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Erschienen in:
2021 | OriginalPaper | Buchkapitel
Reinforcement Learning for Optimizing Wi-Fi Access Channel Selection
verfasst von : Hung Nguyen, Duc Long Pham, Mau Hien Doan, Thi Thanh Sang Nguyen, Duc Anh Vu Dinh, Adrianna Kozierkiewicz
Erschienen in: Advances in Computational Collective Intelligence
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Abstract
Wi-Fi’s success is largely a testament to its cost-effectiveness, convenience, and ease of integration with other networks. Wi-Fi allows a suddenly increased number of users to access network services within their networking environment from any convenient location. However, wireless networks have a frequent issue of losing packets caused by poor Wi-Fi signal, network interference, and long-distance connection. This study primarily analyses the mentioned Wi-Fi issues and demonstrates the solution to maintain a reliable network connection within an area including multiple access points and devices by using Reinforcement Learning (RL). The RL algorithm is developed to recommend appropriate channels for the access points in a wireless network environment. The case study of Wi-Fi access data at a university is examined to evaluate the proposed method. Experimental results have shown that the RL-based Wi-Fi access channel selection can achieve higher performance than manual channel selection.