A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Wavelet Transform. <s> There is a need, for example in electronic surveillance, to determine the modulation type of an incoming signal. The use of the wavelet transform for modulation identification of digital signals is described. The wavelet transform can effectively extract the transient characteristics in a digital communication signal, yielding distinct patterns for simple identification. Three identifiers for classifying PSK and FSK, M-ary PSK and M-ary FSK are considered. Statistics for hypothesis testing are derived. When the carrier-to-noise ratio is low, the symbol period and synchronisation time are needed to improve identification accuracy. A method for estimating them from the wavelet transform coefficients is included. The performance of the identifier is investigated through simulations. <s> BIB001 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Wavelet Transform. <s> Automatic modulation identification plays a significant role in electronic warfare, electronic surveillance systems and electronic counter measure. The task of modulation recognition of communication signals is to determine the modulation type and signal parameters. In fact, automatic modulation identification can be range to an application of pattern recognition in communication field. The support vector machines (SVM) is a new universal learning machine which is widely used in the fields of pattern recognition, regression estimation and probability density. In this paper, a new method using wavelet kernel function was proposed, which maps the input vector xi into a high dimensional feature space F. In this feature space F, we can construct the optimal hyperplane that realizes the maximal margin in this space. That is to say, we can use SVM to classify the communication signals into two groups, namely analogue modulated signals and digitally modulated signals. In addition, computer simulation results are given at last, which show good performance of the method. <s> BIB002 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Wavelet Transform. <s> We study the adaptation of convolutional neural networks to the complex-valued temporal radio signal domain. We compare the efficacy of radio modulation classification using naively learned features against using expert feature based methods which are widely used today and e show significant performance improvements. We show that blind temporal learning on large and densely encoded time series using deep convolutional neural networks is viable and a strong candidate approach for this task especially at low signal to noise ratio. <s> BIB003
Compared with the Fourier transform, the wavelet transform (WT) can provide the frequency of the signals and the time associated to those frequencies, which offers refined local signals analysis and low computations. The paper BIB001 proposed a WT modulation classifier without requiring any a priori knowledge. The combination of WT and SVM is a popular scheme in modulation identification. The wavelet kernel function and SVM for modulation recognition is employed in BIB002 . The proposed method is capable of classifying BPSK, QPSK, and AM signals with good accuracy. The paper extended the SVM classifier with key features of WT to recognize 8 kinds of digital modulated signals. In  , the authors use higher-order statistical moments features based on continuous WT and multilayer feedforward neural network for modulation recognition. Taking into account for various distracters including different SNR, fading channels, and frequency shift, the authors in  Wireless Communications and Mobile Computing 7 propose an improved CNN architecture based on BIB003 , in which the pooling layer is employed to retain principal components and reduces the dimension of features. The recognition rate and performance are further enhanced by wavelet denoising technology and appropriate order of the wavelet transformation.