Using omnidirectional BTS and different evolutionary approaches to solve the RND Problem

Authors:
Miguel A. Vega-Rodríguez;Juan A. Gómez-Pulido;Enrique Alba;David Vega-Pérez;Silvio Priem-Mendes;Guillermo Molina
Affiliations:
Dep. Techn. of Computers and Communications, Univ. of Extremadura, Caceres, Spain;Dep. Techn. of Computers and Communications, Univ. of Extremadura, Caceres, Spain;Dep. of Computer Science, Univ. of Malaga, Malaga, Spain;Dep. Techn. of Computers and Communications, Univ. of Extremadura, Caceres, Spain;Polytechnic Institute of Leiria, School of Technology and Management, Leiria, Portugal;Dep. of Computer Science, Univ. of Malaga, Malaga, Spain
Venue:
EUROCAST'07 Proceedings of the 11th international conference on Computer aided systems theory
Year:
2007

Citing 5
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Abstract

RND (Radio Network Design) is an important problem in mobile telecommunications (for example in mobile/cellular telephony), being also relevant in the rising area of sensor networks. This problem consists in covering a certain geographical area by using the smallest number of radio antennas achieving the biggest cover rate. To date, several radio antenna models have been used: square coverage antennas, omnidirectional antennas that cover a circular area, etc. In this work we use omnidirectional antennas. On the other hand, RND is an NP-hard problem; therefore its solution by means of evolutionary algorithms is appropriate. In this work we study different evolutionary approaches to tackle this problem. PBIL (Population-Based Incremental Learning) is based on genetic algorithms and competitive learning (typical in neural networks). DE (Differential Evolution) is a very simple population-based stochastic function minimizer used in a wide range of optimization problems, including multi-objective optimization. SA (Simulated Annealing) is a classic trajectory descent optimization technique. Finally, CHC is a particular class of evolutionary algorithm which does not use mutation and relies instead on incest prevention and disruptive crossover. Due to the complexity of such a large analysis including so many techniques, we have used not only sequential algorithms, but also grid computing with BOINC in order to execute thousands of experiments in only several days using around 100 computers.