Posicionamento de Estações-Base em uma cidade simulada usando um Algoritmo Genético de Chaves Aleatórias Enviesadas
Resumo
Conforme as tecnologias de comunicação móvel evoluem, o posicionamento inteligente de estações-base vem se tornando cada vez mais importante. O problema de posicionamento de estações-base trata de posicionar estações de forma eficiente, de forma a trazer um balanço entre cobertura e custo de serviço. Neste trabalho é proposta uma implementação utilizando a meta-heurística BRKGA, que visa alcançar esse balanço de forma ponderada. Foram realizados diversos testes para provar a eficácia da solução proposta, onde o BRKGA apresentou valores distantes 1,4%, em média, da cobertura ótima, resultados superiores em termos de tempo de execução e cobertura de área frente aos métodos adotados pela literatura.
Palavras-chave:
algoritmo genético, brkga, algoritmo genético de chaves-aleatórias enviesadas, redes móveis, otimização de localização, posicionamento de estações-base
Referências
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Bean, J. C. (1994). Genetic algorithms and random keys for sequencing and optimization. ORSA Journal on Computing, 6(2):154–160.
Boccardi, F., Heath, R. W., Lozano, A., Marzetta, T. L., and Popovski, P. (2014). Five disruptive technology directions for 5g. IEEE Communications Magazine, 52(2):74– 80.
Cardeiro, J. and Correia, L. M. (2006). Optimisation of base station location in umtsfdd for realistic traffic distributions. In 2006 IEEE 17th International Symposium on Personal, Indoor and Mobile Radio Communications, pages 1–5.
Chen, Q. B., Wang, Y., Liu, Z. J., and Liang, C. C. (2012). A new intelligent approach based on genetic algorithm for finding optimal base stations configuration in the dynamic scenario used in son. In Frontiers of Manufacturing and Design Science II, volume 121 of Applied Mechanics and Materials, pages 4325–4329. Trans Tech Publications Ltd.
Gao, Z., Dai, L., Mi, D., Wang, Z., Imran, M. A., and Shakir, M. Z. (2015). Mmwave massive-mimo-based wireless backhaul for the 5g ultra-dense network. IEEE Wireless Communications, 22(5):13–21.
Ge, X., Tu, S., Mao, G., Wang, C., and Han, T. (2016). 5g ultra-dense cellular networks. IEEE Wireless Communications, 23(1):72–79.
Gonçalves, J. F. and Resende, M. G. C. (2011). Biased random-key genetic algorithms for combinatorial optimization. Journal of Heuristics, 17(5):487–525.
Holland, J. H. (1992). Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. MIT Press, Cambridge, MA, USA.
Jin Kyu Han, Byoung Seong Park, Yong Seok Choi, and Han Kyu Park (2001). Genetic approach with a new representation for base station placement in mobile communications. In IEEE 54th Vehicular Technology Conference. VTC Fall 2001. Proceedings (Cat. No.01CH37211), volume 4, pages 2703–2707 vol.4.
Li, L., Ma, B., Jia, Z., and Si, Y. (2017). Base station locations optimization in lte using artificial immune algorithm. In 2017 10th International Symposium on Computational Intelligence and Design (ISCID), volume 1, pages 165–168.
Mathar, R. and Niessen, T. (2000). Optimum positioning of base stations for cellular radio networks. Wireless Networks, 6(6):421–428.
Skouby, K. E. and Lynggaard, P. (2014). Smart home and smart city solutions enabled by 5g, iot, aai and cot services. In 2014 International Conference on Contemporary Computing and Informatics (IC3I), pages 874–878.
Spears, W. and De Jong, K. (1991). On the virtues of parametrized uniform crossover.
Toskala, A., Holma, H., Pajukoski, K., and Tiirola, E. (2006). Utran long term evolution in 3gpp. In 2006 IEEE 17th International Symposium on Personal, Indoor and Mobile Radio Communications, pages 1–5.
Toso, R. and Resende, M. (2015). A c++application programming interface for biased random-key genetic algorithms. Optimization Methods and Software, 30.
Vieira, R. D., Paiva, R. C. D., Hulkkonen, J., Jarvela, R., Iida, R. F., Saily, M., Tavares, F. M., and Niemela, K. (2010). Gsm evolution importance in re-farming 900 mhz band. In 2010 IEEE 72nd Vehicular Technology Conference - Fall, pages 1–5.
