Modulation pattern recognition of M-QAM signals based on convolutional neural network and extreme learning machine

Wanpei Chen; Shen Gao; Tao Zhang; Qinrong Yang

doi:10.1117/12.2576969

12 October 2020 Modulation pattern recognition of M-QAM signals based on convolutional neural network and extreme learning machine

Wanpei Chen, Shen Gao, Tao Zhang, Qinrong Yang

Author Affiliations +

Proceedings Volume 11574, International Symposium on Artificial Intelligence and Robotics 2020; 1157407 (2020) https://doi.org/10.1117/12.2576969
Event: International Symposium on Artificial Intelligence and Robotics (ISAIR), 2020, Kitakyushu, Japan

Abstract

In this paper, we propose a workflow and a deep learning algorithm for recognizing Quadrature amplitude modulation signal(QAM), this design adopts a convolutional neural network (CNN) and Extreme Learning Machine (ELM) as the core，leverage the powerful feature extraction of CNN and fast classification learning of ELM. The spectrogram image features of the signal obtained by short-time Fourier transform (STFT) are input to the CNN-ELM hybrid model, the modulation mode of the QAM signal is finally recognized by ELM. This algorithm surmounts the shortcomings of traditional methods well, Simulation results also verify the superiority of the proposed system whose classification accuracy is beyond 99.86%.

Citation Download Citation

Wanpei Chen, Shen Gao, Tao Zhang, and Qinrong Yang "Modulation pattern recognition of M-QAM signals based on convolutional neural network and extreme learning machine", Proc. SPIE 11574, International Symposium on Artificial Intelligence and Robotics 2020, 1157407 (12 October 2020); https://doi.org/10.1117/12.2576969

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available