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

arXiv: https://arxiv.org/abs/2111.13879 · HTML: https://mlsanity.com/paper/2111.13879/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 56 references that are in the map.
- First posted on arXiv 27 Nov 2021.

## Builds on (most cited first, 2 of 2)

- [Estimating the number of receiving nodes in 802.11 networks via machine learning techniques](https://mlsanity.com/paper/1605.04144/) (2016)
- [Edge Computing in IoT: A 6G Perspective](https://mlsanity.com/paper/2111.08943/) (2021)

## Built on by (most cited first, 5 of 5)

- [Improving Wi-Fi Network Performance Prediction with Deep Learning Models](https://mlsanity.com/paper/2507.11168/) (2025)
- [Cross-network transferable neural models for WLAN interference estimation](https://mlsanity.com/paper/2211.14026/) (2022)
- [Data-Driven Cellular Network Selector for Vehicle Teleoperations](https://mlsanity.com/paper/2410.19791/) (2024)
- [Accurate and Efficient Prediction of Wi-Fi Link Quality Based on Machine Learning](https://mlsanity.com/paper/2509.18933/) (2025)
- [On the Prediction of Wi-Fi Performance through Deep Learning](https://mlsanity.com/paper/2512.00211/) (2025)

## Abstract (arXiv)

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.