Abstract
AbstractWith the support of GIS spatial analysis technology, based on an in-depth study of the wireless propagation environment of a city, combined with the analysis of project requirements, it proposes to use the SPM model to correct the propagation model parameters, using SPM. The wireless propagation model, and research and analysis of the SPM wireless propagation model correction algorithm, further corrected the parameters of a city's SPM wireless propagation model. On this basis, the propagation loss of several classic propagation models in different environments is compared, and the SPM propagation model suitable for the signal frequency band and propagation environment of this study is selected. The correction of the SPM propagation model is based on the designed correction principle and correction process, that is, the weighted least square method is used to fit and analyze the measured level data to obtain an SPM prediction improvement model with local characteristics, and according to the designed verification link. Evaluation of the correction results shows that the accuracy requirements are met. Based on the corrected SPM prediction model, link loss calculations were performed on the 13 test base stations studied in the experiment, and the effective coverage radius of each base station community was obtained. In combination with GIS technology, model parameters and workers of each base station participated in the electronic map loading of the area Go to the network planning software to get the wireless signal coverage prediction map of each base station. Finally, according to the technical requirements of the TD-LTE system network planning and network optimization engineering, the objectiveness and rationality of the site selection and number of base stations in the area were verified, and specific problems regarding poor coverage and overlapping coverage in the area were proposed.
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Computer Science Applications,Signal Processing
Reference23 articles.
1. L. Ma, S. Hu, Q. Min et al., Energy consumption optimization of high sulfur natural gas purification plant based on back propagation neural network and genetic algorithms. Energy Procedia 105, 5166–5171 (2017)
2. D. Djomadji, T. Emmanuel, New propagation model optimization approach based on particles swarm optimization algorithm. Int. J. Comput. Appl. 118(10), 975–8887 (2015)
3. T. Li, S. Ying, P. He, Research on the material mechanics performance prediction based on back propagation neural network. J. Comput. Theor. Nanosci. 13(4), 2519–2523 (2016)
4. J. Wei, X. Yu, Y. Wang, The optimization model based on traffic flow and safety. J. Comput. Theor. Nanosci. 12(12), 6265–6271 (2015)
5. Q. Liu, X. Wan, D. Xie, Optimization of spectral printer modeling based on a modified cellular Yule–Nielsen spectral Neugebauer model. J. Opt. Soc. Am. A 31(6), 1284–1294 (2014)
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献