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ToolsXu, Jialang, Luo, Chunbo, Parr, Gerard and Luo, Yang (2020) A spatiotemporal multi-channel learning framework for automatic modulation recognition. IEEE Wireless Communications Letters, 9 (10). pp. 1629-1632. ISSN 2162-2337
Full text not available from this repository. (Request a copy)Abstract
Automatic modulation recognition (AMR) plays a vital role in modern communication systems. This letter proposes a novel three-stream deep learning framework to extract the features from individual and combined in-phase/quadrature (I/Q) symbols of the modulated data. The proposed framework integrates one-dimensional (1D) convolutional, two-dimensional (2D) convolutional and long short-term memory (LSTM) layers to extract features more effectively from a time and space perspective. Experiments on the benchmark dataset show the proposed framework has efficient convergence speed and achieves improved recognition accuracy, especially for the signals modulated by higher dimensional schemes such as 16 quadrature amplitude modulation (16-QAM) and 64-QAM.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | automatic modulation recognition,deep learning,multi-channel,control and systems engineering,electrical and electronic engineering ,/dk/atira/pure/subjectarea/asjc/2200/2207 |
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Smart Emerging Technologies (former - to 2025) Faculty of Science > Research Groups > Cyber Security Privacy and Trust Laboratory (former - to 2025) Faculty of Science > Research Groups > Data Science and AI Faculty of Science > Research Groups > Cyber Intelligence and Networks |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 22 Jul 2020 23:40 |
| Last Modified: | 28 Mar 2025 09:59 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/76236 |
| DOI: | 10.1109/LWC.2020.2999453 |
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