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Sep 19, 2019 · This paper investigates algorithms for multi-signals detection and modulation classification, which are significant in many communication systems.
In this paper we develop a novel modulation classification scheme based on multifractal dimensions. Theoretic analysis demonstrates that these features are�...
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Jun 8, 2023 · We propose a hybrid heterogeneous modulation classification architecture named DeepSIG, which integrates Recurrent Neural Network (RNN), Convolutional Neural ...
Experimental results demonstrate that the DL framework is capable of detecting and recognizing signals and compared to the traditional methods and other ...
Dec 1, 2017 · In this work, we investigate the value of employing deep learning for the task of wireless signal modulation recognition.
This thesis investigates the value of employing deep learning for the task of wireless signal modulation recognition.
This paper proposes a combined Convolutional Neural Network (CNN) scheme appropriate for automatic modulation recognition.
The purpose of this project is to explore the usage of Spiking Neural Networks and an approach for training them (Deep Continuous Local Learning - DECOLLE) in ...
Aug 20, 2023 · In this study, we propose a CNN-transformer graph neural network (CTGNet) for modulation classification, to uncover complex representations in signal data.
However, its usage in communication systems has not been well explored. This paper investigates algorithms for multi-signals detection and modulation ...