Analysis of the state of the art of Soft Computing Techniques applied to network planning problems in 5G.

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Vasconez Núñez Vanessa Alexandra

Abstract

The current article developed a review of the state of the art about the application of Soft Computing techniques in solving planning problems of 5G networks. To achieve this, the different existing Soft Computing techniques were classified (neural networks, fuzzy logic, evolutionary algorithms) and models proposed and methods of solution were considered from works and research developed about the subject according to their authors. Additionally, the most relevant investigations described techniques which are specified to solve problems of architectures and crucial functionalities in the development of this technology, highlighting the following: finding an optimal position for a Base Station (BS) in an area of particular interest, operating in the desired multiple frequency bands while maintaining high gain, limiting power consumption in 5G network infrastructures, and trying to increase quality of service by decreasing the probability of call blocking. To conclude, the most applied Soft Computing techniques in solving planning problems of 5G networks are fuzzy logic, in order to limit the energy consumption in 5G network infrastructures, in addition to artificial neural networks and genetic algorithms for admission of calls in 5G networks, increasing quality of service by reducing interference.

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Vanessa Alexandra, V. N. (2020). Analysis of the state of the art of Soft Computing Techniques applied to network planning problems in 5G. ConcienciaDigital, 3(2.2), 60-79. https://doi.org/10.33262/concienciadigital.v3i2.2.1246
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References

