A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Cyclostationarity. <s> Channel sensing and spectrum allocation has long been of interest as a prospective addition to cognitive radios for wireless communications systems occupying license-free bands. Conventional approaches to cyclic spectral analysis have been proposed as a method for classifying signals for applications where the carrier frequency and bandwidths are unknown, but is, however, computationally complex and requires a significant amount of observation time for adequate performance. Neural networks have been used for signal classification, but only for situations where the baseband signal is present. By combining these techniques a more efficient and reliable classifier can be developed where a significant amount of processing is performed offline, thus reducing online computation. In this paper we take a renewed look at signal classification using spectral coherence and neural networks, the performance of which is characterized by Monte Carlo simulations <s> BIB001 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Cyclostationarity. <s> In this paper, a concise survey of the literature on cyclostationarity is presented and includes an extensive bibliography. The literature in all languages, in which a substantial amount of research has been published, is included. Seminal contributions are identified as such. Citations are classified into 22 categories and listed in chronological order. Both stochastic and nonstochastic approaches for signal analysis are treated. In the former, which is the classical one, signals are modelled as realizations of stochastic processes. In the latter, signals are modelled as single functions of time and statistical functions are defined through infinite-time averages instead of ensemble averages. Applications of cyclostationarity in communications, signal processing, and many other research areas are considered. <s> BIB002 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Cyclostationarity. <s> Spectrum awareness is currently one of the most challenging problems in cognitive radio (CR) design. Detection and classification of very low SNR signals with relaxed information on the signal parameters being detected is critical for proper CR functionality as it enables the CR to react and adapt to the changes in its radio environment. In this work, the cycle frequency domain profile (CDP) is used for signal detection and preprocessing for signal classification. Signal features are extracted from CDP using a threshold-test method. For classification, a Hidden Markov Model (HMM) has been used to process extracted signal features due to its robust pattern-matching capability. We also investigate the effects of varied observation length on signal detection and classification. It is found that the CDP-based detector and the HMM-based classifier can detect and classify incoming signals at a range of low SNRs. <s> BIB003 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Cyclostationarity. <s> We define a cyclostationary signature as a feature which may be intentionally embedded in a digital communications signal, detected through cyclostationary analysis and used as a unique identifier. The purpose of this paper is to demonstrate how cyclostationary signatures can be exploited to overcome a number of the challenges associated with network coordination in emerging cognitive radio applications and spectrum sharing regimes. In particular we show their uses for signal detection, network identification and rendezvous and discuss these in the context of dynamic spectrum access. We present a theoretical discussion followed by application-oriented examples of the cyclostationary signatures used in practical cognitive radio and dynamic spectrum usage scenarios. We focus on orthogonal frequency division multiplexing (OFDM) based systems and present an analysis of a transceiver implementation employing these techniques developed on a cognitive radio test platform. <s> BIB004 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Cyclostationarity. <s> This paper addresses the problem of automatic modulation recognition of digital signals. A classification method based on spectral correlation and Support Vector Machine (SVM) is developed. The spectral correlation theory is introduced and several characteristic parameters which can be used for modulation analysis are extracted. The parameters are used as the input feature vectors to SVM. SVM maps the vectors into a high dimensional feature space, so the problem of non-separable classification in low dimension is resolved and the decision threshold become unnecessary. The experiment results show that the algorithm is robust with high accuracy even at low SNR. <s> BIB005 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Cyclostationarity. <s> Spectrum sensing is one of the most challenging functions in cognitive radio system. Detection of the presence of signals and distinction of the type of signals in a particular frequency band are critical for cognitive radios to adapt to the radio environment. In this paper, a novel approach to signal classification combining spectral correlation analysis and support vector machine (SVM) is introduced. Four spectral coherence characteristic parameters are chosen via spectral correlation analysis. By utilizing a nonlinear SVM, a significant amount of calculation is performed offline, thus the computational complexity is reduced. Simulations indicate that the overall success rate is above 92.8% with data length of 1000 when SNR is equal to 4 dB. Compared to the existing methods including the classifiers based on binary