Journal
IEEE WIRELESS COMMUNICATIONS LETTERS
Volume 9, Issue 10, Pages 1629-1632Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LWC.2020.2999453
Keywords
Feature extraction; Modulation; Signal to noise ratio; Training; Data mining; Convolution; Deep learning; Automatic modulation recognition; deep learning; multi-channel
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Funding
- National Science Foundation (NSFC) [61871096]
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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.
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