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. Performance of SVM model is evaluated using Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE) and Standard Error Deviation (SED). Results show that SVM achieve MAE, MSE, RMSE and SED of 7.953 dB, 99.966 dB, 9.998 dB and 9.940 dB respectively. SVM model outperforms existing empirical models (Okumura-Hata, COST 231, ECC-33 and Egli) with relatively low prediction error.
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Acknowledgement
This work was carried out under the IoT-Enabled Smart and Connected Communities (SmartCU) research cluster of the Department of Electrical and Information Engineering, Covenant University, Ota, Nigeria. The research was fully sponsored by Covenant University Centre for Research, Innovation and Development (CUCRID), Covenant University, Ota, Nigeria.
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Abolade, R.O., Famakinde, S.O., Popoola, S.I., Oseni, O.F., Atayero, A.A., Misra, S. (2020). Support Vector Machine for Path Loss Predictions in Urban Environment. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12255. Springer, Cham. https://doi.org/10.1007/978-3-030-58820-5_71
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