decision tree (BDT) and multilayer linear perceptron network (MLPN), the proposed approach is more effective in the case of low SNR and limited training numbers. <s> BIB006 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Cyclostationarity. <s> Deep neural network (DNN) has recently received much attention due to its superior performance in classifying data with complex structure. In this paper, we investigate application of DNN technique to automatic classification of modulation classes for digitally modulated signals. First, we select twenty one statistical features which exhibit good separation in empirical distributions for all modulation formats considered (i.e., BPSK, QPSK, 8PSK, 16QAM, and 64QAM). These features are extracted from the received signal samples and used as the input to the fully connected DNN with three hidden layer. The training data containing 25,000 feature vectors is generated by the computer simulation under both additive Gaussian white noise (AWGN) and Rician fading channels. Our test results show that the proposed method brings dramatic performance improvement over the existing classifier especially for high Doppler fading channels. <s> BIB007 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Cyclostationarity. <s> Automated Modulation Classification (AMC) has been applied in various emerging areas such as cognitive radio (CR). In our paper, we propose a deep learning-based AMC method that employs Spectral Correlation Function (SCF). In our proposed method, one deep learning technology, Deep Belief Network (DBN), is applied for pattern recognition and classification. By using noise-resilient SCF signatures and DBN that is effective in learning complex patterns, we achieve high accuracy in modulation detection and classification even in the presence of environment noise. Our simulation results illustrate the efficiency of our proposed method in classifying 4FSK, 16QAM, BPSK, QPSK, and OFDM modulation techniques in various environments. <s> BIB008 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Cyclostationarity. <s> Modulation identification shows great significance for any receiver that has little knowledge of the modulation scheme of the received signal. In this paper, we compare the performance of a deep autoencoder network and three shallow algorithms including SVM, Naive Bayes and BP neural network in the field of communication signal modulation recognition. Firstly, cyclic spectrum is used to pre-process the simulation communication signals, which are at various SNR (from −10dB to 10dB). Then, a deep autoencoder network is established to approximate the internal properties from great amount of data. A softmax regression model is used as a classifier to identify the five typical communication signals, which are FSK, PSK, ASK, MSK, QAM. The results for the experiment illustrate the excellent classification performance of the networks. At last, we discuss the comparison of these methods and three traditional shallow machine learning models. <s> BIB009 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Cyclostationarity. <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> BIB010
A cyclostationary process is a signal whose statistical characteristics change periodically with time BIB002 . Cyclostationarity is an inherent physical property of most signals, which is robust against noise and interference  . Spectral correlation function (SCF) is usually used to examine and analyze the cyclostationarity of the signal BIB004 and can be defined as where is the complex envelope value of ( ) corresponding to the frequency V and the bandwidth Δ and represents cyclic frequency and Δ is the measurement interval. As a continuation, the spectral correlation function of digitally modulated signals was provided in BIB001 . Hidden Markov Model (HMM) was employed to deal with the features extracted from the cycle frequency domain profile in BIB003 . Simulations show that the presented model was able to classify the signals at a range of low SNRs. Another classification scheme with spectral correlation feature and SVM classifier is developed in BIB005 . The results demonstrate that the algorithm is robust under the condition of low SNR and maintain high accuracy. In BIB006 , four features based on spectral correlation were selected for SVM classifier. The proposed method performs more efficiently in low SNR and limited training data. Deep learning methods employed spectral correlation function features have been applied to signal classification. In BIB007 , the method using 21 features of baseband signal and a full-connected DNN model is proposed to recognize five modulation mechanisms. The simulations verify that the proposed algorithm outperforms the ANN classifier under various channel conditions. Deep Belief Network (DBN) is introduced to pattern recognition tasks in BIB008 . With spectral correlation function (SCF) signatures, the DBN classifier achieves great success in the field of modulation classification in various environments. In BIB009 , cyclic spectrum statistics are also adopted in the signal preprocessing and a stacked sparse autoencoder and softmax regression with DL model are employed to recognize communication signals. The comparison of the proposed methods and several traditional machine learning schemes was analyzed by simulations. Another modulation classification method for very high frequency (VHF) signal is presented in BIB010 . The received signals are transformed into cyclic spectrum; then the denoised spectrum images are fed into CNN model to self-learn the inner features and train classifier to recognize modulation formats finally.