Clustering based self-optimization of pilot power in dense femtocell deployments using genetic algorithms

Lina Mohjazi; Mahmoud Al-Qutayri; Hassan Barada; Kin Fai Poon

doi:10.1109/ICECS.2013.6815507

Clustering based self-optimization of pilot power in dense femtocell deployments using genetic algorithms

Lina Mohjazi, Mahmoud Al-Qutayri, Hassan Barada, Kin Fai Poon

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Femtocells are small base stations used to enhance cellular coverage in an indoor environment. However, dense femtocell deployments can lead to severe performance degradation. This paper adopts a new strategy to self-optimize the pilot power of femtocells by creating disjoint femtocell clusters which are managed by the chosen cluster heads (CHs). Each CH optimizes the coverage of its connected members by applying a multi-objective heuristic based on genetic algorithm. The simulation results show that the proposed approach can significantly reduce both the computational time and the data overhead compared with the centralized power optimization.

Original language	British English
Title of host publication	2013 IEEE 20th International Conference on Electronics, Circuits, and Systems, ICECS 2013
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	686-690
Number of pages	5
ISBN (Print)	9781479924523
DOIs	https://doi.org/10.1109/ICECS.2013.6815507
State	Published - 2013
Event	2013 IEEE 20th International Conference on Electronics, Circuits, and Systems, ICECS 2013 - Abu Dhabi, United Arab Emirates Duration: 8 Dec 2013 → 11 Dec 2013

Publication series

Name	Proceedings of the IEEE International Conference on Electronics, Circuits, and Systems

Conference

Conference	2013 IEEE 20th International Conference on Electronics, Circuits, and Systems, ICECS 2013
Country/Territory	United Arab Emirates
City	Abu Dhabi
Period	8/12/13 → 11/12/13

Keywords

Clustering
Femtocells
Heuristics
Optimization
Self-Organizing Networks

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10.1109/ICECS.2013.6815507

Cite this

Mohjazi, L., Al-Qutayri, M., Barada, H., & Poon, K. F. (2013). Clustering based self-optimization of pilot power in dense femtocell deployments using genetic algorithms. In 2013 IEEE 20th International Conference on Electronics, Circuits, and Systems, ICECS 2013 (pp. 686-690). Article 6815507 (Proceedings of the IEEE International Conference on Electronics, Circuits, and Systems). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICECS.2013.6815507

Mohjazi, Lina ; Al-Qutayri, Mahmoud ; Barada, Hassan et al. / Clustering based self-optimization of pilot power in dense femtocell deployments using genetic algorithms. 2013 IEEE 20th International Conference on Electronics, Circuits, and Systems, ICECS 2013. Institute of Electrical and Electronics Engineers Inc., 2013. pp. 686-690 (Proceedings of the IEEE International Conference on Electronics, Circuits, and Systems).

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title = "Clustering based self-optimization of pilot power in dense femtocell deployments using genetic algorithms",

abstract = "Femtocells are small base stations used to enhance cellular coverage in an indoor environment. However, dense femtocell deployments can lead to severe performance degradation. This paper adopts a new strategy to self-optimize the pilot power of femtocells by creating disjoint femtocell clusters which are managed by the chosen cluster heads (CHs). Each CH optimizes the coverage of its connected members by applying a multi-objective heuristic based on genetic algorithm. The simulation results show that the proposed approach can significantly reduce both the computational time and the data overhead compared with the centralized power optimization.",

keywords = "Clustering, Femtocells, Heuristics, Optimization, Self-Organizing Networks",

author = "Lina Mohjazi and Mahmoud Al-Qutayri and Hassan Barada and Poon, {Kin Fai}",

year = "2013",

doi = "10.1109/ICECS.2013.6815507",

language = "British English",

isbn = "9781479924523",

series = "Proceedings of the IEEE International Conference on Electronics, Circuits, and Systems",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "2013 IEEE 20th International Conference on Electronics, Circuits, and Systems, ICECS 2013",

address = "United States",

note = "2013 IEEE 20th International Conference on Electronics, Circuits, and Systems, ICECS 2013 ; Conference date: 08-12-2013 Through 11-12-2013",

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Mohjazi, L, Al-Qutayri, M , Barada, H & Poon, KF 2013, Clustering based self-optimization of pilot power in dense femtocell deployments using genetic algorithms. in 2013 IEEE 20th International Conference on Electronics, Circuits, and Systems, ICECS 2013., 6815507, Proceedings of the IEEE International Conference on Electronics, Circuits, and Systems, Institute of Electrical and Electronics Engineers Inc., pp. 686-690, 2013 IEEE 20th International Conference on Electronics, Circuits, and Systems, ICECS 2013, Abu Dhabi, United Arab Emirates, 8/12/13. https://doi.org/10.1109/ICECS.2013.6815507

Clustering based self-optimization of pilot power in dense femtocell deployments using genetic algorithms. / Mohjazi, Lina; Al-Qutayri, Mahmoud ; Barada, Hassan et al.
2013 IEEE 20th International Conference on Electronics, Circuits, and Systems, ICECS 2013. Institute of Electrical and Electronics Engineers Inc., 2013. p. 686-690 6815507 (Proceedings of the IEEE International Conference on Electronics, Circuits, and Systems).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - Femtocells are small base stations used to enhance cellular coverage in an indoor environment. However, dense femtocell deployments can lead to severe performance degradation. This paper adopts a new strategy to self-optimize the pilot power of femtocells by creating disjoint femtocell clusters which are managed by the chosen cluster heads (CHs). Each CH optimizes the coverage of its connected members by applying a multi-objective heuristic based on genetic algorithm. The simulation results show that the proposed approach can significantly reduce both the computational time and the data overhead compared with the centralized power optimization.

AB - Femtocells are small base stations used to enhance cellular coverage in an indoor environment. However, dense femtocell deployments can lead to severe performance degradation. This paper adopts a new strategy to self-optimize the pilot power of femtocells by creating disjoint femtocell clusters which are managed by the chosen cluster heads (CHs). Each CH optimizes the coverage of its connected members by applying a multi-objective heuristic based on genetic algorithm. The simulation results show that the proposed approach can significantly reduce both the computational time and the data overhead compared with the centralized power optimization.

KW - Clustering

KW - Femtocells

KW - Heuristics

KW - Optimization

KW - Self-Organizing Networks

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T3 - Proceedings of the IEEE International Conference on Electronics, Circuits, and Systems

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BT - 2013 IEEE 20th International Conference on Electronics, Circuits, and Systems, ICECS 2013

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Mohjazi L, Al-Qutayri M , Barada H , Poon KF. Clustering based self-optimization of pilot power in dense femtocell deployments using genetic algorithms. In 2013 IEEE 20th International Conference on Electronics, Circuits, and Systems, ICECS 2013. Institute of Electrical and Electronics Engineers Inc. 2013. p. 686-690. 6815507. (Proceedings of the IEEE International Conference on Electronics, Circuits, and Systems). doi: 10.1109/ICECS.2013.6815507

Clustering based self-optimization of pilot power in dense femtocell deployments using genetic algorithms

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