An efficient modulation classification method using signal constellation diagrams with convolutional neural networks, Gabor filtering, and thresholding

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). INTRODUCTIONDue to the increasingly growing demand for wireless radio spectrum bandwidth, better approaches for utilization of the radio spectrum are essential. 1 The automatic modulation classification (AMC) is one of the most common techniques for identifying frequency spectra, spectrum management, electronic warfare, cognitive radio networks, interference

Automatic modulation classification with 2D transforms and convolutional neural network

“…Abdel‐Moneim et al 1 investigated the AMC problem with different pretrained CNNs including AlexNet, and ResNet using constellation diagrams extracted from the received signals. They extended the Gabor filtering approach adopted in the track of feature extraction from fingerprint patterns to the area of AMC.…”

“…Over high‐quality channels, it is possible to use high modulation orders to achieve the highest possible communication efficiency, without the burden of dealing with the noise effect. On the other hand, as the channel begins to deteriorate, the modulation order needs to be lowered to keep an acceptable performance level from the bit error rate (BER) perspective 1 . For the receiver to perform its main demodulation task, the modulation format should be known ahead.…”

AlexNet-based Visible light communication devices fingerprint extraction and authentication in broadcast systems

“…AlexNet uses several new features to improve training efficiency and classification accuracy. For example, AlexNet uses a non-saturating activation function Re-LU [25], as shown in the following equation:…”

Diffusion-Based Radio Signal Augmentation for Automatic Modulation Classification

“…This backdrop sets the stage for our investigation into AMC, as it seeks to harness deep learning algorithms to automatically identify and adapt to various modulation schemes, further enhancing the capabilities of cognitive radios and adaptive communication systems. The ability to accurately and efficiently discern modulation categories is crucial not only for the operation of modern communication systems [3,4] but also for the significant role it plays in advancing areas such as cognitive radio [5], spectrum monitoring [6], and electronic warfare [7].…”