Abdelbari, A., & Haci, H. (noviembre de 2019). Fuzzy Logic-Based User Scheduling Scheme for 5G Wireless Networks and Beyond. Springer Link.
Agredo, G., Jojoa, P., & Almenar, V. (julio-diciembre de 2015). Sistemas MIMO con un elevado número de antenas: clave para la 5G inalámbrica. Entramado, 11(2), 250-261.
Albarracin, L., & Puerto, G. (2017). Fuzzy Systems: An Approach to 5G Networks under the Software Defined. Revista Científica, 31(1), 96-110.
Al-Maitah, M., Semenova, O., Semenov, A., Kulakov, P., & Kucheruk, V. (octubre de 2018). A Hybrid Approach to Call Admission Control in 5G Networks. Hindawi: Advances in Fuzzy Systems, 1-7.
Angulo, K., Gil, D., & Salcedo, O. (mayo de 2017). Optimización utilizando lógica difusa de dispositivo de análisis de componentes químicos de ingredientes naturales basados en el internet de las cosas IoT. Revista Científica, 30(3), 207-223.
Atayero, A., & Luka, M. (mayo de 2012). Applications of Soft Computing in Mobile and Wireless Communications. International Journal of Computer Applications, 45(22), 48-52.
Cardoso, A., & Vieira, F. (2019). Adaptive fuzzy flow rate control considering multifractal traffic modeling and 5G communications. Journals, 14(11).
Castañeda, E., Garmendia, L., & Santos, M. (2011). Algoritmos Genéticos Difusos: Una Aproximación Práctica para la Creatividad Computacional. Segundo Congreso Internacional de Matemáticas en la Ingeniería y la Arquitectura, 279-290.
Chabbouh, O., Ben, S., & Choukair, Z. A. (2018). A Two-stage RRH Clustering Mechanism in 5G Heterogeneous C-RAN. 5th International Workshop on ADVANCEs in ICT Infrastructures.
Crespo, J., Peña, E., Pascual, V., & Fustiel, J. (2016). Elección entre una metodología ágil y tradicional basado en técnicas de soft computing. Revista Cubana de Ciencias Informáticas, 10.
Demestichas, K., Adamopoulou, E., & Choras, M. (abril de 2017). 5G Communications: Energy Efficiency. Hindawi, 3.
Fundacio Universitat Empresa. (2019). Estudio sobre la planificación, el impacto y la cobertura de redes 4G y 5G de comunicaciones móviles. Recuperado el 24 de septiembre de 2019, de Ministerio de Ciencias, Innovación y Universidades: https://fueib.org/es/investigadors/65/otri/catalogo/5/741/servicio/estudio-sobre-la-planificacion-el-impacto-y-la-cobertura-de-redes-4g-y-5g-de-comunicaciones-moviles
Gjokaj, V., Doroshewitz, J., Nanzer, J., & Chahal, P. (2017). A Design Study of 5G Antennas Optimized Using Genetic Algorithms. 2017 IEEE 67th Electronic Components and Technology Conference.
González, C. (2011). Lógica Difusa: Una introducción práctica. La Mancha: Universidad de Castilla.
González, J., & Salamanca, Ó. (enero-junio de 2016). El camino hacia la tecnología 5G. Revista Electrónica de Estudios Telemáticos, 15(1), 27-47.
Hossain, E., Rasti, M., Tabassum, H., & Abdelnasser, A. (2014). Evolution Towards 5G Multi-tier Cellular Wireless Networks: An Interference Management Perspective. arXiv, 2, 1-10.
Kaloxylos, A., Barmpounakis, S., & Alonistioti, N. (2014). An efficient RAT selection mechanism for 5G cellular networks. (Mobile and Wireless Communications Enablers for the 2020 Information Society.
Khan, A., Abolhasan, M., Ni, W., Lipman, J., & Jamalipour, A. (2019). A Hybrid-Fuzzy Logic Guided Genetic Algorithm (H-FLGA) Approach for Resource Optimization in 5G VANETs. IEEEexplore, 68(7), 6964 - 6974.
Kumar, P., & Mahajan, A. (2009). Soft Computing Techniques for the Control of an Active Power Filter. IEEE Transactions on Power Delivery, 24(1), 452-461.
Kumar, S., Kumar, A., Das, B., & Burnwal, A. (2013). On Soft Computing Techniques in varios areas. Computer Science & Information Technology (CS & IT), 59–68.
Liu, Q., Chuai, G., & Gao, W. (2018). uzzy Logic-based Virtual Cell Design in Ultra-Dense Networks. Wireless Com Network 2018, 87.
Liu, Q., Foong, C., Zhang, S., & Li, L. (septiembre de 2019). A fuzzy-clustering based approach for MADM handover in 5G ultra-dense networks. Springer.
López, M., Guerra, M., & Izaquirre, S. (junio de 2017). Profundización en la introducción de las redes móviles 5G. Revista Técnica de la Empresa de Telecomunicaciones de Cuba S.A., 14, 36-45.
Lounis, A., Alilat, F., & Nazin, A. (2018). Neural Network Model of QoE for Estimation Video Streaming over 5G network. 6th International Workshop on ADVANCEs in ICT Infrastructures and Services, 21–27.
Lynch, D., Saber, T., & Kucera, S. (2019). Evolutionary learning of link allocation algorithms for 5G heterogeneous wireless communications networks. Proceedings of the Genetic and Evolutionary Computation Conference, 1258–1265.
