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General Information
ISSN:
1796-2021 (Online); 2374-4367 (Print)
Abbreviated Title:
J. Commun.
Frequency:
Monthly
DOI:
10.12720/jcm
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E-mail questions
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Acceptance Rate:
27%
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Average Days to Accept:
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3.4
2023
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Editor-in-Chief
Prof. Maode Ma
College of Engineering, Qatar University, Doha, Qatar
I'm very happy and honored to take on the position of editor-in-chief of JCM, which is a high-quality journal with potential and I'll try my every effort to bring JCM to a next level...
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Home
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2020
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Volume 15, No. 12, December 2020
>
An Adaptive Vector Median Filtering Approach to Enhance the Prediction Efficiency of Signal Power Loss
Virginia C. Ebhota and Viranjay M. Srivastava
Department of Electronic Engineering, Howard College, University of KwaZulu-Natal, Durban-4041, South Africa
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.
2-JCM170598
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