A Data-Driven Approach to Client-Transparent Access Selection of Dual-Band WiFi

Dual-band WiFi which supports both 2.4 GHz and 5 GHz has been widely deployed, aiming to expand wireless capacity, and eliminate serious interference in 2.4 GHz. As the proportion of dual-band APs and clients increase enormously, how to select which band to access to achieve considerable user experience is becoming essential in wireless network. Clients’ native decisions that tend to prefer 5 GHz will consequently cause serious interference in 5 GHz and leave 2.4 GHz notably idle. Through analyzing a unique dataset in the wild, we quantitatively study the impact of various WiFi factors on the wireless delay. We propose a decision tree approach to intelligent access selection that decides which band to access dynamically according to prior learned schemes. A prototype of the access selection system named LazyAS , which only requires modification at AP side, is realized and deployed in a production WiFi network. Evaluation results demonstrate that LazyAS reduces the 90th percentile of wireless delay in the production WiFi network from 32 ms to 12 ms.