A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Modulation recognition <s> Optimizing the coexistence performance of a cognitive radio (CR) greatly depends on the extent of its awareness of the radio environment. The more knowledge a CR can acquire about primary user (PU) systems, the better it can plan its radio activities. Ideally, it would like to classify the PU systems with respect to ‘known standards’ so that the documented knowledge of PU systems can be usefully utilized. Intensive research has been done in the area of signal classification but merely with respect to modulations. We present a novel neuro-fuzzy signal classifier (NFSC) to classify signals with respect to known standards. The NFSC demonstrates excellent performance by using modern A/D based wideband data acquisition as well as traditional heterodyne narrowband transceivers in real-time coexistence environments. <s> BIB001 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Modulation recognition <s> A technique for joint modulation format and bit-rate classification is proposed.The proposed technique can simultaneously enable non-data-aided SNR estimation.The technique uses asynchronous delay-tap plots with artificial neural networks.The signal classification accuracy is 99.12% and mean SNR estimation error is 0.88?dB.Due to its simplicity, it is attractive for future cognitive wireless networks. We propose a novel technique for automatic classification of modulation formats/bit-rates of digitally modulated signals as well as non-data-aided (NDA) estimation of signal-to-noise ratio (SNR) in wireless networks. The proposed technique exploits modulation format, bit-rate, and SNR sensitive features of asynchronous delay-tap plots (ADTPs) for the joint estimation of these parameters. Simulation results validate successful classification of three commonly-used modulation formats at two different bit-rates with an overall accuracy of 99.12%. Similarly, in-service estimation of SNR in the range of 0-30 dB is demonstrated with mean estimation error of 0.88 dB. The proposed technique requires low-speed asynchronous sampling of signal envelope and hence, it can enable simple and cost-effective joint modulation format/bit-rate classification and NDA SNR estimation in future wireless networks. Display Omitted <s> BIB002 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Modulation recognition <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> BIB003 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Modulation recognition <s> A novel algorithm for simultaneous modulation format/bit-rate classification and non-data-aided (NDA) signal-to-noise ratio (SNR) estimation in multipath fading channels by applying deep machine learning-based pattern recognition on signals’ asynchronous delay-tap plots (ADTPs) is proposed. The results for three widely-used modulation formats at two different bit-rates demonstrate classification accuracy of 99.8%. In addition, NDA SNR estimation over a wide range of 0−30 dB is shown with mean error of 1 dB. The proposed method requires low-speed, asynchronous sampling of signal and is thus ideal for low-cost multiparameter estimation under real-world channel conditions. <s> BIB004 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Modulation recognition <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> BIB005 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Modulation recognition <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> BIB006 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Modulation recognition <s> Abstract Recently, deep learning has received a lot of attention in many machine learning applications for its superior classification performance in speech recognition, natural language understanding and image processing. However, it still lacks attention in automatic modulation classification (AMC) until now. Here, we introduce the application of deep learning in AMC. We propose a fully connected 2 layer feed-forward deep neural network (DNN) with layerwise unsupervised pretraining for the classification of digitally modulated signals in various channel conditions. The system uses independent autoencoders (AEs) for feature learning with multiple hidden nodes. Signal information from the received samples is extracted and preprocessed via I and Q components, and formed into training input to 1st AE layer. A probabilistic based method is employed at the output layer to detect the correct modulation signal. Simulation results show that a significant improvement can be achieved compared to the other conventional machine learning methods in the literature. Moreover, we also show that our proposed method can extract the features from cyclic-stationary data samples. A good classification accuracy was achieved, even when the proposed deep network is trained and tested at different SNRs. This shows the future potential of the deep learning model for application to AMC. <s> BIB007 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Modulation recognition <s> In this paper, we propose a low-complexity graphic constellation projection (GCP) algorithm for automatic modulation classification (AMC), where the recovered symbols are projected into artificial graphic constellations. Unlike the existing feature- based (FB) algorithms, we convert the AMC problem into an image recognition problem. Subsequently, the deep belief network (DBN) is