Noise Power Density Estimation Based on Deep Learning Using Spectrograms Extracted from Wireless Signals

Myke D. M. Valadão, André L. A. Costa

DOI: 10.14209/sbrt.2024.1571036528

Evento: XLII Simpósio Brasileiro de Telecomunicações e Processamento de Sinais (SBrT2024)

Keywords: Noise Power Density Spectrogram Deep Learning

Abstract

In communication systems, noise is almost invariably present, originating from a multitude of sources and variables. These sources include thermal effects, interference, quantization, and channel imperfections, contributing to the random nature of noise. Determining noise levels is crucial and remains a pervasive challenge in communication systems, especially in recent times when better utilization of spectrum sensing is required. In this paper, we propose a noise prediction method based on deep learning using spectrograms extracted from wireless signals. The proposed method achieved promising results using several state-of-art computer vision architectures.

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