A Survey on Prediction of PQoS Using Machine Learning on Wi-Fi Networks

Morshedi, Maghsoud; Noll, Josef

doi:10.1109/atc50776.2020.9255457

Cited by 7 publications

(9 citation statements)

References 14 publications

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“…Surveys of WiFi performance-indicators cover mostly WiFi data analytics for network monitoring [17] and quality indicators accounting for user satisfaction [18]. Concerning the WiFi analytics, reported ML models are used to extract useful knowledge from big data streams produced over large-scale wireless networks [17].…”

Section: A Wifi-related Surveysmentioning

confidence: 99%

“…Concerning the WiFi analytics, reported ML models are used to extract useful knowledge from big data streams produced over large-scale wireless networks [17]. Additionally, ML-based solutions to support the estimation of QoS, quality of experience (QoE), and their cross-correlation (QoS-QoE) are surveyed in [18]. Concerning WiFi-based applications, indoor localization [14], [19]- [21] and human activity detection [22] are the two main covered areas.…”

Section: A Wifi-related Surveysmentioning

confidence: 99%

“…Addressed WiFi feature Year WiFi [17] Large-scale network monitoring WiFi analytics 2020 [18] Quality indicators accounting for user satisfaction WiFi quality indicators 2020 [14] Indoor localization Application-oriented 2020 [19] 2019 [20] 2019 [21] 2018 [22] Human activity detection 2017 [15] Intrusion detection WiFi security 2021 [23] security issues. In the wireless networks area, the surveys in general provide few details concerning the use of ML models to improve performance of the 802.11 protocol family.…”

Section: Network Ref Main Scopementioning

confidence: 99%

“…The dataset was used in [318] to find hidden dependencies in low level WiFi performance data. Polese et al [112] released an experimental waveform dataset 17 generated using the NI mmWave transceiver system with 60 GHz radio heads, as well as the source code using Keras API for training and testing ML models 18 . Similar measurement data for indoor mmWave using 802.11ad from the papers by Aggarwal et al [107], [108] is also available 19 .…”

Section: B Datasetsmentioning

confidence: 99%

See 3 more Smart Citations

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

Szott,

Kosek-Szott,

Gawłowicz

et al. 2021

Preprint

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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.

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Section: A Wifi-related Surveysmentioning

confidence: 99%

Section: A Wifi-related Surveysmentioning

confidence: 99%

Section: Network Ref Main Scopementioning

confidence: 99%

Section: B Datasetsmentioning

confidence: 99%

See 2 more Smart Citations

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

Szott,

Kosek-Szott,

Gawłowicz

et al. 2021

Preprint

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“…Various research articles have investigated the prediction of the perceived quality of YouTube streaming as the quality of experience (QoE) using machine-learning, while some of them have considered Wi-Fi networks as the access network in their experiment setups. Morshedi and Noll surveyed the literature on the prediction of PQoS using machine-learning on Wi-Fi networks and presented that most of the literature used generic network performance parameters for estimating PQoS using external measurements such as packet capture or ping tests [ 22 ]. While these research efforts have used different network performance parameters to predict the perceived quality of YouTube streaming, they have leveraged very similar YouTube application parameters, such as the startup delay, number of rebufferings (stalling), and resolution (quality) switches to calculate the MOS or label instances with individual parameters in the ML dataset.…”

Section: Related Workmentioning

confidence: 99%

Estimating PQoS of Video Streaming on Wi-Fi Networks Using Machine Learning

Morshedi

Noll

2021

Sensors

Self Cite

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Video on demand (VoD) services such as YouTube have generated considerable volumes of Internet traffic in homes and buildings in recent years. While Internet service providers deploy fiber and recent wireless technologies such as 802.11ax to support high bandwidth requirement, the best-effort nature of 802.11 networks and variable wireless medium conditions hinder users from experiencing maximum quality during video streaming. Hence, Internet service providers (ISPs) have an interest in monitoring the perceived quality of service (PQoS) in customer premises in order to avoid customer dissatisfaction and churn. Since existing approaches for estimating PQoS or quality of experience (QoE) requires external measurement of generic network performance parameters, this paper presents a novel approach to estimate the PQoS of video streaming using only 802.11 specific network performance parameters collected from wireless access points. This study produced datasets comprising 802.11n/ac/ax specific network performance parameters labelled with PQoS in the form of mean opinion scores (MOS) to train machine learning algorithms. As a result, we achieved as many as 93–99% classification accuracy in estimating PQoS by monitoring only 802.11 parameters on off-the-shelf Wi-Fi access points. Furthermore, the 802.11 parameters used in the machine learning model were analyzed to identify the cause of quality degradation detected on the Wi-Fi networks. Finally, ISPs can utilize the results of this study to provide predictable and measurable wireless quality by implementing non-intrusive monitoring of customers’ perceived quality. In addition, this approach reduces customers’ privacy concerns while reducing the operational cost of analytics for ISPs.

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Analysis of Classification Methods for Wi-Fi Signals Based on the Use of Channel State Information Spatial Features and CNN

Lopatin

Fyodorov

2022

Springer Proceedings in Physics

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A Survey on Prediction of PQoS Using Machine Learning on Wi-Fi Networks

Cited by 7 publications

References 14 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

Estimating PQoS of Video Streaming on Wi-Fi Networks Using Machine Learning

Analysis of Classification Methods for Wi-Fi Signals Based on the Use of Channel State Information Spatial Features and CNN

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