adopted to learn the underlying features in these constellations and recognize their corresponding modulation schemes. Simulation results demonstrate that the proposed GCP-DBN based AMC system achieves better performance than other schemes. Specifically, the classification accuracy is beyond 95% at 0 dB, which is very close to the average likelihood ratio test upper bound (ALRT-Upper Bound). <s> BIB008 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Modulation recognition <s> We survey the latest advances in machine learning with deep neural networks by applying them to the task of radio modulation recognition. Results show that radio modulation recognition is not limited by network depth and further work should focus on improving learned synchronization and equalization. Advances in these areas will likely come from novel architectures designed for these tasks or through novel training methods. <s> BIB009 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Modulation recognition <s> The future wireless communication will come up with a strict requirement on high spectral efficiency, developing novel algorithms for spectrum sensing with deep sensing capability will be more challenging. However, traditional expert feature-based spectrum sensing algorithms are lack of sufficient capability of self-learning and adaptability to unknown environments and complex cognitive tasks. To address this problem, we propose to build up a deep learning network to learn short time-frequency transformation (STFT), a basic entity of traditional spectrum sensing algorithms. Spectrum sensing based on the learning to STFT network is supposed to automatically extract features for communication signals and makes decisions for complex cognitive tasks meanwhile. The feasibility and performances of the designed learning network are verified by classifying signal modulation types in deep spectrum sensing applications. <s> BIB010 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Modulation recognition <s> Modulation identification of the transmitted signals remain a challenging area in modern intelligent communication systems like cognitive radios. The computation of the distinct features from input data set and applying machine learning algorithms has been a well-known method in the classification of such signals. However, recently, deep neural networks, a branch of machine learning, have gained significant attention in the pattern recognition of complex data due to its superior performance. Here, we test the application of deep neural networks to the automatic modulation classification in AWGN and flat-fading channel. Three training inputs were used; mainly 1) In-phase and quadrature (I-Q) constellation points, 2) the centroids of constellation points employing the fuzzy C-means algorithm to I-Q diagrams, and 3) the high-order cumulants of received samples. The unsupervised learning from these data sets was done using the sparse autoencoders and a supervised softmax classifier was employed for the classification. The designing parameters for training single and 2-layer sparse autoencoders are proposed and their performance compared with each other. The results show that a very good classification rate is achieved at a low SNR of 0 dB. This shows the potential of the deep learning model for the application of modulation classification. Method for AMC using powerful capability of deep networks.Comparison between a shallow and deep network in the application of AMC.2-layered deep neural network model outperforms other networks.The accuracy of this model converges to results obtained from conventional methods under flat-fading channels. <s> BIB011 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Modulation recognition <s> We conduct an in depth study on the performance of deep learning based radio signal classification for radio communications signals. We consider a rigorous baseline method using higher order moments and strong boosted gradient tree classification, and compare performance between the two approaches across a range of configurations and channel impairments. We consider the effects of carrier frequency offset, symbol rate, and multipath fading in simulation, and conduct over-the-air measurement of radio classification performance in the lab using software radios, and we compare performance and training strategies for both. Finally, we conclude with a discussion of remaining problems, and design considerations for using such techniques. <s> BIB012 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Modulation recognition <s> Automated modulation classification plays a very important part in cognitive radio networks. Deep learning is also a powerful tool that we could not overlook its potential in addressing signal modulation recognition problem. In our last work, we propose a new data conversion algorithm in order to gain a better classification accuracy of communication signal modulation, but we still believe that the convolution neural network (CNN) can work better. However, its application to signal modulation recognition is often hampered by insufficient data and overfitting. Here, we propose a smart approach to programmatic data augmentation method by using the auxiliary classifier generative adversarial networks (ACGANs). The famous CNN model, AlexNet, has been utilized to be the classifier and ACGAN to be the generator, which will enlarge our