wi-Fi Networks
• On the Accuracy and Precision of Moving Averages to Estimate Wi-Fi Link Quality
• On the Prediction of Wi-Fi Performance through Deep Learning