Prediction of HF Propagation Using an Artificial Neural Network for IoT Applications

Ionosphere status plays an important role in satellite communication and navigation systems. In this study, we developed an ANN model to predict the ionosphere status regarding the signal-to-noise ratio at non-line-of-sight, near-vertical incidence (NLOS-NVIS) at frequencies within the HF band. The channel sounding was performed by using two software-defined radios placed at a distance of 29 km apart. The databases regarding signal-to-noise ratio (SNR) data were collected for three ham radio bands: 30 m (10.140203 MHz), 40 m (7.040101 MHz) and 80 m (3.570101 MHz). Subsequently, each database was split into a 70% training set and a 30% testing set. In this configuration, the input vectors were represented by the exact time of day (hour and minute) at which the SNR value was predicted, which functioned as an output variable. Also, three error figures were used as indicators for predicting capability and comparing our ANN with other models.