Deep Learning-assisted Automatic Modulation Classification using Spectrograms
Received: 19 October 2024 | Revised: 25 November 2024 and 8 December 2024 | Accepted: 12 December 2024 | Online: 2 February 2025
Corresponding author: Hamza Ouamna
Abstract
With the increasing demand for reliable and efficient V2X (Vehicle-to-Everything) communications in cognitive radio environments, spectrum sharing becomes imperative. In this context, accurate modulation classification serves as a fundamental component for efficient spectrum sensing and allocation. This paper proposes a novel approach utilizing Convolutional Neural Networks (CNNs) trained on spectrograms of BPSK and QPSK modulation schemes for automatic modulation classification in V2X scenarios. Experimental results demonstrated the effectiveness of the proposed CNN-based framework in accurately classifying modulation schemes in V2X communications.
Keywords:
automatic modulation classification, cognitive radio, BPSK, QPSK, CNN, Alexnet, V2XDownloads
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