A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Constellation Shape. <s> Constellation diagram is a traditional and powerful tool for design and evaluation of digital modulations. In this work we propose to use constellation shape as a robust signature for digital modulation recognition. We represent the transmitted “information” by the geometry of the constellation. Received information is in turn the recovered constellation shape that is deformed by noise, channel and receiver implementation. We first demonstrate that fuzzy c-means clustering is capable of robust recovery of the unknown constellation. To perform Bayesian inference, the reconstructed constellation is modeled by a discrete multiple-valued nonhomogenous spatial random field. For candidate modulations, their corresponding random fields are modeled off-line. The unknown constellation shape is then classified by an ML rule based on the preceding model building phase. The algorithm is applicable to digital modulations of arbitrary size and dimensionality. <s> BIB001 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Constellation Shape. <s> Abstract Recently, deep learning has received a lot of attention in many machine learning applications for its superior classification performance in speech recognition, natural language understanding and image processing. However, it still lacks attention in automatic modulation classification (AMC) until now. Here, we introduce the application of deep learning in AMC. We propose a fully connected 2 layer feed-forward deep neural network (DNN) with layerwise unsupervised pretraining for the classification of digitally modulated signals in various channel conditions. The system uses independent autoencoders (AEs) for feature learning with multiple hidden nodes. Signal information from the received samples is extracted and preprocessed via I and Q components, and formed into training input to 1st AE layer. A probabilistic based method is employed at the output layer to detect the correct modulation signal. Simulation results show that a significant improvement can be achieved compared to the other conventional machine learning methods in the literature. Moreover, we also show that our proposed method can extract the features from cyclic-stationary data samples. A good classification accuracy was achieved, even when the proposed deep network is trained and tested at different SNRs. This shows the future potential of the deep learning model for application to AMC. <s> BIB002 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Constellation Shape. <s> Modulation identification of the transmitted signals remain a challenging area in modern intelligent communication systems like cognitive radios. The computation of the distinct features from input data set and applying machine learning algorithms has been a well-known method in the classification of such signals. However, recently, deep neural networks, a branch of machine learning, have gained significant attention in the pattern recognition of complex data due to its superior performance. Here, we test the application of deep neural networks to the automatic modulation classification in AWGN and flat-fading channel. Three training inputs were used; mainly 1) In-phase and quadrature (I-Q) constellation points, 2) the centroids of constellation points employing the fuzzy C-means algorithm to I-Q diagrams, and 3) the high-order cumulants of received samples. The unsupervised learning from these data sets was done using the sparse autoencoders and a supervised softmax classifier was employed for the classification. The designing parameters for training single and 2-layer sparse autoencoders are proposed and their performance compared with each other. The results show that a very good classification rate is achieved at a low SNR of 0 dB. This shows the potential of the deep learning model for the application of modulation classification. Method for AMC using powerful capability of deep networks.Comparison between a shallow and deep network in the application of AMC.2-layered deep neural network model outperforms other networks.The accuracy of this model converges to results obtained from conventional methods under flat-fading channels. <s> BIB003 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Constellation Shape. <s> In this paper, we propose a low-complexity graphic constellation projection (GCP) algorithm for automatic modulation classification (AMC), where the recovered symbols are projected into artificial graphic constellations. Unlike the existing feature- based (FB) algorithms, we convert the AMC problem into an image recognition problem. Subsequently, the deep belief network (DBN) is adopted to learn the underlying features in these constellations and recognize their corresponding modulation schemes. Simulation results demonstrate that the proposed GCP-DBN based AMC system achieves better performance than other schemes. Specifically, the classification accuracy is beyond 95% at 0 dB, which is very close to the average likelihood ratio test upper bound (ALRT-Upper Bound). <s> BIB004 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Constellation Shape. <s> An intelligent constellation diagram analyzer is proposed to implement both modulation format recognition (MFR) and optical signal-to-noise rate (OSNR) estimation by using convolution neural network (CNN)-based deep learning technique. With the ability of feature extraction and self-learning, CNN can process constellation diagram in its raw data form (i.e., pixel points of an image) from the perspective of image processing, without manual intervention nor data statistics. The constellation diagram images of six widely-used modulation formats over a wide OSNR range (15~30 dB and 20~35 dB) are obtained from a constellation diagram generation module in oscilloscope. Both simulation and experiment are conducted. Compared with other 4 traditional machine learning algorithms, CNN achieves the better accuracies and is obviously superior to other methods at the cost of O(n) computation complexity and less than 0.5 s testing time. For OSNR estimation, the high accuracies are obtained at epochs of 200 (95% for 64QAM, and over 99% for other five formats); for MFR, 100% accuracies are achieved even with less training data at lower epochs. The experimental results show that the OSNR estimation errors for all the signals are less than 0.7 dB. Additionally, the effects of multiple factors on CNN performance are comprehensively investigated, including the training data size, image resolution, and