Wireless communication modulating signal identification method based on deep learning

Abstract

The invention discloses a wireless communication modulating signal identification method based on deep learning for mainly solving the problem that the identification effect depends too much on manualmodulating signal feature extraction in the prior art and improving the drawbacks of low identification accuracy in the case of low signal to noise ratio in the prior art. The method comprises the following steps: sampling captured to-be-identified modulating signals; performing normalization on a sampling sequence obtained by sampling, and drawing a two-dimensional histogram of the modulating signals according to the normalized sampling sequence; constructing a deep convolutional neural network; training the deep convolutional neural network by using training examples; and identifying wireless communication modulating signals by using the trained deep convolutional neural network. By adoption of the wireless communication modulating signal identification method disclosed by the invention, the identification effect of the modulating signals does not depend on the manual feature selection and extraction, and very high identification accuracy is also ensured in the case of low signal tonoise ratio.

Description

technical field [0001] The invention belongs to the technical field of communication, and further relates to a deep learning-based wireless communication modulation signal identification method in the technical field of wireless communication. The invention is mainly used in the wireless communication to identify the modulation method of the wireless communication modulation signal through the deep convolutional neural network. Background technique [0002] The identification of wireless communication modulation signals is mainly for the identification of modulation methods of wireless communication signals. Typical modulation methods of wireless communication signals include amplitude modulation MASK, phase modulation MPSK, frequency modulation MFSK, and quadrature amplitude modulation MQAM. As an intermediate step of signal detection, software radio and other technologies, modulation signal recognition technology has been applied more and more in the field of wireless comm...

AI Technical Summary

Problems solved by technology

The disadvantage of this method is that the recognition effect of using the decision threshold to identify the wireless communication modulation signal is poor when the signal-to-noise ratio is low, and the recognition accuracy of this method depends heavily on the extraction of the characteristics of the wireless communication modulation signal. The quality of the envelope square spectrum feature extraction in this method directly affects the recognition accuracy of the modulated signal, resulting in poor performance of this method in practical applications.

The disadvantage of this method is that the recognition accuracy of this method depends heavily on the extraction of wireless communication modulation signal features, and the types of modulation signals that can be identified by extracting order statistics as features are limited to a few types of modulation signals, which leads to the application of this method. The scene is relatively small, and the recognition method using order statistics is relatively poor when the signal-to-noise ratio is relatively small

Images