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DC Field | Value | Language |
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dc.contributor.author | Li, S | - |
dc.contributor.author | Hu, S | - |
dc.contributor.author | Nilavalan, N | - |
dc.coverage.spatial | Qindao, China | - |
dc.date.accessioned | 2023-07-05T20:30:41Z | - |
dc.date.available | 2023-07-05T20:30:41Z | - |
dc.date.issued | 2023-06-17 | - |
dc.identifier | ORCID iDs: Shaoqing Hu https://orcid.org/0000-0001-8642-2914; Nila Nilavalan https://orcid.org/0000-0001-8168-2039. | - |
dc.identifier.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. | en_US |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/26785 | - |
dc.description.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. | en_US |
dc.format.extent | 1 - 4 | - |
dc.format.medium | Electronic | - |
dc.language.iso | en_US | en_US |
dc.publisher | ICICA | en_US |
dc.source | 12th International Conference on Information Communication and Applications | - |
dc.source | 12th International Conference on Information Communication and Applications | - |
dc.subject | Sub-Nyquist | en_US |
dc.subject | modulation recognition | en_US |
dc.subject | convolutional neural network | en_US |
dc.subject | deep learning | en_US |
dc.title | Deep Learning Based Sub-Nyquist Modulation Recognition | en_US |
dc.type | Conference Paper | en_US |
pubs.finish-date | 2023-06-18 | - |
pubs.finish-date | 2023-06-18 | - |
pubs.publication-status | Accepted | - |
pubs.start-date | 2023-06-17 | - |
pubs.start-date | 2023-06-17 | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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FullText.pdf | 464.34 kB | Adobe PDF | View/Open |
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