A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> S Transform. <s> Examining the frequency content of signals is critical in many applications, from neuroscience to astronomy. Many techniques have been proposed to accomplish this. One of these, the S-transform, provides simultaneous time and frequency information similar to the wavelet transform, but uses sinusoidal basis functions to produce frequency and globally referenced phase measurements. It has shown promise in many medical imaging applications but has high computational requirements. This paper presents a general transform that describes Fourier-family transforms, including the Fourier, short-time Fourier, and S- transforms. A discrete, nonredundant formulation of this transform, as well as algorithms for calculating the forward and inverse transforms are also developed. These utilize efficient sampling of the time-frequency plane and have the same computational complexity as the fast Fourier transform. When configured appropriately, this new algorithm samples the continuous S-transform spectrum efficiently and nonredundantly, allowing signals to be transformed in milliseconds rather than days, as compared to the original S-transform algorithm. The new and efficient algorithms make practical many existing signal and image processing techniques, both in biomedical and other applications. <s> BIB001 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> S Transform. <s> Automatic Modulation Classification plays a significant role in Cognitive Radio to identify the modulation format of the primary user. In this paper, we present the Stockwell transform (S-transform) based features extraction for classification of different digital modulation schemes using different classifiers such as Neural Network (NN), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Naive Bayes (NB), k-Nearest Neighbor (k-NN). The S — transform provides time-frequency or spatial-frequency localization of a signal. This property of S-transform gives good discriminant features for different modulation schemes. Two simple features i.e., energy and entropy are used for classification. Different modulation schemes i.e., BPSK, QPSK, FSK and MSK are used for classification. The results are compared with wavelet transform based features using probability of correct classification, performance matrix including classification accuracy and computational complexity (time) for SNR range varying from 0 to 20 dB. Based upon the results, we found that S-transform based features outperform wavelet transform based features with better classification accuracy and less computational complexity. <s> BIB002
S transform (ST) has unique advantages in possessing both the phase and frequency estimations of the FT and the super-resolution of the WT BIB001 . Early application of the ST was in electrical power networks with power quality analysis. In BIB002 , ST based features extraction for digital modulation classification was presented. A comparison with the WT was given by employing different machine learning classifiers.