Autors: Ivanov, A. S., Tonchev K. T., Poulkov, V. K., Al-Shatri H., Klein A.
Title: Hybrid Noise-Resilient Deep Learning Architecture for Modulation Classification in Cognitive Radio Networks
Keywords: Cognitive Radio, Deep Learning, Modulation classification, s

Abstract: The increasing maturity of the concepts which would allow for the operation of a practical Cognitive Radio Network require functionalities that include different methodologies from others. One such approach is Deep Learning which can be applied to diverse problems in CR to enhance its effectiveness by increasing the utiliza- tion of the unused radio spectrum. Using DL, the CR device can iden- tify whether the signal comes from the Primary User transmitter or from an interferer. The method proposed in this paper is a hybrid DL architecture which aims at achieving high recognition rate at low signal-to-noise ratio and various channel impairments including fading because these are the relevant conditions of operation of the CR. It consists of an autoencoder and a neural network structure due to the good denoising qualities of the former and the recognition accuracy of the latter. The autoencoder aims to restore the original signal from the corrupted samples.

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    4th EAI International Conference on Future Access Enablers of Ubiquitous and Intelligent Infrastructures “FABULOUS 2019”, 2018, Bulgaria,

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    Цитирания (Citation/s):
    1. Basha, N., Hamdaoui, B., Sivanesan, K., Guizani, M., "Channel-Resilient Deep-Learning-Driven Device Fingerprinting Through Multiple Data Streams", IEEE Open Journal of the Communications Society, vol. 4, pp. 118-133, 2023, DOI: 10.1109/OJCOMS.2022.3233372. - 2023 - в издания, индексирани в Scopus или Web of Science

    Вид: пленарен доклад в международен форум, публикация в реферирано издание, индексирана в Scopus и Web of Science