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AuthorKhan, Muhammad Asif
AuthorHamila, Ridha
AuthorGastli, Adel
AuthorKiranyaz, Serkan
AuthorAl-Emadi, Nasser Ahmed
Available date2022-11-23T11:25:32Z
Publication Date2022
Publication NameJournal of Network and Systems Management
ResourceScopus
Resource2-s2.0-85137077870
URIhttp://dx.doi.org/10.1007/s10922-022-09684-2
URIhttp://hdl.handle.net/10576/36629
AbstractDevice mobility in dense Wi-Fi networks offers several challenges. Two well-known problems related to device mobility are handover prediction and access point selection. Due to the complex nature of the radio environment, analytical models may not characterize the wireless channel, which makes the solution of these problems very difficult. Recently, cognitive network architectures using sophisticated learning techniques are increasingly being applied to such problems. In this paper, we propose data-driven machine learning (ML) schemes to efficiently solve these problems in wireless LAN (WLAN) networks. The proposed schemes are evaluated and results are compared with traditional approaches to the aforementioned problems. The results report significant improvement in network performance by applying the proposed schemes. The proposed scheme for handover prediction outperforms traditional methods i.e. received signal strength method and traveling distance method by reducing the number of unnecessary handovers by 60% and 50% respectively. Similarly, in AP selection, the proposed scheme outperforms the strongest signal first and least loaded first algorithms by achieving higher throughput gains up to 9.2% and 8% respectively. 2022, The Author(s).
Languageen
PublisherSpringer
SubjectAccess point selection
Cognitive networks
Handover
Machine learning
Throughput
Wi-Fi
TitleML-Based Handover Prediction and AP Selection in Cognitive Wi-Fi Networks
TypeArticle
Issue Number4
Volume Number30
dc.accessType Open Access


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