GRG-MAPE and PCC-MAPE Based on Uncertainty-Mathematical Theory for Path-Loss Model Selection

In this contribution, Grey Relational Grade (GRG) and Pearson Correlation Coefficient (PCC) which are originally used for automation area are proposed in path-loss model selection of channel modeling. The commonly used method-Root Mean Square Error (RMSE) is employed as a comparison. Measurement data derived from inland river regions and open sea environment as well as many kinds of propagation path loss models (such as: Okumura-Hata model, Free-space model, REL model and ITU-R model) are for evaluation the performance of model selection algorithms. The results prove that optimal models which are chosen by GRG-MAPE and PCC-MAPE based on Uncertainty-Mathematical Theory have the better matching degree with measurement data. Consequently, both GRG-MAPE and PCC-MAPE are more accurate than RMSE.