Clustering Based Self-Optimization of Pilot Power in Dense Femtocell Deployments Using Genetic Algorithms

Mohjazi, L. , Al-Qutayri, M., Barada, H. and 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), Abu Dhabi, United Arab Emirates, 08-11 Dec 2013, pp. 686-690. ISBN 9781479924523 (doi: 10.1109/ICECS.2013.6815507)

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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.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Mohjazi, Dr Lina
Authors: Mohjazi, L., Al-Qutayri, M., Barada, H., and Poon, K. F.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
ISBN:9781479924523
Published Online:15 May 2014

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