A Cascading Bandit Approach to Efficient Mobility Management in Ultra-Dense Networks

Chao Wang, Ruida Zhou, Jing Yang, Cong Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Efficient mobility management is a key problem in modern wireless networks with high node density. In this paper, we propose an online learning approach for mobility management in ultra-dense networks, based on the cascading multi-armed bandits model. The proposed Cost-aware Cascading Bandit Neighbor Cell List (CCB-NCL) mobility protocol relies on the active neighbor cell list to assist the user equipment to explore the base station selection sequentially. Simulation results show that the proposed algorithm reduces the handover latency with lower dropped call rate, hence it is a better fit to efficient mobility management.

Original languageEnglish (US)
Title of host publication2019 IEEE 29th International Workshop on Machine Learning for Signal Processing, MLSP 2019
PublisherIEEE Computer Society
ISBN (Electronic)9781728108247
DOIs
StatePublished - Oct 2019
Event29th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2019 - Pittsburgh, United States
Duration: Oct 13 2019Oct 16 2019

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing, MLSP
Volume2019-October
ISSN (Print)2161-0363
ISSN (Electronic)2161-0371

Conference

Conference29th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2019
CountryUnited States
CityPittsburgh
Period10/13/1910/16/19

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Signal Processing

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  • Cite this

    Wang, C., Zhou, R., Yang, J., & Shen, C. (2019). A Cascading Bandit Approach to Efficient Mobility Management in Ultra-Dense Networks. In 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing, MLSP 2019 [8918838] (IEEE International Workshop on Machine Learning for Signal Processing, MLSP; Vol. 2019-October). IEEE Computer Society. https://doi.org/10.1109/MLSP.2019.8918838