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.

 

Index Terms—Signal denoising, filtering techniques, vector median filters, multi-layer perceptron, artificial neural network, radial basis function

Cite: Virginia C. Ebhota and Viranjay M. Srivastava, "An Adaptive Vector Median Filtering Approach to Enhance the Prediction Efficiency of Signal Power Loss," Journal of Communications vol. 15, no. 12, pp. 866-875, December 2020. Doi: 10.12720/jcm.15.12.866-875

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.