A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Moments and Cumulants. <s> In this paper we investigate automatic modulation classification (AMC) using homogeneous feature-vectors based on cyclic cumulants (CCs) of fourth-, sixth- and eight-orders, respectively, for QAM, PSK and ASK signals within a pattern recognition framework. Analysis of CCs of the baseband signal at the receiver is performed and used for feature selection. The cycle spectrum of the baseband signal at the receiver is derived as a function of excess bandwidth for a raised cosine pulse shape and a necessary and sufficient condition on the oversampling factor is obtained. Theoretical arguments regarding the discrimination capability of the examined feature-vectors are verified through extensive simulations. <s> BIB001 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Moments and Cumulants. <s> Automatic recognition of digital modulation signals has seen increasing demand nowadays. The use of artificial neural networks for this purpose has been popular since the late 1990s. Here, we include a variety of modulation types for recognition, e.g. QAM16, V29, V32, QAM64 through the addition of a newly proposed statistical feature set. Two training algorithms for multi-layer perceptron (MLP) recogniser, namely Backpropagation with Momentum and Adaptive Learning Rate is investigated, while resilient backpropagation (RPROP) is proposed for this problem, are employed in this work. In particular, the RPROP algorithm is applied for the first time in this area. In conjunction with these algorithms, we use a separate data set as validation set during training cycle to improve generalisation. Genetic algorithm (GA) based feature selection is used to select the best feature subset from the combined statistical and spectral feature set. RPROP MLP recogniser achieves about 99% recognition performance on most SNR values with only six features selected using GA. <s> BIB002 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Moments and Cumulants. <s> This paper presents a method based on Higher-order cumulants for classifying modulation type of PSK, ASK and QAM signals. The method is robust to the presence of frequency offset. The high performance and robustness of the method are proved by computer simulation. <s> BIB003 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Moments and Cumulants. <s> Nowadays, automatic modulation classification (AMC) plays an important role in both cooperative and non-cooperative communication applications. Very often, multipath fading channels result in the severe AMC performance degradation or induce large classification errors. The negative impacts of multipath fading channels on AMC have been discussed in the existing literature but no solution has ever been proposed so far to the best of our knowledge. In this paper, we propose a new robust AMC algorithm, which applies higher-order statistics (HOS) in a generic framework for blind channel estimation and pattern recognition. We also derive the Cramer-Rao lower bound for the fourth-order cumulant estimator when the AMC candidates are BPSK and QPSK over the additive white Gaussian noise channel, and it is a nearly minimum-variance estimator leading to robust AMC features in a wide variety of signal-to-noise ratios. The advantage of our new algorithm is that, by carefully designing the essential features needed for AMC, we do not really have to acquire the complete channel information and therefore it can be feasible without any a priori information in practice. The Monte Carlo simulation results show that our new AMC algorithm can achieve the much better classification accuracy than the existing AMC techniques. <s> BIB004 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Moments and Cumulants. <s> Adaptive modulation schemes have been proposed to optimize Shannon's channel capacity in recent orthogonal frequency division multiplexing (OFDM) based broadband wireless standard proposals. By adapting the modulation type (effectively changing the number of bits per symbol) at the transmitter end one can improve the bit error rate (BER) during transmission at designated SNR. Blind detection of the transmitted modulation type is desirable to optimise the bandwidth available at the receivers. Hence, there is a need for an intelligent modulation classification engine at the receiver end. In this work, we evaluate some higher order statistical measures coupled with a classical Naive Bayes classifier for fast identification of adaptive modulation schemes. We also benchmark the experimental results with the optimal Maximum Likelihood Classifier, and Support Vector Machine based Classifier using the same feature set. <s> BIB005 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Moments and Cumulants. <s> An automatic modulation classification algorithm for application in communications via multipath fading channels, without a priori information on channel characteristics, based on normalized sixth-order cumulants is described. In comparison with existing approaches, proposed algorithm can achieve much better classification accuracy in distinguishing BPSK from complex-valued modulation techniques. Theoretical analysis is verified via extensive simulations, under real-world channel propagation conditions. <s> BIB006 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Moments and Cumulants. <s> In this paper we consider the problem of Radar Specific Emitter Identification (SEI) with the aim of distinguishing among several transmitting sources of the same kind which is a very hot topic in the device forensic field. At the design stage, we introduce a classification technique based on some suitable features evaluated from the cumulants of the signal emitted by the radar system. The devised features share some invariance properties which make them very attractive for the SEI problem. Hence, we use them as the input to a K-Nearest Neighbor (KNN) classifier which performs the assignment of the emitter to a specific class. At the analysis stage, we assess the performance of the new system on a real dataset containing radar signals from three identical airborne emitters. The results highlight that a satisfactory