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Article: Renewables powered cellular networks: Energy field modeling and network coverage

TitleRenewables powered cellular networks: Energy field modeling and network coverage
Authors
KeywordsCellular networks
Energy harvesting
Renewable energy sources
Stochastic processes
Issue Date2014
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7693
Citation
IEEE Transactions on Wireless Communications, 2014, v. 14 n. 8, p. 4234-4247 How to Cite?
AbstractPowering radio access networks using renewables, such as wind and solar power, promises dramatic reduction in the network operation cost and the network carbon footprints. However, the spatial variation of the energy field can lead to fluctuations in power supplied to the network and thereby affects its coverage. This warrants research on quantifying the aforementioned negative effect and designing countermeasure techniques, motivating the current work. First, a novel energy field model is presented, in which fixed maximum energy intensity γ occurs at Poisson distributed locations, called energy centers. The intensities fall off from the centers following an exponential decay function of squared distance and the energy intensity at an arbitrary location is given by the decayed intensity from the nearest energy center. The product between the energy center density and the exponential rate of the decay function, denoted as ψ, is shown to determine the energy field distribution. Next, the paper considers a cellular downlink network powered by harvesting energy from the energy field and analyzes its network coverage. For the case of harvesters deployed at the same sites as base stations (BSs), as γ increases, the mobile outage probability is shown to scale as (cγ-πψ+p), where p is the outage probability corresponding to a flat energy field and $c$ is a constant. Subsequently, a simple scheme is proposed for counteracting the energy randomness by spatial averaging. Specifically, distributed harvesters are deployed in clusters and the generated energy from the same cluster is aggregated and then redistributed to BSs. As the cluster size increases, the power supplied to each BS is shown to converge to a constant proportional to the number of harvesters per BS. Several additional issues are investigated in this paper, including regulation of the power transmission loss in energy aggregation and extensions of the energy field model. © 2002-2012 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/214841
ISSN
2015 Impact Factor: 2.925
2015 SCImago Journal Rankings: 2.340

 

DC FieldValueLanguage
dc.contributor.authorHuang, K-
dc.contributor.authorKountouris, M-
dc.contributor.authorLi, VOK-
dc.date.accessioned2015-08-21T11:58:15Z-
dc.date.available2015-08-21T11:58:15Z-
dc.date.issued2014-
dc.identifier.citationIEEE Transactions on Wireless Communications, 2014, v. 14 n. 8, p. 4234-4247-
dc.identifier.issn1536-1276-
dc.identifier.urihttp://hdl.handle.net/10722/214841-
dc.description.abstractPowering radio access networks using renewables, such as wind and solar power, promises dramatic reduction in the network operation cost and the network carbon footprints. However, the spatial variation of the energy field can lead to fluctuations in power supplied to the network and thereby affects its coverage. This warrants research on quantifying the aforementioned negative effect and designing countermeasure techniques, motivating the current work. First, a novel energy field model is presented, in which fixed maximum energy intensity γ occurs at Poisson distributed locations, called energy centers. The intensities fall off from the centers following an exponential decay function of squared distance and the energy intensity at an arbitrary location is given by the decayed intensity from the nearest energy center. The product between the energy center density and the exponential rate of the decay function, denoted as ψ, is shown to determine the energy field distribution. Next, the paper considers a cellular downlink network powered by harvesting energy from the energy field and analyzes its network coverage. For the case of harvesters deployed at the same sites as base stations (BSs), as γ increases, the mobile outage probability is shown to scale as (cγ-πψ+p), where p is the outage probability corresponding to a flat energy field and $c$ is a constant. Subsequently, a simple scheme is proposed for counteracting the energy randomness by spatial averaging. Specifically, distributed harvesters are deployed in clusters and the generated energy from the same cluster is aggregated and then redistributed to BSs. As the cluster size increases, the power supplied to each BS is shown to converge to a constant proportional to the number of harvesters per BS. Several additional issues are investigated in this paper, including regulation of the power transmission loss in energy aggregation and extensions of the energy field model. © 2002-2012 IEEE.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7693-
dc.relation.ispartofIEEE Transactions on Wireless Communications-
dc.rightsIEEE Transactions on Wireless Communications. Copyright © IEEE.-
dc.rights©2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectCellular networks-
dc.subjectEnergy harvesting-
dc.subjectRenewable energy sources-
dc.subjectStochastic processes-
dc.titleRenewables powered cellular networks: Energy field modeling and network coverage-
dc.typeArticle-
dc.identifier.emailHuang, K: huangkb@eee.hku.hk-
dc.identifier.emailLi, VOK: vli@eee.hku.hk-
dc.identifier.authorityHuang, K=rp01875-
dc.identifier.authorityLi, VOK=rp00150-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/TWC.2015.2418262-
dc.identifier.scopuseid_2-s2.0-84939515851-
dc.identifier.hkuros250220-
dc.identifier.hkuros250215-
dc.identifier.volume14-
dc.identifier.issue8-
dc.identifier.spage4234-
dc.identifier.epage4247-
dc.publisher.placeUnited States-

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