This paper proposes a Deep Learning approach to radio signal de-noising. This approach is data-driven, thus it allows de-noising signals, corresponding to distinct protocols, without requiring explicit use of expert knowledge, in this way granting higher flexibility. The core component of the Artificial Neural Network architecture used in this work is a Convolutional De-noising AutoEncoder. We report about the performance of the system in spectrogram-based denoising of the protocol preamble across protocols of the IEEE 802.11 family, studied using simulation data. This approach can be used within a machine learning pipeline: the denoised data can be fed to a protocol classifier. A further perspective advantage of using the AutoEncoders in such a pipeline is that they can be co-trained with the downstream classifier (protocol detector), to optimize its accuracy.

Almazrouei, E., Gianini, G., Almoosa, N., Damiani, E. (2019). A Deep Learning Approach to Radio Signal Denoising. In 2019 IEEE Wireless Communications and Networking Conference Workshop, WCNCW 2019 (pp.1-8). IEEE [10.1109/WCNCW.2019.8902756].

A Deep Learning Approach to Radio Signal Denoising

Gianini G.;
2019

Abstract

This paper proposes a Deep Learning approach to radio signal de-noising. This approach is data-driven, thus it allows de-noising signals, corresponding to distinct protocols, without requiring explicit use of expert knowledge, in this way granting higher flexibility. The core component of the Artificial Neural Network architecture used in this work is a Convolutional De-noising AutoEncoder. We report about the performance of the system in spectrogram-based denoising of the protocol preamble across protocols of the IEEE 802.11 family, studied using simulation data. This approach can be used within a machine learning pipeline: the denoised data can be fed to a protocol classifier. A further perspective advantage of using the AutoEncoders in such a pipeline is that they can be co-trained with the downstream classifier (protocol detector), to optimize its accuracy.
paper
IEEE Standards; Network architecture; Neural networks; Pipelines; Signal denoising
English
2019 IEEE Wireless Communications and Networking Conference Workshop, WCNCW 2019 - 15 April 2019 through 18 April 2019
2019
2019 IEEE Wireless Communications and Networking Conference Workshop, WCNCW 2019
9781728109220
2019
1
8
8902756
reserved
Almazrouei, E., Gianini, G., Almoosa, N., Damiani, E. (2019). A Deep Learning Approach to Radio Signal Denoising. In 2019 IEEE Wireless Communications and Networking Conference Workshop, WCNCW 2019 (pp.1-8). IEEE [10.1109/WCNCW.2019.8902756].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/454843
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