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

Authors  
Keywords  Cellular networks Energy harvesting Renewable energy sources Stochastic processes 
Issue Date  2014 
Publisher  IEEE. 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. 42344247 How to Cite? 
Abstract  Powering 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 (cγπψ+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. © 20022012 IEEE. 
Persistent Identifier  http://hdl.handle.net/10722/214841 
ISSN  2015 Impact Factor: 2.925 2015 SCImago Journal Rankings: 2.340 
DC Field  Value  Language 

dc.contributor.author  Huang, K   
dc.contributor.author  Kountouris, M   
dc.contributor.author  Li, VOK   
dc.date.accessioned  20150821T11:58:15Z   
dc.date.available  20150821T11:58:15Z   
dc.date.issued  2014   
dc.identifier.citation  IEEE Transactions on Wireless Communications, 2014, v. 14 n. 8, p. 42344247   
dc.identifier.issn  15361276   
dc.identifier.uri  http://hdl.handle.net/10722/214841   
dc.description.abstract  Powering 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 (cγπψ+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. © 20022012 IEEE.   
dc.language  eng   
dc.publisher  IEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7693   
dc.relation.ispartof  IEEE Transactions on Wireless Communications   
dc.rights  IEEE 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.rights  This work is licensed under a Creative Commons AttributionNonCommercialNoDerivatives 4.0 International License.   
dc.subject  Cellular networks   
dc.subject  Energy harvesting   
dc.subject  Renewable energy sources   
dc.subject  Stochastic processes   
dc.title  Renewables powered cellular networks: Energy field modeling and network coverage   
dc.type  Article   
dc.identifier.email  Huang, K: huangkb@eee.hku.hk   
dc.identifier.email  Li, VOK: vli@eee.hku.hk   
dc.identifier.authority  Huang, K=rp01875   
dc.identifier.authority  Li, VOK=rp00150   
dc.description.nature  published_or_final_version   
dc.identifier.doi  10.1109/TWC.2015.2418262   
dc.identifier.scopus  eid_2s2.084939515851   
dc.identifier.hkuros  250220   
dc.identifier.hkuros  250215   
dc.identifier.volume  14   
dc.identifier.issue  8   
dc.identifier.spage  4234   
dc.identifier.epage  4247   
dc.publisher.place  United States   