Optimized smart grid energy procurement for LTE networks using evolutionary algorithms

Handle URI:
http://hdl.handle.net/10754/563826
Title:
Optimized smart grid energy procurement for LTE networks using evolutionary algorithms
Authors:
Ghazzai, Hakim ( 0000-0002-8636-4264 ) ; Yaacoub, Elias E.; Alouini, Mohamed-Slim ( 0000-0003-4827-1793 ) ; Abu-Dayya, Adnan A.
Abstract:
Energy efficiency aspects in cellular networks can contribute significantly to reducing worldwide greenhouse gas emissions. The base station (BS) sleeping strategy has become a well-known technique to achieve energy savings by switching off redundant BSs mainly for lightly loaded networks. Moreover, introducing renewable energy as an alternative power source has become a real challenge among network operators. In this paper, we formulate an optimization problem that aims to maximize the profit of Long-Term Evolution (LTE) cellular operators and to simultaneously minimize the CO2 emissions in green wireless cellular networks without affecting the desired quality of service (QoS). The BS sleeping strategy lends itself to an interesting implementation using several heuristic approaches, such as the genetic (GA) and particle swarm optimization (PSO) algorithms. In this paper, we propose GA-based and PSO-based methods that reduce the energy consumption of BSs by not only shutting down underutilized BSs but by optimizing the amounts of energy procured from different retailers (renewable energy and electricity retailers), as well. A comparison with another previously proposed algorithm is also carried out to evaluate the performance and the computational complexity of the employed methods.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Electrical Engineering Program; Communication Theory Lab
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
IEEE Transactions on Vehicular Technology
Issue Date:
Nov-2014
DOI:
10.1109/TVT.2014.2312380
Type:
Article
ISSN:
00189545
Sponsors:
This work was supported in part by the Qatar National Research Fund (a member of Qatar Foundation) under NPRP Grant 6-001-2-001. The review of this paper was coordinated by Dr. Y. Ji.
Appears in Collections:
Articles; Electrical Engineering Program; Communication Theory Lab; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorGhazzai, Hakimen
dc.contributor.authorYaacoub, Elias E.en
dc.contributor.authorAlouini, Mohamed-Slimen
dc.contributor.authorAbu-Dayya, Adnan A.en
dc.date.accessioned2015-08-03T12:15:48Zen
dc.date.available2015-08-03T12:15:48Zen
dc.date.issued2014-11en
dc.identifier.issn00189545en
dc.identifier.doi10.1109/TVT.2014.2312380en
dc.identifier.urihttp://hdl.handle.net/10754/563826en
dc.description.abstractEnergy efficiency aspects in cellular networks can contribute significantly to reducing worldwide greenhouse gas emissions. The base station (BS) sleeping strategy has become a well-known technique to achieve energy savings by switching off redundant BSs mainly for lightly loaded networks. Moreover, introducing renewable energy as an alternative power source has become a real challenge among network operators. In this paper, we formulate an optimization problem that aims to maximize the profit of Long-Term Evolution (LTE) cellular operators and to simultaneously minimize the CO2 emissions in green wireless cellular networks without affecting the desired quality of service (QoS). The BS sleeping strategy lends itself to an interesting implementation using several heuristic approaches, such as the genetic (GA) and particle swarm optimization (PSO) algorithms. In this paper, we propose GA-based and PSO-based methods that reduce the energy consumption of BSs by not only shutting down underutilized BSs but by optimizing the amounts of energy procured from different retailers (renewable energy and electricity retailers), as well. A comparison with another previously proposed algorithm is also carried out to evaluate the performance and the computational complexity of the employed methods.en
dc.description.sponsorshipThis work was supported in part by the Qatar National Research Fund (a member of Qatar Foundation) under NPRP Grant 6-001-2-001. The review of this paper was coordinated by Dr. Y. Ji.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.subjectBase station (BS) sleeping strategyen
dc.subjectenergy efficiencyen
dc.subjectevolutionary algorithmsen
dc.subjectgreen networken
dc.subjectsmart griden
dc.titleOptimized smart grid energy procurement for LTE networks using evolutionary algorithmsen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentElectrical Engineering Programen
dc.contributor.departmentCommunication Theory Laben
dc.identifier.journalIEEE Transactions on Vehicular Technologyen
dc.contributor.institutionQatar Mobility Innovations CenterDoha, Qataren
kaust.authorGhazzai, Hakimen
kaust.authorAlouini, Mohamed-Slimen
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