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

Device 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 a data-driven machine learning (ML) schemes to efficiently solve these problems in 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. For instance, the proposed scheme for handover prediction outperforms traditional methods i.e. RSS 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 SSF and LLF algorithms by achieving higher throughput gains upto 9.2% and 8% respectively.