Optimal placement of mobile antenna in an urban area using evolutionary multiobjective optimization
The optimized placements of mobile antennas (MA) in an existing high traffic urban area are identified using evolutionary multiobjective optimization algorithms (EMOA). The design parameters such as (x, y) coordinates, transmitting power, height, and tilt are considered. The objectives are coverage...
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sg-ntu-dr.10356-1020372020-03-07T14:00:35Z Optimal placement of mobile antenna in an urban area using evolutionary multiobjective optimization Narayanan, Lakshiminarasimman Subramanian, Baskar Iruthayarajan, M. Willjuice Arokiaswami, Alphones School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering The optimized placements of mobile antennas (MA) in an existing high traffic urban area are identified using evolutionary multiobjective optimization algorithms (EMOA). The design parameters such as (x, y) coordinates, transmitting power, height, and tilt are considered. The objectives are coverage and cost where as the inequality constraints are handover, traffic demand, and overlap. Modified NSGA-II (MNSGA-II) is preferred to improve the non dominated solutions. A detailed study is performed about physical and geometrical structure of propagation environment. Four different case studies are performed. The simulation results reveal that Hata–Okumura path loss model is more suited. MNSGA-II is able to determine optimal MA parameters for multiobjective case studies. 2013-07-11T02:54:00Z 2019-12-06T20:48:36Z 2013-07-11T02:54:00Z 2019-12-06T20:48:36Z 2012 2012 Journal Article Narayanan, L., Subramanian, B., Arokiaswami, A., Iruthayarajan, M. W. (2012). Optimal placement of mobile antenna in an urban area using evolutionary multiobjective optimization. Microwave and Optical Technology Letters, 54(3), 737-743. https://hdl.handle.net/10356/102037 http://hdl.handle.net/10220/11170 10.1002/mop.26627 en Microwave and optical technology letters © 2012 Wiley Periodicals, Inc. |
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DRNTU::Engineering::Electrical and electronic engineering Narayanan, Lakshiminarasimman Subramanian, Baskar Iruthayarajan, M. Willjuice Arokiaswami, Alphones Optimal placement of mobile antenna in an urban area using evolutionary multiobjective optimization |
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The optimized placements of mobile antennas (MA) in an existing high traffic urban area are identified using evolutionary multiobjective optimization algorithms (EMOA). The design parameters such as (x, y) coordinates, transmitting power, height, and tilt are considered. The objectives are coverage and cost where as the inequality constraints are handover, traffic demand, and overlap. Modified NSGA-II (MNSGA-II) is preferred to improve the non dominated solutions. A detailed study is performed about physical and geometrical structure of propagation environment. Four different case studies are performed. The simulation results reveal that Hata–Okumura path loss model is more suited. MNSGA-II is able to determine optimal MA parameters for multiobjective case studies. |
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School of Electrical and Electronic Engineering |
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School of Electrical and Electronic Engineering Narayanan, Lakshiminarasimman Subramanian, Baskar Iruthayarajan, M. Willjuice Arokiaswami, Alphones |
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Article |
author |
Narayanan, Lakshiminarasimman Subramanian, Baskar Iruthayarajan, M. Willjuice Arokiaswami, Alphones |
author_sort |
Narayanan, Lakshiminarasimman |
title |
Optimal placement of mobile antenna in an urban area using evolutionary multiobjective optimization |
title_short |
Optimal placement of mobile antenna in an urban area using evolutionary multiobjective optimization |
title_full |
Optimal placement of mobile antenna in an urban area using evolutionary multiobjective optimization |
title_fullStr |
Optimal placement of mobile antenna in an urban area using evolutionary multiobjective optimization |
title_full_unstemmed |
Optimal placement of mobile antenna in an urban area using evolutionary multiobjective optimization |
title_sort |
optimal placement of mobile antenna in an urban area using evolutionary multiobjective optimization |
publishDate |
2013 |
url |
https://hdl.handle.net/10356/102037 http://hdl.handle.net/10220/11170 |
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