Query: Automatic modulation classification using contrastive fully convolutional network

title: Automatic Modulation Classification Using Contrastive Fully Convolutional Network
abstract: Automatic modulation classification (AMC) aims at identifying the modulation format of the received signal. In this letter, we propose a novel grid constellation matrix (GCM) based AMC method using a contrastive fully convolutional network (CFCN) We use GCMs as the input of the network, which are extracted from the received signals using low complexity preprocessing. Moreover, a loss function with contrastive loss is designed to train the CFCN, which boosts the discrepancies among different modulations and obtains discriminative representations. Extensive simulations demonstrate that CFCN performs superior classification performance and better robustness to model mismatches with low training time comparing with other recent methods.

title: Features Fusion based Automatic Modulation Classification Using Convolutional Neural Network
abstract: The modulation format is a key parameter that influences the monitoring of the intercepted signals. Automatic modulation classification (AMC) is utilized to recognize the modulation format of the intercepted signals. However, most recent AMC methods neglect the complementarity accross different features. In this paper, we propose a novel feature fusion based AMC scheme using the convolutional neural network (FFCNN) Fused feature is generated by concatenating the two dimensional spectrum correlation function (SCF) images and the graphic constellation (GC) images. Moreover, the FFCNN classifier is adopted to obtain more discriminative representations, leading to improved final modulation classification performance. Extensive simulations demonstrate that the proposed FFCNN scheme outperforms other recent methods.

title: Automatic Modulation Classification Using Multi Scale Convolutional Neural Network
abstract: In this paper, a multi scale convolutional neural network based (MSN) method is proposed for robust automatic modulation classification (AMC) The classifier directly utilizes in phase and quadrature (I/Q) samples to identify the modulation type of received signal without any data preprocessing, thereby reducing the computational complexity. Further, the network architecture employs one dimensional convolution (Conv1D) to extract multi scale feature maps due to its merits of low computational complexity. Then these multi scale feature maps are merged together by repeated multi scale fusions, in order to improve the classification accuracy performance and the robustness to varying SNR environment. Repeated multi scale fusions can make better use of amplitude phase information because it can learn the local changes brought by modulation as well as the timing characteristics of the samples. Simulation results show that proposed MSN achieves classification rate of 97.38% classification accuracy at high SNR regimes for 24 different modulation types on the public well known over the air (OTA) dataset. Moreover, MSN still can recognize the modulation types of received signals with the accuracy rates of about 95% under varying SNR scenarios. Compared to the methods proposed in other papers, our classifier not only shows a better performance in terms of classification accuracy, but also is the most robust in varying SNR environment.

title: Automatic Modulation Classification Using Convolutional Neural Network With Features Fusion of SPWVD and BJD
abstract: Automatic modulation classification (AMC) is becoming increasingly important in spectrum monitoring and cognitive radio. However, most existing modulation classification algorithms neglect the complementarities between different features and the importance of features fusion. To remedy these flaws, this paper presents a scheme of features fusion for AMC using convolutional neural network (CNN) The approach attempts to fuse different images and handcrafted features of signals to obtain more discriminating features. First, eight handcrafted features and different images features are both extracted. In the latter, signals are converted into two kinds of time frequency images by smooth pseudo wigner ville distribution and Born Jordan distribution, and a fine tuned CNN model is utilized to extract image features. Second, the joint features are formed by combination of each of image and handcrafted features, and a multimodality fusion model is applied to fuse the joint features to yield further improvement. Finally, simulation results reveal the superior performance of the proposed scheme. It is worth mentioning that the classification accuracy can reach 92.5% with signal to noise ratio at $4 dB.

title: Automatic Modulation Classification Using Compressive Convolutional Neural Network
abstract: The deep convolutional neural network has strong representative ability, which can learn latent information repeatedly from signal samples and improve the accuracy of automatic modulation classification (AMC) In this paper, a novel compressive convolutional neural network (CCNN) is proposed for AMC, where different constellation images, i.e. regular constellation images (RCs) and contrast enhanced grid constellation images (CGCs) are generated as network inputs from received signals. Moreover, a compressive loss constraint is proposed to train the CCNN, which aims at capturing high dimensional features for modulation classification. Additionally, CCNN utilizes intra class compactness and inter class separability to enhance the classification and robustness performance for the different orders of modulations. The simulation results demonstrate that CCNN displays superior classification and robustness performance than existing AMC methods.

