Please use this identifier to cite or link to this item: https://idr.l2.nitk.ac.in/jspui/handle/123456789/13170
Full metadata record
DC FieldValueLanguage
dc.contributor.authorPlets, D.
dc.contributor.authorChemmangat, K.
dc.contributor.authorDeschrijver, D.
dc.contributor.authorMehari, M.
dc.contributor.authorUlaganathan, S.
dc.contributor.authorPakparvar, M.
dc.contributor.authorDhaene, T.
dc.contributor.authorHoebeke, J.
dc.contributor.authorMoerman, I.
dc.contributor.authorTanghe, E.
dc.date.accessioned2020-03-31T08:45:20Z-
dc.date.available2020-03-31T08:45:20Z-
dc.date.issued2017
dc.identifier.citationWireless Networks, 2017, Vol.23, 8, pp.2347-2359en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/13170-
dc.description.abstractDue to the rapid growth of wireless networks and the dearth of the electromagnetic spectrum, more interference is imposed to the wireless terminals which constrains their performance. In order to mitigate such performance degradation, this paper proposes a novel experimentally verified surrogate model based cognitive decision engine which aims at performance optimization of IEEE 802.11 links. The surrogate model takes the current state and configuration of the network as input and makes a prediction of the QoS parameter that would assist the decision engine to steer the network towards the optimal configuration. The decision engine was applied in two realistic interference scenarios where in both cases, utilization of the cognitive decision engine significantly outperformed the case where the decision engine was not deployed. 2016, Springer Science+Business Media New York.en_US
dc.titleSurrogate modeling based cognitive decision engine for optimization of WLAN performanceen_US
dc.typeArticleen_US
Appears in Collections:1. Journal Articles

Files in This Item:
There are no files associated with this item.


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