@inproceedings{5a7d69597b4b4df09f900da7c5e6f01b,
title = "Noise Floor Estimation Based on Deep CNNs",
abstract = "This paper proposed a new method for noise estimation based on deep learning. We treat the wideband power spectrum as a one-dimensional (1-D) gray image and regard the noise floor estimation problem as a curve regression task. We design an end-to-end deep learning model based on convolutional neural networks (CNNs) to accomplish the task. By using sufficient numbers of simulation noise floor labeled spectra samples to train the model, experimental results show that our model can effectively regress the noise floor of the wideband power spectra. Comparing to the nonlinear recursive smoothing filter method, our method not only can be suitable for the single narrow carrier signal noise floor estimation but also gain good results when there exist multiple carriers in the wideband power spectrum.",
keywords = "convolutional neural networks, deep learning, noise floor estimation",
author = "Hao Huang and Jianqing Li and Jiao Wang and Hong Wang",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2020 ; Conference date: 17-10-2020 Through 19-10-2020",
year = "2020",
month = oct,
day = "17",
doi = "10.1109/CISP-BMEI51763.2020.9263608",
language = "English",
series = "Proceedings - 2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "574--579",
editor = "Qiang Zheng and Xiaopeng Zheng and Xiangfu Zhao and Weiqing Yan and Nan Zhang and Lipo Wang",
booktitle = "Proceedings - 2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2020",
address = "United States",
}