A learning strategy for paging in mobile environments

Koukoutsidis, IZ; Demestichas, PP; Theologou, ME

dc.contributor.author	Koukoutsidis, IZ	en
dc.contributor.author	Demestichas, PP	en
dc.contributor.author	Theologou, ME	en
dc.date.accessioned	2014-03-01T01:18:32Z
dc.date.available	2014-03-01T01:18:32Z
dc.date.issued	2003	en
dc.identifier.issn	15308669	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/15067
dc.subject	Location area	en
dc.subject	Movement area	en
dc.subject	Online algorithm	en
dc.subject	Paging cost	en
dc.subject	Reinforcement learning	en
dc.subject	Tracking mobile users	en
dc.subject.other	Algorithms	en
dc.subject.other	Computational methods	en
dc.subject.other	Heuristic methods	en
dc.subject.other	Learning systems	en
dc.subject.other	Probability distributions	en
dc.subject.other	Random processes	en
dc.subject.other	Tracking (position)	en
dc.subject.other	Wireless telecommunication systems	en
dc.subject.other	Location areas (LA)	en
dc.subject.other	Movement areas	en
dc.subject.other	Online algorithm	en
dc.subject.other	Paging cost	en
dc.subject.other	Reinforcement learning	en
dc.subject.other	Tracking mobile users	en
dc.subject.other	Mobile computing	en
dc.title	A learning strategy for paging in mobile environments	en
heal.type	journalArticle	en
heal.identifier.primary	10.1002/wcm.120	en
heal.identifier.secondary	http://dx.doi.org/10.1002/wcm.120	en
heal.publicationDate	2003	en
heal.abstract	The essence of designing a good paging strategy is to incorporate the user mobility characteristics in a predictive mechanism that reduces the average paging cost with as little computational effort as possible. In this scope, we introduce a novel paging scheme based on the concept of reinforcement learning. Learning endows the paging mechanism with the predictive power necessary to determine a mobile terminal's position, without having to extract a location probability distribution for each specific user. The proposed algorithm is compared against a heuristic randomized learning strategy akin to reinforcement learning, that we invented for this purpose, and performs better than the case where no learning is used at all. It is shown that if the user normally moves only among a fraction of cells in the location area, significant savings can be achieved over the randomized strategy, without excessive time to train the network. Copyright © 2003 John Wiley & Sons, Ltd.	en
heal.journalName	Wireless Communications and Mobile Computing	en
dc.identifier.doi	10.1002/wcm.120	en
dc.identifier.volume	3	en
dc.identifier.issue	8	en
dc.identifier.spage	975	en
dc.identifier.epage	985	en