A multi-objective approach to indoor wireless heterogeneous networks planning based on biogeography-based optimization

In this paper, we present a multi-objective optimization approach for indoor wireless network planning subject to constraints for exposure minimization, coverage maximization and power consumption minimization. We consider heterogeneous networks consisting of WiFi access points (APs) and long term e...

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Bibliographic Details
Published in:Computer networks (Amsterdam, Netherlands : 1999) Vol. 91; pp. 564 - 576
Main Authors: Goudos, Sotirios K., Plets, David, Liu, Ning, Martens, Luc, Joseph, Wout
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 14.11.2015
Elsevier Sequoia S.A
Subjects:
Algorithms
Biogeography
Biogeography-based optimization
Biological evolution
Cellular communication
Classification
Design optimization
Energy consumption
Evolution
Evolutionary algorithms
Exposure minimization
Genetic algorithms
Genetics
Heterogeneous networks
Indoor
Indoor wireless network planning
Maximization
Minimization
Multi-objective optimization
Multiple objective analysis
Networks
Objectives
Optimization
Optimization algorithms
Pareto optimization
Planning
Power consumption
Sorting algorithms
Studies
Wireless communication systems
Wireless networks
Pareto optimization
Indoor wireless network planning
Exposure minimization
Heterogeneous networks
Multi-objective optimization
Biogeography-based optimization
ISSN:1389-1286, 1872-7069
Online Access:Get full text
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Summary:In this paper, we present a multi-objective optimization approach for indoor wireless network planning subject to constraints for exposure minimization, coverage maximization and power consumption minimization. We consider heterogeneous networks consisting of WiFi access points (APs) and long term evolution (LTE) femtocells. We propose a design framework based on multi-objective biogeography-based optimization (MOBBO). We apply the MOBBO algorithm to network planning design cases in a real office environment. To validate this approach we compare results with other multi-objective algorithms like the nondominated sorting genetic algorithm-II (NSGA-II) and the generalized differential evolution (GDE3) algorithm. The results of the proposed method indicate the advantages and applicability of the multi-objective approach.
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ISSN:1389-1286
1872-7069
DOI:10.1016/j.comnet.2015.08.037