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

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

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationInternational Symposium on Artificial Intelligence and Robotics 2020
EditorsHuimin Lu, Joze Guna, Yujie Li
PublisherSPIE
ISBN (Electronic)9781510639683
DOIs
Publication statusPublished - 2020
EventInternational Symposium on Artificial Intelligence and Robotics 2020 - Kitakyushu, Japan
Duration: 8 Aug 202010 Aug 2020

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11574
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceInternational Symposium on Artificial Intelligence and Robotics 2020
Country/TerritoryJapan
CityKitakyushu
Period8/08/2010/08/20

Keywords

  • Handover
  • Reinforcement learning
  • Ultra-dense networks

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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