An Adaptive Vector Median Filtering Approach to Enhance the Prediction Efficiency of Signal Power Loss

Abstract

This research work designed and implemented an adaptive Artificial Neural Network (ANN) model using Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) models built on a Vector Median Filter (VMF) for pre-processing of the dataset. Normalized dataset is denoised using VMF and trained with both MLP- and RBF-ANN models. The proposed model has been developed from measurement data collected from two transmitter locations of non-line-of-sight and line-of-sight operating at the 1900MHz frequency band from LTE cellular network over distances of 1800m 1400m respectively. For non-line-of-sight site-1, VMF-MLP gives a correlation coefficient of 0.9600 compared to 0.9490 for VMF-RBF with a Bayesian regularization training algorithm. The VMF-MLP has 2.1380, 1.5000, and 1.4510 for root mean squared error, mean absolute error, and standard deviation compared to 2.3550, 1.5370, and 1.5610 for VMF-RBF network, respectively. The same trend was seen for line-of-sight in site-2 where correlation coefficient for VMF-MLP is 0.9900 and for VMF-RBF is 0.9840. The VMF-MLP has root mean squared error, mean absolute error, and standard deviation as 2.0670, 1.4900, and 1.3180, respectively, compared to VMF-RBF as 2.3470, 1.9010, and 1.3760, respectively. The predictions of these measurement data have been analyzed in this research work.