@inproceedings{59bb4253dbd34c929602836c279e35a9,
title = "Mobility management for ultra-dense edge computing: A reinforcement learning approach",
abstract = "Mobile Edge Computing (MEC) is one of the promising solutions for delay-sensitive emerging applications. There are multiple available options to provide wireless access and computing service for users in the dense deployment of MEC-enabled small base stations (SBSs). It makes the mobility management (MM) more complicated. To this, we study the MM problem during the users{\^a}{\texttrademark} movement in the ultra- dense edge computing scenario to minimize the delay with handover cost as a penalty term of the offloading tasks. In this paper, we propose an online learning optimization scheme based on reinforcement learning to optimize handover decision-making by predicting the upcoming future information. Simulation results show that the proposed scheme can effectively reduce the average delay of users{\^a}{\texttrademark} computing tasks and the handover rate compared with the available conventional handover schemes.",
keywords = "Mobile edge computing, Mobility management, Reinforcement learning, Ultra-dense network",
author = "Haibin Zhang and Rong Wang and Jiajia Liu",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 90th IEEE Vehicular Technology Conference, VTC 2019 Fall ; Conference date: 22-09-2019 Through 25-09-2019",
year = "2019",
month = sep,
doi = "10.1109/VTCFall.2019.8891330",
language = "英语",
series = "IEEE Vehicular Technology Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2019 IEEE 90th Vehicular Technology Conference, VTC 2019 Fall - Proceedings",
}