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Impact of noise reduction on Path Loss Model development and Tuning using Wavelet Transform

Year: 2025

Type: article

Abstract: 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.

Source: Journal of Science Computing and Applied Engineering Research

Authors: Olukanni, Seyi, Isabona, Joseph, Odesanya, Ituabhor

Language: English

Topic: Millimeter-Wave Propagation and Modeling

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W7219950073

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