Artificial Neural Network Modeling for Path Loss Prediction in Urban Environments
This work addresses path loss prediction for wireless communication systems in urban areas, representing an incremental improvement over existing methods.
The paper tackled the problem of accurately predicting path loss in urban environments by proposing an artificial neural network-based regression framework, which achieved higher accuracy and flexibility compared to conventional linear models.
Although various linear log-distance path loss models have been developed, advanced models are requiring to more accurately and flexibly represent the path loss for complex environments such as the urban area. This letter proposes an artificial neural network (ANN) based multi-dimensional regression framework for path loss modeling in urban environments at 3 to 6 GHz frequency band. ANN is used to learn the path loss structure from the measured path loss data which is a function of distance and frequency. The effect of the network architecture parameter (activation function, the number of hidden layers and nodes) on the prediction accuracy are analyzed. We observe that the proposed model is more accurate and flexible compared to the conventional linear model.