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Access Point Selection Using Reinforcement Learning in Dense Mobile Networks
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Publication Year
2020-01-01
Journal
International Conference on Information Networking
Publisher
IEEE Computer Society
Citation
International Conference on Information Networking, Vol.2020-January, pp.676-681
Mesh Keyword
Access point selectionCoverage problemHigh frequency HFOptimization problemsPerformance metricsQoS requirementsRandom access channelReinforcement learning techniques
All Science Classification Codes (ASJC)
Computer Networks and CommunicationsInformation Systems
Abstract
5G networks comprise a dense network of access points to mitigate the reduced coverage problem resulting from using high-frequency ranges such as mm-waves. Using these frequencies alleviates the problem of bandwidth scarcity as well. However, one of the challenges in this area is for the users to be able to select an efficient access point that benefits them in terms of meeting their QoS requirements, such as delay; and also reduce the random access channel congestion that occurs at the access points. In order to solve this problem, we first establish an optimization problem and experiment with a reinforcement learning-based scheme. We assess the results in terms of random access performance metrics. Experiment results demonstrate the effectiveness of the approach. In particular, implementing a reinforcement learning technique allowed a 44.5% reduction in average access delay and improved access success probability.
ISSN
1976-7684
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36576
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85082138372&origin=inward
DOI
https://doi.org/10.1109/icoin48656.2020.9016560
Journal URL
http://www.icoin.org/
Type
Conference
Funding
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019R1A2C1008530).
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Choi, Youngjune Image
Choi, Youngjune최영준
Department of Software and Computer Engineering
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