A modulation recognition method based on a con-volutional neural network (CNN) architecture is assessed through classification of synthetic baseband signals in the presence of a second interfering signal source. The complexity and adaptability of CNNs is leveraged so as to forgo statistical feature extraction procedures and efficiently classify based on raw signals or their modified forms. Both scenarios with the interfering signal's modulation scheme known and unknown, are considered. Simulation results show that the CNN architecture achieves considerable accuracy despite the presence of interference, and the knowledge of the modulation scheme of the interfering signal significantly improves the accuracy.

Automatic Modulation Classification in the Presence of Interference / Triantaris, P.; Tsimbalo, E.; Chin, W. H.; Gunduz, D.. - (2019), pp. 549-553. (Intervento presentato al convegno 28th European Conference on Networks and Communications, EuCNC 2019 tenutosi a esp nel 2019) [10.1109/EuCNC.2019.8802004].

Automatic Modulation Classification in the Presence of Interference

D. Gunduz
2019

Abstract

A modulation recognition method based on a con-volutional neural network (CNN) architecture is assessed through classification of synthetic baseband signals in the presence of a second interfering signal source. The complexity and adaptability of CNNs is leveraged so as to forgo statistical feature extraction procedures and efficiently classify based on raw signals or their modified forms. Both scenarios with the interfering signal's modulation scheme known and unknown, are considered. Simulation results show that the CNN architecture achieves considerable accuracy despite the presence of interference, and the knowledge of the modulation scheme of the interfering signal significantly improves the accuracy.
2019
28th European Conference on Networks and Communications, EuCNC 2019
esp
2019
549
553
Triantaris, P.; Tsimbalo, E.; Chin, W. H.; Gunduz, D.
Automatic Modulation Classification in the Presence of Interference / Triantaris, P.; Tsimbalo, E.; Chin, W. H.; Gunduz, D.. - (2019), pp. 549-553. (Intervento presentato al convegno 28th European Conference on Networks and Communications, EuCNC 2019 tenutosi a esp nel 2019) [10.1109/EuCNC.2019.8802004].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1202702
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