Optimized LTE Cell Planning with Varying Spatial and Temporal User Densities

Handle URI:
http://hdl.handle.net/10754/550519
Title:
Optimized LTE Cell Planning with Varying Spatial and Temporal User Densities
Authors:
Ghazzai, Hakim ( 0000-0002-8636-4264 ) ; Yaacoub, Elias; Alouini, Mohamed-Slim ( 0000-0003-4827-1793 ) ; Dawy, Zaher; Abu Dayya, Adnan
Abstract:
Base station deployment in cellular networks is one of the fundamental problems in network design. This paper proposes a novel method for the cell planning problem for the fourth generation (4G) cellular networks using meta-heuristic algorithms. In this approach, we aim to satisfy both cell coverage and capacity constraints simultaneously by formulating an optimization problem that captures practical planning aspects. The starting point of the planning process is defined through a dimensioning exercise that captures both coverage and capacity constraints. Afterwards, we implement a meta-heuristic algorithm based on swarm intelligence (e.g., particle swarm optimization or the recently-proposed grey wolf optimizer) to find suboptimal base station locations that satisfy both problem constraints in the area of interest which can be divided into several subareas with different spatial user densities. Subsequently, an iterative approach is executed to eliminate eventual redundant base stations. We also perform Monte Carlo simulations to study the performance of the proposed scheme and compute the average number of users in outage. Next, the problems of green planning with regards to temporal traffic variation and planning with location constraints due to tight limits on electromagnetic radiations are addressed, using the proposed method. Finally, in our simulation results, we apply our proposed approach for different scenarios with different subareas and user distributions and show that the desired network quality of service targets are always reached even for large-scale problems.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Optimized LTE Cell Planning with Varying Spatial and Temporal User Densities 2015:1 IEEE Transactions on Vehicular Technology
Publisher:
IEEE
Journal:
IEEE Transactions on Vehicular Technology
Issue Date:
9-Mar-2015
DOI:
10.1109/TVT.2015.2411579
Type:
Article
ISSN:
0018-9545; 1939-9359
Additional Links:
http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7056465
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorGhazzai, Hakimen
dc.contributor.authorYaacoub, Eliasen
dc.contributor.authorAlouini, Mohamed-Slimen
dc.contributor.authorDawy, Zaheren
dc.contributor.authorAbu Dayya, Adnanen
dc.date.accessioned2015-04-23T14:08:33Zen
dc.date.available2015-04-23T14:08:33Zen
dc.date.issued2015-03-09en
dc.identifier.citationOptimized LTE Cell Planning with Varying Spatial and Temporal User Densities 2015:1 IEEE Transactions on Vehicular Technologyen
dc.identifier.issn0018-9545en
dc.identifier.issn1939-9359en
dc.identifier.doi10.1109/TVT.2015.2411579en
dc.identifier.urihttp://hdl.handle.net/10754/550519en
dc.description.abstractBase station deployment in cellular networks is one of the fundamental problems in network design. This paper proposes a novel method for the cell planning problem for the fourth generation (4G) cellular networks using meta-heuristic algorithms. In this approach, we aim to satisfy both cell coverage and capacity constraints simultaneously by formulating an optimization problem that captures practical planning aspects. The starting point of the planning process is defined through a dimensioning exercise that captures both coverage and capacity constraints. Afterwards, we implement a meta-heuristic algorithm based on swarm intelligence (e.g., particle swarm optimization or the recently-proposed grey wolf optimizer) to find suboptimal base station locations that satisfy both problem constraints in the area of interest which can be divided into several subareas with different spatial user densities. Subsequently, an iterative approach is executed to eliminate eventual redundant base stations. We also perform Monte Carlo simulations to study the performance of the proposed scheme and compute the average number of users in outage. Next, the problems of green planning with regards to temporal traffic variation and planning with location constraints due to tight limits on electromagnetic radiations are addressed, using the proposed method. Finally, in our simulation results, we apply our proposed approach for different scenarios with different subareas and user distributions and show that the desired network quality of service targets are always reached even for large-scale problems.en
dc.publisherIEEEen
dc.relation.urlhttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7056465en
dc.rights(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other works.en
dc.subjectCoverage and Cell Capacity Dimensioningen
dc.subjectElectromagnetic Radiation Exposureen
dc.subjectGreen Planningen
dc.subjectLTE Cellular Network Planningen
dc.subjectMeta-Heuristic Algorithmsen
dc.titleOptimized LTE Cell Planning with Varying Spatial and Temporal User Densitiesen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalIEEE Transactions on Vehicular Technologyen
dc.eprint.versionPost-printen
dc.contributor.institutionStrategic Decision Group (SDG), Be irut, Lebanonen
dc.contributor.institutionElectrical and Computer Engineering De partment, American University of Beirut, Beirut, Lebanonen
dc.contributor.institutionQatar Mobility Innovations Center ( QMIC), Qatar Science & Technology Park, Doha, Qataren
kaust.authorGhazzai, Hakimen
kaust.authorAlouini, Mohamed-Slimen
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