Reinforcement learning based adaptive handover in ultra-dense cellular networks with small cells

Qianyu Liu; Chiew Foong Kwong; Wei Sun; Lincan Li; Haoyu Zhao

doi:10.1117/12.2580119

12 October 2020 Reinforcement learning based adaptive handover in ultra-dense cellular networks with small cells

Qianyu Liu, Chiew Foong Kwong, Wei Sun, Lincan Li, Haoyu Zhao

Proceedings Volume 11574, International Symposium on Artificial Intelligence and Robotics 2020; 115740F (2020) https://doi.org/10.1117/12.2580119
Event: International Symposium on Artificial Intelligence and Robotics (ISAIR), 2020, Kitakyushu, Japan

Abstract

The dense deployment of the small base station (BS) in fifth-generation commination system can satisfy the user demand on high data rate transmission. On the other hand, such a scenario also increases the complexity of mobility management. In this paper, we developed a Q-learning framework exploiting user radio condition, that is, reference signal receiving power (RSRP), signal to inference and noise ratio (SINR) and transmission distance to learn the optimal policy for handover triggering. The objective of the proposed approach is to increase the mobility robustness of user in ultra-dense networks (UDNs) by minimizing redundant handover and handover failure ratio. Simulation results show that our proposed triggering mechanism efficiency suppresses ping-pong handover effect while maintaining handover failure at an acceptable level. Besides, the proposed triggering mechanism can trigger the handover process directly without HOM and TTT. The respond speed of triggering mechanism can thus be increased.

Citation Download Citation

Qianyu Liu, Chiew Foong Kwong, Wei Sun, Lincan Li, and Haoyu Zhao "Reinforcement learning based adaptive handover in ultra-dense cellular networks with small cells", Proc. SPIE 11574, International Symposium on Artificial Intelligence and Robotics 2020, 115740F (12 October 2020); https://doi.org/10.1117/12.2580119

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