This paper analyzes the use of artificial neural networks (ANNs) for predicting the received power/path loss in both outdoor and indoor links. The approach followed has been a combined use of ANNs and ray-tracing, the latter allowing the identification and parameterization of the so-called dominant path. A complete description of the process for creating and training an ANN-based model is presented with special emphasis on the training process. More specifically, we will be discussing various techniques to arrive at valid predictions focusing on an optimum selection of the training set. A quantitative analysis based on results from two narrowband measurement campaigns, one outdoors and the other indoors, is also presented.
Fernández Anitzine et al. REF provided an analysis based on ANNs for the prediction of path loss in both indoor and outdoor links.
Influence of Training Set Selection in Artificial Neural Network-Based Propagation Path Loss Predictions
International Journal of Antennas and Propagation
Engineering