Deep Learning for Modulation Recognition: A Survey With a Demonstration

Abstract

In this paper, we review a variety of deep learning algorithms and models for modulation recognition and classification of wireless communication signals. Specifically, deep learning (DL) has shown overwhelming advantages in computer vision, robotics, and voice recognition. Recently, DL has been proposed to apply to wireless communications for signal detection and classification in order to better learn the active users for electromagnetic spectrum sharing purposes. Therefore, we aim to provide a survey on the most recent techniques which use DL for recognizing and classifying a wireless signal. We focus on the most widely used DL models, emphasize the advantages and limitations, and discuss the challenges as well as future directions. In addition, we also apply a DL algorithm, convolutional neural network (CNN), to demonstrate the feasibility of using CNN to recognize and classify the over-the-air wireless signals using Mathworks DL toolbox with PlutoSDR and Universal Software Radio Peripheral (USRP), respectively.

Machine Learning Based Automatic Modulation Recognition for Wireless Communications: A Comprehensive Survey

“…In the literature, there are some brief surveys on DL-AMR 1 for SISO communication systems, e.g., [8] and [9]. For instance, the third section in [8] was dedicated to giving a brief overview of modulation recognition approaches based on the category of the extracted features.…”

“…However, several DL-AMR models were also reviewed in terms of the employed features and classification criteria. Likewise, in [9], a brief review of DL-AMR techniques in terms of modulation pool are provided. The main DL models in the AMR methods were outlined along with a demonstration on the feasibility of using Convolutional Neural Network (CNN) to recognize wireless signals.…”

Intelligent Modulation Pattern Recognition Based on Wavelet Approximate Coefficient Entropy in Cognitive Radio Networks

“…Compared with the traditional feature extractions and classification methods, deep learning has been applied to modulation recognition of communication signals because of its strong classification ability and fitting ability to nonlinear functions, which provides a new solution to the problems existing in wavelet analysis [15], [16]. In [17], a method based on deep learning is proposed, which combined with two convolutional neural networks trained on different data sets to achieve a relatively high automatic modulation recognition rate.…”

“…In the simulation, 500 repeated experiments were performed to analyze the recognition rate of three kinds of wavelet entropy, and the classification and recognition were carried out according to the range of the characteristic parameters corresponding to different modulation signals. Take a= [8,16,32,16,8,4] and the recognition rate is shown in Fig. 10.…”

Few-Shot Modulation Classification Method Based on Feature Dimension Reduction and Pseudo-Label Training

“…The authors of [6], [7] used information entropy features for AMC. In deep learning based methods domain, authors summarized the typical AMC methods based on deep learning in recent years in [8]. The authors of [9], [10] started using supervised learning for AMC in 2016 firstly, they used convolution neural network (CNN) to construct an end-to-end learning model, and successfully identified 11 digital signals with different modulations, including Wide Band Frequency Modulation (WBFM), Double Side Band (DSB), Binary Phase Shift Keying (BPSK) and 16 Quadrature Amplitude Modulation (QAM).…”