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AuthorHe, Yu Lin
AuthorYe, Xuan
AuthorCui, Laizhong
AuthorFournier-Viger, Philippe
AuthorLuo, Chengwen
AuthorHuang, Joshua Zhexue
AuthorSuganthan, Ponnuthurai N.
Available date2023-02-13T08:56:59Z
Publication Date2022-05-30
Publication NameIEEE Transactions on Network Science and Engineering
Identifierhttp://dx.doi.org/10.1109/TNSE.2022.3178740
CitationHe, Y., Ye, X., Cui, L., Fournier-Viger, P., Luo, C., Huang, J. Z., & Suganthan, P. N. (2022). Wireless network slice assignment with incremental random vector functional link network. IEEE Transactions on Network Science and Engineering, 10(3), 1283-1296.
ISSN2334-329X
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85131733667&origin=inward
URIhttp://hdl.handle.net/10576/40008
AbstractThis paper presents an artificial intelligence-assisted network slice prediction method, which utilizes a novel incremental random vector functional link (IRVFL) network to deal with the wireless network slice assignment (WNSA) problem in a data-driven way. The goal of WNSA is to assign an appropriate network slice for a user's requirement based on the next-generation wireless derive and communication data. The IRVFL network is an incremental version of the RVFL network, where a data stream processing approach is used to gradually update output layer weights as new data arrive rather than processing the data as a single large data set. To ensure that the RVFL network can be trained for WNSA and have high adaptability and expansibility, we derive a novel flexible and appropriate rule for updating output layer weights of the IRVFL network. We have carried out extensive experiments to validate the feasibility, rationality, and effectiveness of using the IRVFL network for the WNSA problem. Results show that network slice prediction converges as the IRVFL network is incrementally trained and that the time required for training the incremental RVFL network is far less than for the non-incremental RVFL network. The incremental training of IRVFL network improves the performance of wireless network slice prediction. In addition, a comparison with six classification algorithms reveal that the IRVFL network consumes the least amount of time and has equivalent wireless network slice prediction performance.
SponsorThis work was supported in part by the National Key Research and Development Plan of China under Grant 2018YFB1800805, in part by the National Natural Science Foundation of China under Grants 61972261 and 61772345, in part by the Basic Research Foundation of Shenzhen under Grants JCYJ20210324093609026 and JCYJ 20190808142207420, in part by Shenzhen Science and Technology Program under Grants RCYX20200714114645048 and GJHZ20190822095416463, and in part by the Pearl River Young Scholars Funding of Shenzhen University.
Languageen
PublisherIEEE Computer Society
Subject5G network
6G network
data stream computation
Fourth Industrial Revolution
incremental learning
Medical services
Modulation
Moore-Penrose generalized inverse
Network slicing
Next generation networking
Quality of service
random vector functional link network
wireless network slice
Wireless networks
TitleWireless Network Slice Assignment with Incremental Random Vector Functional Link Network
TypeArticle
Pagination1283-1296
Issue Number3
Volume Number10
ESSN2327-4697
dc.accessType Abstract Only


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