KLI

Machine Learning-Based Mobility Robustness Optimization Under Dynamic Cellular Networks

Metadata Downloads
Abstract
In this paper, we propose a machine learning?based mobility robustness optimization algorithm to optimize handover parameters for seamless mobility under dynamic small-cell networks. Small cells can be arbitrarily deployed, portable, and turned on and off to fulfill wireless traffic demands or energy efficiency. As a result, the small-cell network topology dynamically varies challenging network optimization, especially handover optimization. Previous studies have only considered dynamics due to user mobility in a specific static network topology. To optimize handovers under dynamic network topologies, together with user mobility, we propose an algorithm consisting of two steps: topology adaptation and mobility adaptation. To adapt to a dynamic topology, the algorithm obtains prior knowledge, which presents a belief distribution of the optimal handover parameters, for the current network topology as coarse optimization. In the second step, the algorithm fine-tunes the handover parameters to adapt to user mobility based on reinforcement learning, which utilizes the knowledge obtained during the first step. Under a dynamic small-cell network, we showed that the proposed algorithm reduced adaptation time to 4.17% of the time needed by a comparative machine?based algorithm. Furthermore, the proposed algorithm improved the user satisfaction rate to 416.7% compared to the previous work.
Author(s)
Ninh-Thang Nguyen권성오
Issued Date
2021
Type
Article
Keyword
transfer learningoptimizationdistributed reinforcement learningHandoverhandover optimizationHeuristic algorithmsNetwork topologyself-organizing networksmall cell on/offTopologyTransfer learning
DOI
10.1109/ACCESS.2021.3083554
URI
https://oak.ulsan.ac.kr/handle/2021.oak/9069
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_crossref_primary_10_1109_ACCESS_2021_3083554&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,Machine%20Learning-Based%20Mobility%20Robustness%20Optimization%20Under%20Dynamic%20Cellular%20Networks&offset=0&pcAvailability=true
Publisher
IEEE ACCESS
Location
미국
Language
영어
ISSN
2169-3536
Citation Volume
9
Citation Number
1
Citation Start Page
77830
Citation End Page
77844
Appears in Collections:
Engineering > IT Convergence
Authorize & License
  • Authorize공개
Files in This Item:
  • There are no files associated with this item.

Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.