title: AMC IoT: Automatic Modulation Classification Using Efficient Convolutional Neural Networks for Low Powered IoT Devices
abstract: Automatic modulation classification (AMC) is used to identify the modulation for the received signal. IoT devices use modern communication methods which are based on multiple input multiple output (MIMO) in which the signals are received from various sources. The identification of modulation is vital. Feature based AMC methods combined with deep learning techniques has the potential to meet the latency requirement in the IoT applications. An efficient convolutional neural network based on depthwise separable convolution has been proposed to classify the modulation of the received signals. The proposed architecture has 58% less parameters than the conventional convolutional architecture and the performance is comparable.

title: Automatic Modulation Classification Using a Deep Multi Stream Neural Network
abstract: In wireless communication, modulation classification is an important part of the non cooperative communication, and it is difficult to classify the various modulation schemes using conventional methods. The deep learning network has been used to handle the problem and acquire good results. In the deep convolutional neural network (CNN) the data length in the input is fixed. However, the signal length varies in communication, and it causes that the network cannot take advantage of the input signal data to improve the classification accuracy. In this paper, a novel deep network method using a multi stream structure is proposed. The multi stream network form increases the network width, and enriches the types of signal features extracted. The superposition convolutional unit in each stream can further improve the classification performance, while the shallower network form is easier to train for avoiding the over fitting problem. Further, we show that the proposed method can learn more features of the signal data, and it is also shown to be superior to common deep networks.

title: Automatic Modulation Classification using Convolutional Neural Network
abstract: With the increasing demands in the field of communication systems evolved the idea of cognitive radio, the design of an intelligent radio capable of sensing the channel conditions and hence deciding the optimal method of connection. Such systems are designed to operate over different channel conditions with different modulation schemes. A modulation classifier is an essential module in such systems and this paper proposes one such design based on convolutional neural networks. The proposed system tries to evaluate the performance of modulation classification over 3 different modulation classes over the Tensor Flow framework.

title: Automatic Modulation Classification Using CNN LSTM Based Dual Stream Structure
abstract: Deep learning (DL) has recently aroused substantial concern due to its successful implementations in many fields. Currently, there are few studies on the applications of DL in the automatic modulation classification (AMC) which plays a critical role in non cooperation communications. Besides, most previous work ignores the feature interaction, and only considers spatial or temporal attributes of signals. Combining the advantages of the convolutional neural network (CNN) and the long short term memory (LSTM) this paper addresses the AMC using CNN LSTM based dual stream structure, which efficiently explores the feature interaction, and the spatial temporal properties of raw complex temporal signals. Specifically, a preprocessing step is first implemented to convert signals into the temporal in phase/quadrature $I/Q$ format, and the amplitude/phase $A/P$ representation, which facilitates the acquirement of more effective features for classification. To extract features from each signal pattern, each stream is composed of CNN, and LSTM (denoted as CNN LSTM) Most importantly, the features learned from two streams interact in pairs, which increases the diversity of features and thereby further improves the performance. Finally, some comparisons with previous work are performed. The experimental results not only demonstrate the superior performance of the proposed method compared with the existing state of the art methods, but also reveal the potential of DL based approaches for AMC.

title: Automatic determination of digital modulation types with different noises using Convolutional Neural Network based on time frequency information
abstract: In this study, a novel digital modulation classification model has been proposed for automatically recognizing six different modulation types including amplitude shift keying (ASK) frequency shift keying (FSK) phase shift keying (PSK) quadrate amplitude shift keying (QASK) quadrate frequency shift keying (QFSK) and quadrate phase shift keying (QPSK) The determination of modulation type is significant in military communication, satellite communication systems, and submarine communication. To classify the modulation types, we have proposed a two stage hybrid method combining short time Fourier transform (STFT) and convolutional neural network (CNN) In the first stage, as the data source, the time frequency information from these modulation signals have been extracted with STFT. This information has been obtained as 2D images to feed the input of the CNN deep learning method. In the second stage, the obtained 2D time frequency information has been given to the input of the CNN algorithm to classify the modulation types. In this work, noises at various SNR values from 0 dB to 25 dB were created and added to the modulated signals. Even in the presence of noise, the proposed hybrid deep learning model achieved excellent results in the noised modulation signals.