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| Content Provider | Taylor & Francis Online |
|---|---|
| Author | Adetiba, Emmanuel Atayero, Aderemi A. Faruk, Nasir Calafate, Carlos T. Popoola, Segun I. Yuen, Chau |
| Abstract | 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 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. |
| File Format | PDF HTM / HTML |
| Journal | Cogent Engineering |
| Volume Number | 5 |
| Issue Number | 1 |
| Language | English |
| Publisher | Cogent |
| Publisher Date | 2018-03-07 |
| Access Restriction | Open |
| Subject Keyword | Path loss Received signal strength Scale conjugate gradient Radio network planning Artificial Neural Network |
| Content Type | Text |
| Resource Type | Article |
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