A Spatiotemporal Multi-Channel Learning Framework for Automatic Modulation Recognition

• Published in: IEEE wireless communications letters Vol. 9; no. 10; pp. 1629 - 1632
• Main Authors: Xu, Jialang, Luo, Chunbo, Parr, Gerard, Luo, Yang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-10-2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Automatic modulation recognition; Communications systems; Convolution; Data mining; Deep learning; Feature extraction; Modulation; multi-channel; Quadrature amplitude modulation; Signal to noise ratio; Training
• ISSN: 2162-2337; 2162-2345
• DOI: 10.1109/LWC.2020.2999453

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.