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

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.