Intelligent Handover Triggering Mechanism in Wi-Fi Networks via Adaptation-based Reinforcement Learning

In large and densely deployed IEEE 802.11 (Wi-Fi) networks, a fast and seamless handover scheme is an important aspect in order to provide reliable connectivity for mobile users. However, most of conventional handover triggering mechanisms of mobile terminal (MT) is designed for fixed scenarios and thus could result in negative effects such as frequent handovers, ping-pong handovers, and handover failures on the handover process of MT at other scenarios. These effects degrade the overall network performance. To address these issues, this paper proposes an intelligent handover triggering mechanism for MT based on deep deterministic policy gradient (DDPG) frameworks with adaptation weight vectors and reward penalty mechanism (APM). The input metrics in one episode are converted to weight vectors for reward of DDPG, which can help MT to be applied in different scenarios. Meanwhile, a penalty mechanism for reward is executed in every step, which can reduce ping-pong handovers. Afterward, the DDPG framework learns the optimal handover triggering policy from the environment. The trained DDPG is deployed to MT to trigger the handover process. The results demonstrate that the proposed method can ensure the stronger mobility robustness of MT that is improved by 20%-96% compared to the conventional approach with respect to the number of handovers and ping-ping handover rate while maintaining a relatively proper level of throughput to guarantee of the MT operation.