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48 UEC Int’l Mini-Conference No.52

Intelligent Handover Triggering Mechanism in Wi-Fi Networks via

Adaptation-based Reinforcement Learning

Nian Ding , Celimuge Wu, Yangfei Lin, Zhaoyang Du
∗
Department of Computer and Network Engineering
The University of Electro-Communications
Tokyo, Japan

Keywords: Handover management, Deep deterministic policy gradient (DDPG), Self-adaptive weight
vectors, Wi-Fi networks.

Abstract
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 2096the number of
handovers and ping-ping handover rate while maintaining a relatively proper level of throughput to
guarantee of the MT operation.

∗
The author is supported by (MICH) MEXT Scholarship.