WiFi network selection is typically based on the received signal strength of an access point (AP). However, the strongest signal does not necessarily lead to good user experience. For example, a strong signal or SSID may simultaneously attract many WiFi clients, causing congestion.
This disclosure utilizes machine learning models trained to intelligently select a wireless access point or an SSID based on multiple factors, e.g., neighboring APs, neighboring clients, historical service information, signal and interference levels, ping jitter, time-of-day, day-of-week, etc. Per the techniques, the selected AP is associated with a best overall score as determined by the machine learning model based on several factors. The selected AP therefore is not necessarily the AP with the strongest signal or geographically nearest to the client device making the selection. User experience is improved by selecting the AP in this manner.
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Lee, Jiwoong, "Intelligent wireless network selection", Technical Disclosure Commons, (January 23, 2019)