Automatic Modulation Mode Recognition of Communication Signals Based on Complex-Valued Neural Network

Abstract

Intelligent transportation systems (ITS) are designed to provide efficient and comfortable transportation. The development of ITS has brought new communication challenges, which require faster and more reliable transmission of information. In this paper, we investigate the modulation mode recognition method of communication signals based on a complex-valued neural network (CVNN). By combining a complex-valued convolutional neural network (CVCNN) with complex-valued long short-term memory (CVLSTM) and adding a residual learning unit, a modulation recognition model is established. The model can automatically learn from complex-valued signals without manual feature extraction and can recognize 11 modulation modes (3 analog modulation modes and 8 digital modulation modes) with a signal-to-noise ratio (SNR) between −20 dB and 18 dB. We design a Gaussian filter, and divide the signal to be identified into high SNR signal and low SNR signal through SNR estimation. The low SNR signal is Gaussian filtered before modulation recognition, so as to improve its modulation recognition accuracy. The algorithm proposed in this paper directly recognizes the modulation mode of the complex-valued signal without any preprocessing, and the recognition accuracy is better than the existing algorithms. This work is of great significance to the improvement of information transmission speed and the construction of ITS.