Open AccessA Hybrid Approach for Signal Modulation Recognition Using Deep Learning Methods
Author(s) -
Changjun Fan,
Yufeng Wang,
Ming Liu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1757/1/012074
Subject(s) - computer science , modulation (music) , convolutional neural network , scheme (mathematics) , artificial intelligence , signal (programming language) , representation (politics) , pattern recognition (psychology) , deep learning , artificial neural network , machine learning , speech recognition , mathematics , mathematical analysis , programming language , philosophy , politics , political science , law , aesthetics
In this study, a novel signal modulation recognition framework has been proposed for automatically classifying eleven different modulation types with various SNR values. The framework employs both the raw complex-valued I/Q signal and its time-frequency description to represent the radio signal. And, a hybrid deep neural network is presented to recognize different modulation types from the representation data by leveraging the appealing properties of a convolutional neural network (CNN) and a long short-term memory (LSTM) network. Extensive validation of our scheme is performed on a large public dataset by comparing it with three existing 。 methods from literature, and our scheme yields quite promising results in terms of recognition accuracy.