Contextual Multi-Armed Bandits for Non-Stationary Wireless Network Selection

As the number of wireless technologies (4G, 5G, 802.11ax and other) have been rapidly increasing, so have the number of wireless networks concurrently deployed on a given coverage area. As a result, the judicious selection of the network that maximizes the quality perceived by a user terminal has become a significantly relevant problem. Contextual Multi-Armed Bandits (CMAB) are viable models to approach the problem. While multiple CMAB algorithms have been designed, most of them are only suited for stationary environments. This work proposes a new set of network selection algorithms that relate the traffic type (used as contextual information) to the perceived quality of the available networks for non-stationary scenarios. Results show significantly improved performance when compared to non-adaptive approaches.