TY - GEN
T1 - A Deep Learning Approach to Radio Signal Denoising
AU - Almazrouei, Ebtesam
AU - Gianini, Gabriele
AU - Almoosa, Nawaf
AU - Damiani, Ernesto
N1 - Funding Information:
ACKNOWLEDGEMENTS Authors GG and ED acknowledge the support by the EU H2020, research programme, through the projects Toreador (No. 688797) and Threat-Arrest (No. 786890).
Funding Information:
Authors GG and ED acknowledge the support by the EU H2020, research programme, through the projects Toreador (No. 688797) and Threat-Arrest (No. 786890).
Publisher Copyright:
© 2019 IEEE.
PY - 2019/4
Y1 - 2019/4
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85075782422&partnerID=8YFLogxK
U2 - 10.1109/WCNCW.2019.8902756
DO - 10.1109/WCNCW.2019.8902756
M3 - Conference contribution
AN - SCOPUS:85075782422
T3 - 2019 IEEE Wireless Communications and Networking Conference Workshop, WCNCW 2019
BT - 2019 IEEE Wireless Communications and Networking Conference Workshop, WCNCW 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE Wireless Communications and Networking Conference Workshop, WCNCW 2019
Y2 - 15 April 2019 through 18 April 2019
ER -