Whitley, D. (2019). Next Generation Genetic Algorithms: A User’s Guide and Tutorial, pages 245–274. Springer International Publishing, Cham.
Zimmermann, J., Höns, R., and Mühlenbein, H. (2003). Encon: An evolutionary alrithm for the antenna placement problem. Computers Industrial Engineering, 44:209– 226.
Bean, J. C. (1994). Genetic algorithms and random keys for sequencing and optimization. ORSA Journal on Computing, 6(2):154–160.
Boccardi, F., Heath, R. W., Lozano, A., Marzetta, T. L., and Popovski, P. (2014). Five disruptive technology directions for 5g. IEEE Communications Magazine, 52(2):74– 80.
Cardeiro, J. and Correia, L. M. (2006). Optimisation of base station location in umtsfdd for realistic traffic distributions. In 2006 IEEE 17th International Symposium on Personal, Indoor and Mobile Radio Communications, pages 1–5.
Chen, Q. B., Wang, Y., Liu, Z. J., and Liang, C. C. (2012). A new intelligent approach based on genetic algorithm for finding optimal base stations configuration in the dynamic scenario used in son. In Frontiers of Manufacturing and Design Science II, volume 121 of Applied Mechanics and Materials, pages 4325–4329. Trans Tech Publications Ltd.
Gao, Z., Dai, L., Mi, D., Wang, Z., Imran, M. A., and Shakir, M. Z. (2015). Mmwave massive-mimo-based wireless backhaul for the 5g ultra-dense network. IEEE Wireless Communications, 22(5):13–21.
Ge, X., Tu, S., Mao, G., Wang, C., and Han, T. (2016). 5g ultra-dense cellular networks. IEEE Wireless Communications, 23(1):72–79.
Gonçalves, J. F. and Resende, M. G. C. (2011). Biased random-key genetic algorithms for combinatorial optimization. Journal of Heuristics, 17(5):487–525.
Holland, J. H. (1992). Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. MIT Press, Cambridge, MA, USA.
Jin Kyu Han, Byoung Seong Park, Yong Seok Choi, and Han Kyu Park (2001). Genetic approach with a new representation for base station placement in mobile communications. In IEEE 54th Vehicular Technology Conference. VTC Fall 2001. Proceedings (Cat. No.01CH37211), volume 4, pages 2703–2707 vol.4.
Li, L., Ma, B., Jia, Z., and Si, Y. (2017). Base station locations optimization in lte using artificial immune algorithm. In 2017 10th International Symposium on Computational Intelligence and Design (ISCID), volume 1, pages 165–168.
Mathar, R. and Niessen, T. (2000). Optimum positioning of base stations for cellular radio networks. Wireless Networks, 6(6):421–428.
Skouby, K. E. and Lynggaard, P. (2014). Smart home and smart city solutions enabled by 5g, iot, aai and cot services. In 2014 International Conference on Contemporary Computing and Informatics (IC3I), pages 874–878.
Spears, W. and De Jong, K. (1991). On the virtues of parametrized uniform crossover.
Toskala, A., Holma, H., Pajukoski, K., and Tiirola, E. (2006). Utran long term evolution in 3gpp. In 2006 IEEE 17th International Symposium on Personal, Indoor and Mobile Radio Communications, pages 1–5.
Toso, R. and Resende, M. (2015). A c++application programming interface for biased random-key genetic algorithms. Optimization Methods and Software, 30.
Vieira, R. D., Paiva, R. C. D., Hulkkonen, J., Jarvela, R., Iida, R. F., Saily, M., Tavares, F. M., and Niemela, K. (2010). Gsm evolution importance in re-farming 900 mhz band. In 2010 IEEE 72nd Vehicular Technology Conference - Fall, pages 1–5.
Whitley, D. (2019). Next Generation Genetic Algorithms: A User’s Guide and Tutorial, pages 245–274. Springer International Publishing, Cham.
Zimmermann, J., Höns, R., and Mühlenbein, H. (2003). Encon: An evolutionary alrithm for the antenna placement problem. Computers Industrial Engineering, 44:209– 226.
Publicado
15/10/2019
Como Citar
AMORIM, Lucas; VASSALO, Daniel; PEREIRA, Gabriel; PINHEIRO, Rian.
Posicionamento de Estações-Base em uma cidade simulada usando um Algoritmo Genético de Chaves Aleatórias Enviesadas. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 16. , 2019, Salvador.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 984-995.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2019.9351.
