On the Employment of Machine Learning Techniques for Troubleshooting WiFi Networks

Syrigos, Ilias; Sakellariou, Nikos; Keranidis, Stratos; Korakis, Thanasis

doi:10.1109/ccnc.2019.8651823

Cited by 12 publications

(2 citation statements)

References 10 publications

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“…Similarly, Syrigos et al [206] detect the causes of Wi-Fi under-performance, e.g., high contention with other Wi-Fi and non-Wi-Fi devices, operation in low SNR region, hidden terminal, or capture effect. A centralized Wi-Fi network controller collects two performance metrics from connected APs (i.e., those exposed by the ath9k driver):…”

Section: Predicting the Health Of Wi-fi Connectionsmentioning

confidence: 99%

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

Szott

Kosek-Szott

Gawłowicz

et al. 2022

IEEE Commun. Surv. Tutorials

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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.

show abstract

Section: Predicting the Health Of Wi-fi Connectionsmentioning

confidence: 99%

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

Szott

Kosek-Szott

Gawłowicz

et al. 2022

IEEE Commun. Surv. Tutorials

View full text Add to dashboard Cite

show abstract

“…Similarly, Syrigos et al [177] try to detect the causes of WiFi under-performance (e.g., high contention with other WiFi and non-WiFi devices, operation in low SNR region, hidden terminal, or capture effect). To this end, they deploy a centralized WiFi network controller which collects performance metrics from connected APs (i.e., those exposed by the ath9k driver).…”

Section: Predicting the Health Of Wifi Connectionsmentioning

confidence: 99%

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

Szott,

Kosek-Szott,

Gawłowicz

et al. 2021

Preprint

View full text Add to dashboard Cite

Wireless local area networks (WLANs) empowered by IEEE 802.11 (WiFi) hold a dominant position in providing Internet access thanks to their freedom of deployment and configuration as well as affordable and highly interoperable devices. The WiFi community is currently deploying WiFi 6 and developing WiFi 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 classic optimization approaches fail in such conditions, machine learning (ML) is well known for being able to handle complexity. Much research has been published on using ML to improve WiFi performance and solutions are slowly being adopted in existing deployments. In this survey, we adopt a structured approach to describing the various areas where WiFi can be enhanced using ML. To this end, we analyze over 200 papers in the field providing readers with an overview of the main trends. Based on this review, we identify both open challenges in each WiFi performance area as well as general future research directions.

show abstract

Passive Side-Channel Interference Estimation for WiFi Networks

Onalan¹,

Kurtoglu

Soyak

2021

2021 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom)

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On the Employment of Machine Learning Techniques for Troubleshooting WiFi Networks

Cited by 12 publications

References 10 publications

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

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

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

Passive Side-Channel Interference Estimation for WiFi Networks

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