Path-loss prediction for radio frequency signal of wireless sensor network in field based on artificial neural network

For solving the problem that path-loss of radio frequency signal could not be easily retrieved on the process of wireless sensor network (WSN) applications in field, the relationships between path-loss of WSN radio frequency (RF) signal in field and its impact factors were studied based on artificial neural network (ANN) theory. Two carrier frequencies, 915 MHz and 2 470 MHz, were selected. Path-loss prediction ANN model of WSN RF signal in field was achieved through measuring RF path-loss under the two carrier frequencies with different combinations of impact factors at different winter wheat growth stages. Correlation coefficient of the model was 0.92, by comparing the path-loss measured with predicted values, it was verified that the highest absolute prediction error was 4.186 dB, the highest prediction standard deviation was 2.759 dB and prediction accuracy was 94.2%. The designed BP ANN is suitable for path-loss prediction of the radio frequency signal in field.