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http://bura.brunel.ac.uk/handle/2438/26785
Title: | Deep Learning Based Sub-Nyquist Modulation Recognition |
Authors: | Li, S Hu, S Nilavalan, N |
Keywords: | Sub-Nyquist;modulation recognition;convolutional neural network;deep learning |
Issue Date: | 17-Jun-2023 |
Publisher: | ICICA |
Citation: | Li, S., Hu, S. and Nilavalan, N. (2023) 'Deep Learning Based Sub-Nyquist Modulation Recognition', Proceedings of the 12th International Conference on Information Communication and Applications, Qindao, China, 17-18 June, pp. 1 - 4. |
Abstract: | In this paper, we designed a Convolutional Neural Network (CNN) for Sub-Nyquist modulation recognition and compare the performance Long Short-Term Memory (LSTM) network and Convolutional Long Short-term Deep Neural Network (CLDNN) respectively. Unlike conventional modulation recognition task that operates with Nyquist sampled rate, the network architectures for Sub-Nyquist modulation recognition were specifically designed with a certain number of neurons, layers, and other hyperparameters to effectively extract key features from Sub-Nyquist sampled signals and process larger volumes of data. The simulation results demonstrate that the CNN network has the best recognition accuracy of 98.01% on the GBsense dataset, followed by the CLDNN of 96.81% and LSTM of 87.51% respectively. |
URI: | https://bura.brunel.ac.uk/handle/2438/26785 |
Other Identifiers: | ORCID iDs: Shaoqing Hu https://orcid.org/0000-0001-8642-2914; Nila Nilavalan https://orcid.org/0000-0001-8168-2039. |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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