Distributed user-to-multiple access points association through deep learning for beyond 5G

Dinh, Thi Ha Ly; Kaneko, Megumi; Wakao, Keisuke; Kawamura, Kazuya; Moriyama, Takatsune; Abeysekera, Hirantha; Takatori, Yasushi

doi:10.1016/j.comnet.2021.108258

Cited by 11 publications

(7 citation statements)

References 11 publications

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“…Seob Oh et al [26] presented a user association scheme for channel load balancing based on channel quality, traffic volume, and channel interference. Ly Dinh et al [27] proposed a distributed user-to-multiple AP association approach that maximises both QoS and AP load constraints. El Khaled et al [28] proposed two algorithms that predict the success of a user association to APs in fixed wireless networks for rural and harsh propagation environments respectively.…”

Section: B Ap Association Solutionsmentioning

confidence: 99%

A Trust-Based Cooperative System for Efficient Wi-Fi Radio Access Networks

Raschellà,

Eiza,

Mackay

et al. 2023

IEEE Access

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This paper proposes a novel trust-based cooperative system to facilitate efficient Wi-Fi network access trading to solve the network congestion problem in a beneficial manner for both service providers and customers. The proposed system enables service providers to improve their users' application performance through a novel cooperative Access Point (AP) association solution. The system is based on a Software-Defined Wireless Network (SDWN) controller, which has a global view of users' devices, requirements, and APs. The SDWN controller is supported by Smart Contracts (SCs) as code of law, to liaise control among service providers according to the terms of their mutual agreements. Evaluation results in dense Wi-Fi network environments show how the system can significantly improve the overall performance for the cooperating network. Specifically, the results have been compared against the standard AP association approach and other centralised algorithms dealing with the same problem, in terms of the data bit rate provided to the users' stations (STAs), Quality of Experience (QoE), bandwidth and energy consumed by the APs.

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Section: B Ap Association Solutionsmentioning

confidence: 99%

A Trust-Based Cooperative System for Efficient Wi-Fi Radio Access Networks

Raschellà,

Eiza,

Mackay

et al. 2023

IEEE Access

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“…An interesting RL-based scheme of user-to-multiple AP association is presented by Dinh et al [198]. Two distributed association methods based on deep Q-learning (DQL) enable stations to learn their best set of APs to connect to, using only local knowledge of the wireless environment or with a limited feedback from the APs.…”

Section: A Channel and Band Selectionmentioning

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.

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“…An interesting scheme of user-to-multiple AP association was presented in [164]. The authors proposed two distributed association methods based on Deep Q-Learning (DQL), where a station learns its best set of APs to be connected i) solely using local knowledge of the wireless environment and ii) with limited feedback from AP.…”

Section: B Ap Selection and Associationmentioning

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

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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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Distributed user-to-multiple access points association through deep learning for beyond 5G

Cited by 11 publications

References 11 publications

A Trust-Based Cooperative System for Efficient Wi-Fi Radio Access Networks

A Trust-Based Cooperative System for Efficient Wi-Fi Radio Access Networks

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

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