network structure. The proposed technique has the potential to be embedded in the test instrument to perform intelligent signal analysis or applied for optical performance monitoring. <s> BIB005 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Constellation Shape. <s> Deep learning (DL) is a powerful classification technique that has great success in many application domains. However, its usage in communication systems has not been well explored. In this paper, we address the issue of using DL in communication systems, especially for modulation classification. Convolutional neural network (CNN) is utilized to complete the classification task. We convert the raw modulated signals into images that have a grid-like topology and feed them to CNN for network training. Two existing approaches, including cumulant and support vector machine (SVM) based classification algorithms, are involved for performance comparison. Simulation results indicate that the proposed CNN based modulation classification approach achieves comparable classification accuracy without the necessity of manual feature selection. <s> BIB006 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Constellation Shape. <s> Automated modulation classification plays a very important part in cognitive radio networks. Deep learning is also a powerful tool that we could not overlook its potential in addressing signal modulation recognition problem. In our last work, we propose a new data conversion algorithm in order to gain a better classification accuracy of communication signal modulation, but we still believe that the convolution neural network (CNN) can work better. However, its application to signal modulation recognition is often hampered by insufficient data and overfitting. Here, we propose a smart approach to programmatic data augmentation method by using the auxiliary classifier generative adversarial networks (ACGANs). The famous CNN model, AlexNet, has been utilized to be the classifier and ACGAN to be the generator, which will enlarge our data set. In order to alleviate the common issues in the traditional generative adversarial nets training, such as discriminator overfitting, generator disconverge, and mode collapse, we apply several training tricks in our training. With the result on original data set as our baseline, we will evaluate our result on enlarged data set to validate the ACGAN’s performance. The result shows that we can gain 0.1~6% increase in the classification accuracy in the ACGAN-based data set. <s> BIB007 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Constellation Shape. <s> Deep Learning (DL) is such a powerful tool that we have seen tremendous success in areas such as Computer Vision, Speech Recognition, and Natural Language Processing. Since Automated Modulation Classification (AMC) is an important part in Cognitive Radio Networks, we try to explore its potential in solving signal modulation recognition problem. It cannot be overlooked that DL model is a complex model, thus making them prone to over-fitting. DL model requires many training data to combat with over-fitting, but adding high quality labels to training data manually is not always cheap and accessible, especially in real-time system, which may counter unprecedented data in dataset. Semi-supervised Learning is a way to exploit unlabeled data effectively to reduce over-fitting in DL. In this paper, we extend Generative Adversarial Networks (GANs) to the semi-supervised learning will show it is a method can be used to create a more data-efficient classifier. <s> BIB008 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Constellation Shape. <s> Deep learning (DL) is a new machine learning (ML) methodology that has found successful implementations in many application domains. However, its usage in communications systems has not been well explored. This paper investigates the use of the DL in modulation classification, which is a major task in many communications systems. The DL relies on a massive amount of data and, for research and applications, this can be easily available in communications systems. Furthermore, unlike the ML, the DL has the advantage of not requiring manual feature selections, which significantly reduces the task complexity in modulation classification. In this paper, we use two convolutional neural network (CNN)-based DL models, AlexNet and GoogLeNet. Specifically, we develop several methods to represent modulated signals in data formats with gridlike topologies for the CNN. The impacts of representation on classification performance are also analyzed. In addition, comparisons with traditional cumulant and ML-based algorithms are presented. Experimental results demonstrate the significant performance advantage and application feasibility of the DL-based approach for modulation classification. <s> BIB009
In digital modulation, the constellation is usually used to map the signal into scattered points. The constellation diagram provides an intuitive way to represent signal basic structure and the relationship between different modulation modes. In BIB001 , the constellation shape was employed as a reliable signature to realize digital modulation recognition. DL has been effectively used in constellation-based approaches for automatic modulation classification. In BIB002 BIB003 , the authors use the DL network combined with IQ components of constellation points as features. In BIB004 , a simple graphic constellation projection (GCP) scheme for AMC is presented. Unlike FB approaches, the AMC task is turned to image recognition technology. Reference BIB005 proposed a DL-based intelligent constellation analyzer that could realize modulation type recognition as well as OSNR estimation. The authors utilized CNN as DL models and treat constellation map as original processing features. Due to the automatic feature extraction and learning, CNN is capable of processing original constellation distribution without the knowledge of any other parameters. In BIB009 BIB006 , the signals were transformed to the images with topological structure and the CNN algorithm was used to deal with the classification. In BIB007 , the authors explore a new framework for generating additional images in CNN training through using auxiliary classifier generative adversarial networks (ACGAN) for data augmentation to solve the modulation classification problem. Reference BIB008 extended a new semisupervised learning model combined with generative adversarial networks (GANs). The proposed scheme was verified to be a more data-efficient classifier.