Abstract
Recently, automatic modulation classification (AMC) has extensively and commonly been utilized in several modern wireless communication systems as a significant tool of signal detection for civilian and military applications and cognitive radio systems. Although several methods have been established to identify the modulation scheme of a received signal, they show a difficulty of learning radio characteristics for most conventional machine learning algorithms. This article focuses on the deep learning (DL) classification technique to solve these problems. To improve the classification accuracy of a communication signal modulation type, we apply a new model that combines Gabor filtering and thresholding with the help of convolution filters implemented in DL. A basic convolutional neural network, AlexNet, and a residual neural network are used for being compatible with constellation diagrams in order to achieve a superior classification performance. Moreover, the Gabor filter can effectively extract spatial information, including edges and textures. In terms of classification accuracy, the proposed AMC system improves the signal modulation classification accuracy significantly, and achieves competitive results. We use seven modulation types over the range of signal‐noise ratio (SNR) values from −10 to 30 dB. The performed experiments reveal that the proposal guarantees a remarkable classification accuracy of approximately 100% at a 10 dB SNR over AWGN and Rayleigh fading channels. Therefore, to prove the functional viability of our proposed method, it can be applied in adaptive modulators that can be used in many devices in applications such as Internet‐of‐Things (IoT).
Recently, automatic modulation classification (AMC) has been extensively utilized in several modern wireless communication systems as an important tool of signal identification for cognitive radio. In this article, we propose a new data conversion method for AMC. The conversion is performed on received 1D signals to obtain 2D constellation diagrams for further classification. We apply a new model, which combines Gabor filtering, thresholding, and then processing with convolution filters embedded in the convolutional neural network (CNN). Both basic CNN, AlexNet, and a residual neural network are considered for being compatible with constellation diagram images in order to achieve a good classification performance. The performed experiments ensure that the suggested method guarantees a remarkable classification accuracy over AWGN and Rayleigh fading channels.