Optimum Positioning of Base Station for Cellular Service Devices Using Discrete Knowledge Model
Keywords:Antenna Positioning, KNN Classifier, Optimization, Radio Propagation Models, Refarming
A good wireless network design depends on technical and financial viability and a number of other criteria that must be met. Following the emergence of new technologies and services, such as 5G transmission and the reuse of frequencies, new work is being carried out to ensure a better design for a particular area. This study examines a discrete radio propagation model which employs the K nearest neighbors classifier. The model takes into account the different characteristics of the environment. This article presents a case study for the optimum positioning of base stations in Federal University of Pará (Belém – Brazil), representing a typical Amazon environment. The mentioned scenario is heterogeneous, presenting edifications and considerable forest area. Measurement campaigns were conducted in three different frequencies for the design features of the model: 521 MHz (Brazilian digital TV system), 2100 MHz (Enhanced Data Rates for GSM Evolution), and 2600 MHz (Long Term Evolution). A study of the fading phenomenon in these frequencies was carried out to generalize the frequencies of application for the propagation loss model. When this model was ready, tests (computing simulations) were conducted in two scenarios to optimize the positioning of the radio base stations being studied.
P. Muñoz, O. Sallent, J. Pérez-Romero, “Self-Dimensioning and Planning of Small Cell Capacity in Multitenant 5G Networks”, IEEE Trans. on Veh. Tech., vol. 67, no. 5, pp. 4552-4564, May. 2018.
W. Ni. et al, “Graph theory and its applications to future network planning: software-defined online small cell management”, IEEE Wireless Commun., vol. 22, no. 1, pp. 52-60, Feb. 2015.
Y. S. Meng, Y. H. Lee, B C. Ng, “Further study of rainfall effect on VHF forested radio-wave propagation with four layered model”. Progress in electromagnetics research-PIER99, pp.149-161, 2009.
C. R. Gomes, D. K. N. Silva, J. Araujo, H. S. Gomes, G. P. S. Cavalcante, “Radio-Wave Propagation Model for UHF Band in Different Climatic Conditions with Dyadic Green’s Function”, J. Microw., Optoelectronics and Electromagn. Appl., vol. 14, pp. 60-72, 2015. doi: 10.1590/2179-10742015v14i1427
A. Taufique, B. Jaber, A. Imran, Z. Dawy, E. Yacoub, "Planning Wireless Cellular Networks of Future: Outlook, Challenges and Opportunities," IEEE Access, vol. 5, pp. 4821-4845, 2017.
I. R. Gomes, C. R. Gomes, H. S. Gomes, G. P. S. Cavalcante, “Empirical radio propagation model for DTV applied to non- homogeneous paths and different climates using machine learning techniques,” PLoS ONE 13(3), 2019. e0194511. doi: 10.1371/journal.pone.0194511.
N. S. Nkordeh et al. “LTE Network Planning using the Hata-Okumura and the COST-231 Hata Pathloss Models,” presented at the World Congr. Engineering, London, 2014.
R. L. Fraiha Lopes, S. G. C. Fraiha, H. S. Gomes, V. D. Lima, G. P. S. Cavalcante, "Application of Hybrid ARIMA and Artificial Neural Network Modelling for Electromagnetic Propagation: An Alternative to the Least Squares Method and ITU Recommendation P.1546-5 for Amazon Urbanized Cities," Int. J. of Antennas and Propag., vol. 2020, Article ID 8494185, 12 pages, 2020. doi:10.1155/2020/8494185.
Z. Yun, and M. F. Iskander, "Ray Tracing for Radio Propagation Modeling: Principles and Applications," IEEE Access, vol. 3, pp. 1089-1100, 2015.
C. Xuesong, et al. “Interference Modeling for Low-Height Air-to-Ground Channels in Live LTE Networks”, IEEE Antennas and Wireless Propag. Letters, vol. 18, no. 10, 2019.
P. Minyoung, et al. “LTE Maritime Coverage Solution and Ocean Propagation Loss Model”, Int. Conf. Performance Evaluation and Modeling in Wired and Wireless Networks (PEMWN), 2017.
J. E. A. Peña, “Correlation Analysis of Propagation Models for the Design of a LTE - A Network”. IEEE Int. Engineering Summit, II Cumbre Internacional de las Ingenierias (IE-Summit), 2016.
A. M. Clarke, J. Friedrich, E. M. Tartaglia, S. Marchesotti, W. Senn, M. H. Herzog, “Human and Machine Learning in Non-Markovian Decision Making,” PLoS ONE 10(4): e0123105. doi: 10.1371/journal.pone.0123105.
S. Makridakis, E. Spiliotis, V. Assimakopoulos, “Statistical and Machine Learning forecasting methods: Concerns and ways forward”. PLoS ONE 13(3), 2018. e0194889. doi:10.1371/journal.pone.0194889.
K. P. Murphy, Machine learning: A probabilistic perspective. Cambridge, MA, USA: MIT Press, 2012.
B. Korte et al. Combinatorial optimization. 3rd ed. Heidelberg, Germany: Springer, 2012.
Brazilian National Agency of Telecommunications (ANATEL)
www.anatel.gov.br, (access Dec. 1 2019).
Arc Gis World Imagery.
www.arcgis.com/home/webmap/viewer.html?useExisting=1&layers=10df2279f9684e4a9f6a7f08febac2a9, (accessed Dec. 6, 2019).