This study proposes Artificial Neural Network (ANN) based field strength prediction models for the rural areas of Abuja, the federal capital territory of Nigeria. The ANN-based models were created on bases of the Generalized Regression Neural network (GRNN) and the Multi-Layer Perceptron Neural Network (MLP-NN). These networks were created, trained and tested for field strength prediction using received power data recorded at 900MHz from multiple Base Transceiver Stations (BTSs) distributed across the rural areas. Results indicate that the GRNN and MLP-NN based models with Root Mean Squared Error (RMSE) values of 4.78dBm and 5.56dBm respectively, offer significant improvement over the empirical Hata-Okumura counterpart, which overestimates the signal strength by an RMSE value of 20.17dBm.
Field Strength; Generalized Regression Neural Network; Multi-Layer Perceptron Neural Network; Hata-Okumura
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