A Spatiotemporal Multi-Channel Learning Framework for Automatic Modulation Recognition

Xu, 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)


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
Faculty \ School: Faculty of Science > School of Computing Sciences
Related URLs:
Depositing User: LivePure Connector
Date Deposited: 22 Jul 2020 23:40
Last Modified: 07 Oct 2020 23:56
URI: https://ueaeprints.uea.ac.uk/id/eprint/76236
DOI: 10.1109/LWC.2020.2999453

Actions (login required)

View Item View Item