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Device mobility in dense Wi-Fi networks offers several challenges. Twowell-known problems related to device mobility are handover prediction andaccess point selection. Due to the complex nature of the radio environment,analytical models may not characterize the wireless channel, which makes thesolution of these problems very difficult. Recently, cognitive networkarchitectures using sophisticated learning techniques are increasingly beingapplied to such problems. In this paper, we propose data-driven machinelearning (ML) schemes to efficiently solve these problems in wireless LAN(WLAN) networks. The proposed schemes are evaluated and results are comparedwith traditional approaches to the aforementioned problems. The results reportsignificant improvement in network performance by applying the proposedschemes. The proposed scheme for handover prediction outperforms traditionalmethods i.e. received signal strength method and traveling distance method byreducing the number of unnecessary handovers by 60% and 50% respectively.Similarly, in AP selection, the proposed scheme outperforms the strongestsignal first and least loaded first algorithms by achieving higher throughputgains up to 9.2% and 8% respectively.

ML-based Handover Prediction and AP Selection in Cognitive Wi-Fi Networks