Authors:
Koorosh Navi
;
Manoochehr Kelarestaghi
and
Farshad Eshghi
Affiliation:
Kharazmi University, Iran, Islamic Republic of
Keyword(s):
BTS Localization, EVEBO, Election Inspired, Evolutionary Algorithm, Meta-Heuristics, NP-Hard.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Genetic Algorithms
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Soft Computing
Abstract:
In this paper, we use EVEBO, an election-inspired optimization algorithm, to solve the BTS (i.e. transceiver) localization problem. The proposed method tries to solve the classic and very important problem of achieving maximum coverage with minimum number of BTSs in a specified geographical area. It also tries to reduce the over-coverage rate, one of the undesirable phenomena in cellular networks. The EVEBO’s merit in solving the problem is measured by a common fitness function, and speed of convergence. Simulation results show that EVEBO solves the problem in much less number of evaluations compared to the best results reported in the literature for square-coverage transceivers. We also show that it can be used in a scenario involving more challenging non-square-coverage (almost circular) transceiver type with satisfactory results.