Impact of noise reduction on Path Loss Model development and Tuning using Wavelet Transform
Keywords:
path loss, wavelet, noise, model, communicationAbstract
Accurate path-loss estimation is an essential part of wireless network planning. It is achieved through extensive measurement of the received signal strength (RSS) in the target area. The measured data is mostly corrupted by noise, which affects the accuracy of the path loss model it is used for. Therefore, the purpose of this paper is to highlight the impact of noise on the dataset used for the development of path loss model. In this paper, a wavelet transform was used for the de-noising of the RSS data, and the outcome was used for the tuning of the standard log-distance model. The standard model, the tuned standard model and the wavelet+tuned-standard model were compared. As expected, the wavelet+tuned-standard model outperforms the others.
Downloads
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published by Global Research Publication will be distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License (CC-BY). So anyone is allowed to copy, distribute, and transmit the article on condition that the original article and source are correctly cited. Authors retain all copyright interest or it is retained by other copyright holders, as appropriate, and agree that the manuscript remains permanently open access in Global Research Publication's site under the terms of the Creative Commons Attribution 4.0 International License (CC-BY). Global Research Publication shall have the right to use and archive the content to create a record and may reformat or paraphrase to benefit the display of the record. For commercial use, we need to know about it.
