ML-based Handover Prediction and AP Selection in Cognitive Wi-Fi Networks
This addresses mobility challenges in Wi-Fi networks for improved network performance, but it is incremental as it applies existing ML techniques to a specific domain problem.
The paper tackled handover prediction and access point selection in dense Wi-Fi networks using data-driven machine learning schemes, resulting in a 60% and 50% reduction in unnecessary handovers compared to traditional methods and throughput gains up to 9.2% and 8% respectively.
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 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.