Maldonado, S. (2013). Técnicas de soft-computing para el desarrollo de redes de acceso móvil con control de polución electromagnética. Madrid: Universidad de Alcalá.
Martín, A. (2015). Integración de técnicas de soft-computing n la planificación basada en conocimiento de tareas de desarrollo software. La Mancha, España: Universidad de Castilla.
Ministerio de Tecnologías de la Información y las Comunicaciones de Colombia. (2019). Plan 5g: El futuro digital es de tods. Colombia.
Murugadass, A. (2019). A Fuzzy Logic Based Pico Serving Node Placement for 5G Ultra Dense Networks. International Journal of Advanced Science and Technology, 28(19), 108-115.
Panda, M., & Patra, M. (2013). Soft: Computing:Concepts and Techniques (Primera ed.). New Delhi, Boston, USA: University Science Press.
Peñaranda, C. (2015). Aplicación de Técnicas Soft Computing y Heurísticas para la identificación y clasificación de la información empleada por un recomendador de recetas. Valencia: Universitat Politecnica de Valencia.
Pierucci, L. (2015). The Quality of experience perspective toward 5G technology. IEE Wireless Comunnications, 22(4), 10-16.
Popovski, P., Trillingsgaard, Kasper, Simeone, O., & Durisi, G. (2018). 5G Wireless Network Slicing for eMBB, URLLC, and mMTC: A Communication-Theoretic View. Open Access Journal, 6.
Premnath, K. N., Srinivasan, R., & Elijah, B. (septiembre de 2014). Magnetic Field Model (MFM) in Soft Computing and parallelization techniques for Self Organizing Networks (SON) in Telecommunications. International Journal of Energy Optimization and Engineering, 3(3), 57-71.
Ramírez, J., Sarmiento, H., & López, J. (2018). Diagnóstico de fallas en procesos industriales mediante inteligencia artificial. Revista Espacios, 39(24), 12.
Rashad, C. (2019). Fuzzy-Neural based Cost Effective Handover Prediction Technique for 5G-IoT networks. International Journal of Innovative Technology and Exploring Engineering, 9, 191-197.
Rodríguez, A., & Pérez, A. (2017). Métodos científicos de indagación y de construcción del conocimiento. Revista Escuela de Administración de Negocios(82), 1-26.
Sachan, R., Jong Choi, T., & Wook, C. (abril de 2016). A Genetic Algorithm with Location Intelligence Method for Energy Optimization in 5G Wireless Networks. Hindawi, 1-9.
Sanou, B. (2018). Sentando las bases para la 5G: Oportunidades y desafíos. Desarrollo de las Telecomunicaciones de la UIT.
Santos, M., & Miranda, E. (junio de 2012). Aplicación de la lógica difusa en el ámbito de las energías renovables. Revista Elementos, 2(1), 102-114.
Subramani, M., & Kumaravelu, V. (2018). A Three-Stage Fuzzy-Logic-Based Handover Necessity Estimation and Target Network Selection Scheme for Next Generation Heterogeneous Networks. Journal of Circuits.
Sun, J., shi, W., Yang, Z., & Yang, J. (2019). Behavioral Modeling and Linearization of Wideband RF Power Amplifiers Using BiLSTM Networks for 5G Wireless Systems. IEEE Transactions on Vehicular Technology, 1(1).
Sun, S., Gong, L., Rong, B., & Lu, K. (2015). An Intelligent SDN Framework for 5G Heterogeneous Networks. IEEE Communications Magazine, 53(11), 142-147.
UI Najam, H., Ejaz, W., Ejaz, N., Kim, H., Anpalagan, A., & Jo, M. (2016). Network Selection and Channel Allocation forSpectrum Sharing in 5G Heterogeneous Networks. IEEE Access, 4, 980-992.
Veslin, E. (2013). Aplicación de algoritmos genéticos en problemas de Ingeniería . Revista de Investigación, Innovación e Ingeniería, 10-29.
Xu, S., Li, R., & Yang, Q. (2018). Improved Genetic Algorithm Based Intelligent Resource Allocation in 5G Ultra Dense Networks. 2018 IEEE Wireless Communications and Networking Conference (WCNC).
Xu, X., Zhang, H., Dai, X., Hou, Y., & Tao, X. Z. (2014). SON Based Next Generation Mobile Network With Service Slicing and Trials. China Communications, 11(2), 65-77.
You, X., Zhang, C., Tan, X., Jin, S., & Wu, H. (2019). AI for 5G: research directions and paradigms. Sci China Inf Sci, 62(2).
Zapata, P., Luna, F., Valenzuela, J., Mora, A., & Padilla, P. (2018). Metaheurísticas híbridas para el problema del apagado de celdas en redes 5G. XIII Congreso Espanol en Metaheurísticas y Algoritmos Evolutivos y Bioinspirados, 18, 665-670.
Zhang, H., Liu, N., Chu, X., Long, K., Aghvami, A., & Leung, V. (2017). Red segmentada de redes 5G y redes móviles futuras: movilidad, gestión de recursos y desafíos. IEEE Communications Magazine, 55(8), 138-145.
Zhao, X., Yang, H., Guo, H., Peng, T., & Zhang, J. (2019). Accurate Fault Location based on Deep Neural Evolution Network in Optical Networks for 5G and Beyond. ptical Fiber Communication Conference (OFC) 2019.