Optimizing wi-fi rssi and channel assignments using a genetic algorithm for wi-fi tuning

Abstract

In this work, we propose a genetic algorithm-based Wi-Fi-tuning platform to facilitate network administrators in coping with the co-channel interference triggered by other wireless sources. Generally, with a well-designed WLAN, signal interference from adjacent areas is usually minimal. Unfortunately, when other wireless sources are introduced into the WLAN system, co-channel interference is in-evitable. Interference usually causes degradation and/or disruption in network services. Resolving this issue becomes even more complicated when the interfering signals come from access points owned by other ISPs and are not accessible by the network administrators. This paper proposes a Wi-Fi tuning platform that allows the automatic reconfiguration of WLAN settings by finding the best settings for channel assignment and power transmission. When signal interference is detected, the platform attempts to find heuristic solutions for wireless settings based on a genetic algorithm. Our experiments show that the proposed algorithm can regenerate the WLAN settings, providing stronger signal levels and higher coverage ranges while reducing interference levels in the deployment area. With the proposed platform, troubleshooting becomes less complicated, requiring less cost and time. With the help of the Wi-Fi tuning platform, network administrators can react promptly to incidents, enhancing the availability, reliability, and consistency of the WLAN system.