Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/155226
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dc.contributor.authorChen, Zhenweien_US
dc.contributor.authorZhang, Wenjieen_US
dc.contributor.authorZheng, Yifengen_US
dc.contributor.authorYang, Liwenen_US
dc.contributor.authorYeo, Chai Kiaten_US
dc.date.accessioned2022-02-28T02:51:19Z-
dc.date.available2022-02-28T02:51:19Z-
dc.date.issued2020-
dc.identifier.citationChen, Z., Zhang, W., Zheng, Y., Yang, L. & Yeo, C. K. (2020). Distributed algorithm for AP association with random arrivals and departures of users. IET Communications, 14(5), 846-856. https://dx.doi.org/10.1049/iet-com.2019.0817en_US
dc.identifier.issn1751-8628en_US
dc.identifier.urihttps://hdl.handle.net/10356/155226-
dc.description.abstractHere, the authors study the novel problem of optimising access point (AP) association by maximising the network throughput, subject to the degree bound of AP. The formulated problem is a combinatorial optimisation. They resort to the Markov Chain approximation technique to design a distributed algorithm. They first approximate their optimal objective via Log-Sum-Exp function. Thereafter, they construct a special class of Markov Chain with steady-state distribution specify to their problem to yield a distributed solution. Furthermore, they extend the static problem setting to a dynamic environment where the users can randomly leave or join the system. Their proposed algorithm has provable performance, achieving an approximation gap of (1/η)log ℱ. It is simple and can be implemented in a distributed manner. Their extensive simulation results show that the proposed algorithm can converge very fast, and achieve a close-to-optimal performance with a guaranteed loss bound.en_US
dc.language.isoenen_US
dc.relation.ispartofIET Communicationsen_US
dc.rights© 2020 The Institution of Engineering and Technology. All rights reserved.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleDistributed algorithm for AP association with random arrivals and departures of usersen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.identifier.doi10.1049/iet-com.2019.0817-
dc.identifier.scopus2-s2.0-85082077780-
dc.identifier.issue5en_US
dc.identifier.volume14en_US
dc.identifier.spage846en_US
dc.identifier.epage856en_US
dc.subject.keywordsCombinational Optimisationen_US
dc.subject.keywordsDistributed Algorithmen_US
dc.description.acknowledgementThis work was supported by the Natural Science Funds of China(Nos. 61701213, 61705260), Natural Science Funds of Fujian (No.2018J01546).en_US
item.grantfulltextnone-
item.fulltextNo Fulltext-
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