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dc.contributor.authorShen, C.en_US
dc.contributor.authorTekin, C.en_US
dc.contributor.authorVan Der Schaar, M.en_US
dc.date.accessioned2018-04-12T10:43:07Z
dc.date.available2018-04-12T10:43:07Z
dc.date.issued2016en_US
dc.identifier.issn0733-8716
dc.identifier.urihttp://hdl.handle.net/11693/36521
dc.description.abstractEnergy efficient mobility management is an important problem in modern wireless networks with heterogeneous cell sizes and increased nodes densities. We show that optimization-based mobility protocols cannot achieve long-Term optimal energy consumption, particularly for ultra-dense networks (UDNs). To address the complex dynamics of UDN, we propose a non-stochastic online-learning approach, which does not make any assumption on the statistical behavior of the small base station (SBS) activities. In addition, we introduce handover cost to the overall energy consumption, which forces the resulting solution to explicitly minimize frequent handovers. The proposed batched randomization with exponential weighting (BREW) algorithm relies on batching to explore in bulk, and hence reduces unnecessary handovers. We prove that the regret of BREW is sublinear in time, thus guaranteeing its convergence to the optimal SBS selection. We further study the robustness of the BREW algorithm to delayed or missing feedback. Moreover, we study the setting where SBSs can be dynamically turned ON and OFF. We prove that sublinear regret is impossible with respect to arbitrary SBS ON/OFF, and then develop a novel learning strategy, called ranking expert (RE), that simultaneously takes into account the handover cost and the availability of SBS. To address the high complexity of RE, we propose a contextual ranking expert (CRE) algorithm that only assigns experts in a given context. Rigorous regret bounds are proved for both RE and CRE with respect to the best expert. Simulations show that not only do the proposed mobility algorithms greatly reduce the system energy consumption, but they are also robust to various dynamics which are common in practical ultra-dense wireless networks.en_US
dc.language.isoEnglishen_US
dc.source.titleIEEE Journal on Selected Areas in Communicationsen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/JSAC.2016.2612038en_US
dc.subjectEnergy efficient mobility managementen_US
dc.subjectFrequent handover (FHO)en_US
dc.subjectNon-stochastic learningen_US
dc.subjectUltra-dense networks (UDN)en_US
dc.subjectComplex networksen_US
dc.subjectEnergy utilizationen_US
dc.subjectOptimizationen_US
dc.subjectStochastic systemsen_US
dc.subjectWireless networksen_US
dc.subjectDense networken_US
dc.subjectDense wireless networksen_US
dc.subjectExponential weightingen_US
dc.subjectHandoveren_US
dc.subjectMobility managementen_US
dc.subjectNon-stochasticen_US
dc.subjectStatistical behavioren_US
dc.subjectSystem energy consumptionen_US
dc.subjectEnergy efficiencyen_US
dc.titleA non-atochastic learning approach to energy efficient mobility managementen_US
dc.typeArticleen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.citation.spage3854en_US
dc.citation.epage3868en_US
dc.citation.volumeNumber34en_US
dc.citation.issueNumber12en_US
dc.identifier.doi10.1109/JSAC.2016.2612038en_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US


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