A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Advantage of DL Applied in Wireless Signal <s> This letter proposes a novel modulation recognition algorithm for very high frequency (VHF) radio signals, which is based on antinoise processing and deep sparse-filtering convolutional neural network (AN-SF-CNN). First, the cyclic spectra of modulated signals are calculated, and then, low-rank representation is performed on cyclic spectra to reduce disturbances existed in VHF radio signals. After that, before fine tuning the CNN, we propose a sparse-filtering criterion to unsupervised pretrain the network layer-by-layer, which improves generalization effectively. Several experiments are taken on seven kinds of modulated signals, and the simulation results show that, compared with the traditional methods and some renowned deep learning methods, the proposed method can achieve higher or equivalent classification accuracy, and presents robustness against noises. <s> BIB001 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Advantage of DL Applied in Wireless Signal <s> Modulation recognition is a challenging task while performing spectrum sensing in cognitive radio. Recently, deep learning techniques, such as convolutional neural networks (CNNs) have been shown to achieve state-of-the-art accuracy for modulation recognition. However, CNNs are not explicitly designed to undo distortions caused by wireless channels. To improve the accuracy of CNN-based modulation recognition schemes, we propose a signal distortion correction module (CM). The proposed CM is also based on a neural network that can be thought of as an estimator of carrier frequency and phase offset introduced by the channel. The CM output is used to shift the signal frequency and phase before modulation recognition and is differentiable with respect to its weights. This allows the CM to be co-trained end-to-end in tandem with the CNN used for modulation recognition. For supervision, only the modulation scheme label is used and the knowledge of true frequency or phase offset is not required for co-training the combined network (CM+CNN). <s> BIB002 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Advantage of DL Applied in Wireless Signal <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> BIB003
Recognition. The perfect combination of DL and signal recognition possesses notable superiority. On the one hand, large-scale data is essential for the training process in DL model, which is accessible to get for various communication pieces of equipment in daily life. On the other hand, feature engineering can be left out in DL architecture, which is an indispensable part for conventional recognition schemes. Feature selection from the received signal is usually difficult in practical applications. For example, the prior information is essential in the estimation of many parameters, which may be impractical or inaccurate BIB002 . Although some FB approaches perform well in certain solutions, the feature selection may suffer high complexity and time-consuming, so the feature self-learning model is of great significance for realistic scenarios to free from expert experience BIB001 . In addition, with the further improvement of DL algorithms and theory research, more application prospects will be excavated for signal recognition in future communications systems BIB003 .