2020
DOI: 10.1007/978-3-030-58820-5_71
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Support Vector Machine for Path Loss Predictions in Urban Environment

Abstract: Path Loss (PL) propagation models are important for accurate radio network design and planning. In this paper, we propose a new radio propagation model for PL predictions in urban environment using Support Vector Machine (SVM). Field measurement campaigns are conducted in urban environment to obtain mobile network and path loss information of radio signals transmitted at 900, 1800 and 2100 MHz frequencies. SVM model is trained with field measurement data to predict path loss in urban propagation environment. P… Show more

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Cited by 9 publications
(6 citation statements)
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“…The result indicated the MSE of the proposed model was lower than that of the Hata Model. Support vector machine (SVM) was used to predict path loss prediction in suburban environments 42 . Different algorithms, including backpropagation, generic algorithms, and tabu search, were used to select the prediction model's parameters.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…The result indicated the MSE of the proposed model was lower than that of the Hata Model. Support vector machine (SVM) was used to predict path loss prediction in suburban environments 42 . Different algorithms, including backpropagation, generic algorithms, and tabu search, were used to select the prediction model's parameters.…”
Section: Introductionmentioning
confidence: 99%
“…A radial basis function (RBF) was recently introduced in Ojo et al, 43 and an ANN was also introduced independently of the RBF. An ensemble method was used in Abolade et al 42 to solve gene expression in cancer classification problems. Three base models were combined for a single ensemble model.…”
Section: Introductionmentioning
confidence: 99%
“…In recent times, several approaches like stochastic optimization technique, evolutionary algorithms and support vector machine (SVM) have revealed capable results in terms of accuracy prediction [9][10][11].…”
Section: Introductionmentioning
confidence: 99%
“…Machine learning offers useful statistical tools for complex nonlinear regression problems and thus can be exploited to obtain more accurate predictions at low computational cost [8][9][10]. Most researchers perform work to establish an effective diagnostic method for the earliest possible diagnosis of the tumor, and also to make it easier to start treatment at earlier stages, and to increase the survivability rate [11].…”
Section: Introductionmentioning
confidence: 99%