A spatiotemporal multi-channel learning framework for automatic modulation recognition

Xu, Jialang, Luo, Chunbo, Parr, Gerard ORCID: https://orcid.org/0000-0002-9365-9132 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

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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 Faculty of Science > Research Groups > Cyber Security Privacy and Trust Laboratory Faculty of Science > Research Groups > Data Science and AI
Related URLs:	http://www.scopus.com/inward/record.url?... https://ieeexplore.ieee.org/document/910...
Depositing User:	LivePure Connector
Date Deposited:	22 Jul 2020 23:40
Last Modified:	10 Dec 2024 01:34
URI:	https://ueaeprints.uea.ac.uk/id/eprint/76236
DOI:	10.1109/LWC.2020.2999453

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