Q-Learning Based Intelligent Traffic Steering in Heterogeneous Network

In this paper, we present a user equipment (UE) based distributed traffic steering mechanism between long-term evolution (LTE) and Wi-Fi networks. An agent residing in each UE evaluates the traffic condition of the network it is currently connecting to and makes the traffic steering decision. The evaluation is either periodic or event-driven such as access denial in the admission control due to network congestion. The learning mechanism enables each UE to use the locally available information at the UE and select the proper network under dynamic network conditions. The computer simulation results show that the proposed mechanism achieves low outage probability and small number of network switching with even less information than or almost the same as the existing method. We have also implemented the proposed traffic steering mechanism as an APP on android platform and verified that the proposed mechanism works effectively in real-time testing.