Learning-aided client association control for high-density WLANs

As wireless local area network (WLAN) continues to become popular, there is an increasing number of clients with huge data traffic demands. Especially, some high client-density environments are emerging, such as industrial plants, stadiums, and event centers, which poses significant challenges in terms of client association control. Under such environments, conventional client-side solutions that select access points (APs) according to simple indicators such as signal strength may result in poor network performance, and although some centralized association control mechanisms are proposed, it is still difficult that a large amount of complex global network status information needs to be effectively and efficiently utilized. To meet these challenges, we investigate the online centralized association control problem that aims to improve user quality of experience (QoE), and propose a deep reinforcement learning (DRL) aided solution, called Wi-OAC, where an image-like state pattern is designed to achieve state reformulation for deep Q-network (DQN), and the double DQN and dueling DQN strategies are combined to improve convergence speed. On the basis of offline training, Wi-OAC can determine the proper AP-client associations for the arriving clients. Both simulation experiments and real-world experiments have been conducted to validate the effectiveness of Wi-OAC. In real-world experiments, we build a Wi-OAC testbed including 3 APs and 54 clients in less than 10.5 m2 area, and the results show that Wi-OAC can significantly improve the performance on the client throughput, AP load balancing and user QoE.