In this paper, an optimal model is developed for path loss predictions using the Feed-Forward Neural Network (FFNN) algorithm. Drive test measurements were carried out in Canaanland Ota, Nigeria and Ilorin, Nigeria to obtain path loss data at varying distances from 11 different 1,800 MHz base station transmitters. Single-layered FFNNs were trained with normalized terrain profile data (longitude, latitude, elevation, altitude, clutter height) and normalized distances to produce the corresponding path loss values based on the Levenberg-Marquardt algorithm. The number of neurons in the hidden layer was varied (1-50) to determine the Artificial Neural Network (ANN) model with the best prediction accuracy. The performance of the ANN models was evaluated based on different metrics: Mean Absolute error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), standard deviation, and regression coefficient (R). Results of the machine learning processes show that the FNN architecture adopting a tangent activation function and 48 hidden neurons produced the least prediction error, with MAE, MSE, RMSE, standard deviation, and R values of 4.21 dB, 30.99 dB, 5.56 dB, 5.56 dB, and 0.89, respectively. Regarding generalization ability, the predictions of the optimal ANN model yielded MAE, MSE, RMSE, standard deviation, and R values of 4.74 dB, 39.38 dB, 6.27 dB, 6.27 dB, and 0.86, respectively, when tested with new data not previously included  The huge potentials of Information and Communication Technology can be leveraged for the timely attainment of the Sustainable Development Goals. To this end, global interconnectedness is viewed as an enabling technological platform for digital transformation. Unfortunately, the coverage of mobile connectivity that is expected to drive digital inclusion is not yet global. Path loss prediction models are vital tools for radio coverage estimation, determination of base station location, frequency allocation, antenna selection, and interference feasibility studies during radio network planning. Machine learning techniques may be exploited for path loss predictions in rural and urban propagation environments. In this paper, an optimal model is developed for path loss predictions using Feed-Forward Neural Network algorithm. The performance of the developed ANN model proved to be optimal in terms of prediction accuracy and generalization ability when compared to those of widely used empirical models (Hata, COST 231, ECC-33, and Egli). in the training process. Compared to the Hata, COST 231, ECC-33, and Egli models, the developed ANN model performed better in terms of prediction accuracy and generalization ability.
Popoola et al. REF established an optimal model for the prediction of path loss utilizing feed-forward neural networks.
Optimal model for path loss predictions using feed-forward neural networks
Cogent engineering
Mathematics