classification performance is achieved. <s> BIB007 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Moments and Cumulants. <s> In this paper, a new automatic modulation classification system using a polynomial classifier is proposed. The feature set used in the proposed system is comprised of second, fourth and sixth higher order cumulants of the received signal. The polynomial classifier expands the feature vector into a higher dimensional space in which the different modulation types are linearly separable. The performance of the proposed system is tested against BPSK, QPSK, 8-PSK, 16-QAM and 64-QAM constellations. Results showed a relatively higher classification rate for all types of modulations compared to previously published results using fourth and sixth order cumulants with other classification schemes. Moreover, the proposed classification scheme has low computational complexity and is more suited for real time applications. <s> BIB008 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Moments and Cumulants. <s> Automatic modulation classification (AMC) is a classical topic in the signal classification field and is often performed when the modulation type is adaptive. For typical modulation types such as M-PSK and M-QAM, the fourth-order cumulant statistics are usually used for modulation classification. Besides, it is also known that the AMC performance can be seriously degraded by the effects of multipath fading in wireless channels when compared with the ideal white Gaussian noise channel. In this study, a new modulation classification method is proposed, exploring different features of normalised fourth-order cumulant statistics for a modified blind channel estimation method in the multipath fading channel. The relationship between the cumulants of the received signal and the multipath fading effects is established to cope with the channel impulse response in the new algorithm. Simulation results show that the performance of the proposed AMC method is much improved than that of previously proposed ones in terms of the probability of correct classification. <s> BIB009 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Moments and Cumulants. <s> In this paper, a Hierarchical Polynomial (HP) classifier is proposed to automatically classify M-PSK and M-QAM signals in Additive White Gaussian Noise (AWGN) and slow flat fading environments. The system uses higher order cumulants (HOCs) of the received signal to distinguish between the different modulation types. The proposed system divides the overall modulation classification problem into several hierarchical binary sub-classifications. In each binary sub-classification, the HOCs are expanded into a higher dimensional space in which the two classes are linearly separable. It is shown that there is a significant improvement when using the proposed Hierarchical polynomial structure compared to the conventional polynomial classifier. Moreover, simulation results are shown for different block lengths (number of received symbols) and at different SNR values. The proposed system showed an overall improvement in the probability of correct classification that reaches 100% using only 512 received symbols at 20 dB compared to 98% and 98.33% when using more complicated systems like Genetic Programming with KNN classifier (GP-KNN) and Support Vector Machines (SVM) classifiers, respectively. <s> BIB010
In mathematics, moments are employed to describe the probability distribution of a function. The p-th order and q-th conjugations moment BIB009 for a received signal ( ) can be given as where E[•] is expectation operator and (•) * is complex conjugate. Although moments have been widely used in the field of signal process for the advanced ability on noise suppression, most practical applications tend to cumulants for its superiority on non-Gaussian random processes. In statistical theory, cumulants are made up of moments and can be regarded as an alternative to the moments. Commonly used cumulants are expressed in Table 3 . Cumulants are often employed for modulation classification to against the carrier offsets and non-Gaussian noise. In BIB004 , fourth-order cumulants were utilized in the task of signal classification. Higher-order cumulants (HOC) are conducive to further enhance the performance and extend the range of recognition signals. In BIB006 , sixth-order cumulants show significantly performance improvement than the fourth-order cumulants. In BIB003 , eighth-order cumulants are employed to distinguish between 8PSK and 16PSK signal. Additionally, higher-order cyclic cumulants are developed to further expand the signal types including 256-QAM in BIB001 . Some methods combined cumulants with machine learning for modulation classification have been proposed in recent works, such as ANN, K-Nearest Neighbor (KNN), SVM, Naïve Bayes (NB), and polynomial. To improve the performance, two updated ANN training algorithms are presented in BIB002 . KNN is attractive due to its easy implement. In BIB007 , the authors propose a KNN classifier using cumulantsbased features of the intercepted signal. Satisfactory performance was verified by numerous simulations. SVM has great potential on the recognition of small-scale and high dimensional dataset. Another interesting scheme combined HOC with SVM is presented for digital modulation classification in NB model originates from classical mathematical theory and the major advantages lie in simplicity and less sensitive to missing data. In BIB005 , the authors proposed a new scheme combined HOC with NB classifier. Simulations indicated that the NB classifier was superior to both Maximum Likelihood and SVM model on computational complexity and close to SVM on performance. To further reduce the complexity, a novel AMC system based on polynomial classifier was presented in BIB008 . Second, fourth and sixth HOC were utilized to classify M-PSK and M-QAM signals with low computational complexity. In addition, a comprehensive comparison among KNN, SVM, NB, and the proposed polynomial classifier combined with extraction of higher order cumulants features is carried out in BIB010 . The proposed method achieves good tradeoff in terms of accuracy and structure simplicity.