data set. In order to alleviate the common issues in the traditional generative adversarial nets training, such as discriminator overfitting, generator disconverge, and mode collapse, we apply several training tricks in our training. With the result on original data set as our baseline, we will evaluate our result on enlarged data set to validate the ACGAN’s performance. The result shows that we can gain 0.1~6% increase in the classification accuracy in the ACGAN-based data set. <s> BIB013 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Modulation recognition <s> Deep Learning (DL) is such a powerful tool that we have seen tremendous success in areas such as Computer Vision, Speech Recognition, and Natural Language Processing. Since Automated Modulation Classification (AMC) is an important part in Cognitive Radio Networks, we try to explore its potential in solving signal modulation recognition problem. It cannot be overlooked that DL model is a complex model, thus making them prone to over-fitting. DL model requires many training data to combat with over-fitting, but adding high quality labels to training data manually is not always cheap and accessible, especially in real-time system, which may counter unprecedented data in dataset. Semi-supervised Learning is a way to exploit unlabeled data effectively to reduce over-fitting in DL. In this paper, we extend Generative Adversarial Networks (GANs) to the semi-supervised learning will show it is a method can be used to create a more data-efficient classifier. <s> BIB014 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Modulation recognition <s> Deep learning has recently attracted much attention due to its excellent performance in processing audio, image, and video data. However, few studies are devoted to the field of automatic modulation classification (AMC). It is one of the most well-known research topics in communication signal recognition and remains challenging for traditional methods due to complex disturbance from other sources. This paper proposes a heterogeneous deep model fusion (HDMF) method to solve the problem in a unified framework. The contributions include the following: (1) a convolutional neural network (CNN) and long short-term memory (LSTM) are combined by two different ways without prior knowledge involved; (2) a large database, including eleven types of single-carrier modulation signals with various noises as well as a fading channel, is collected with various signal-to-noise ratios (SNRs) based on a real geographical environment; and (3) experimental results demonstrate that HDMF is very capable of coping with the AMC problem, and achieves much better performance when compared with the independent network. <s> BIB015 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Modulation recognition <s> Advances in software defined radio (SDR) technology allow unprecedented control on the entire processing chain, allowing modification of each functional block as well as sampling the changes in the input waveform. This article describes a method for uniquely identifying a specific radio among nominally similar devices using a combination of SDR sensing capability and machine learning (ML) techniques. The key benefit of this approach is that ML operates on raw I/Q samples and distinguishes devices using only the transmitter hardware-induced signal modifications that serve as a unique signature for a particular device. No higher-level decoding, feature engineering, or protocol knowledge is needed, further mitigating challenges of ID spoofing and coexistence of multiple protocols in a shared spectrum. The contributions of the article are as follows: (i) The operational blocks in a typical wireless communications processing chain are modified in a simulation study to demonstrate RF impairments, which we exploit. (ii) Using an overthe- air dataset compiled from an experimental testbed of SDRs, an optimized deep convolutional neural network architecture is proposed, and results are quantitatively compared with alternate techniques such as support vector machines and logistic regression. (iii) Research challenges for increasing the robustness of the approach, as well as the parallel processing needs for efficient training, are described. Our work demonstrates up to 90-99 percent experimental accuracy at transmitter- receiver distances varying between 2-50 ft over a noisy, multi-path wireless channel. <s> BIB016
Wireless technology recognition Algorithms (i) DNN BIB003 BIB002 BIB004 BIB007 (ii) DBN BIB005 BIB008 (iii) CNN [17, 19-21, 54, 64, 65, 70, 95] (iv) LSTM BIB009 (v) CRBM BIB010 (vi) Autoencoder network BIB006 BIB011 (vii) Generative adversarial networks BIB013 BIB014 (viii) HDMF BIB015  (i) CNN [73-76, 79, 81, 82, 96] (ii) LSTM  (iii) NFSC BIB001 [74], the amplitude and phase difference representation were employed for CNN training procedure. The results indicate that the recognition of radar signals has been realized successfully with the proposed scheme even under the condition of LTE and WLAN signals coexisting at the same time. Reference BIB012 extended the deep CNN model for radio signal classification by heavily tuning deep residual networks based on previous works. A discussion was carried on the robustness of the proposed model under different channel parameters and scales of training sets. Reference BIB016 proposed a radio fingerprinting method, which adopted CNN model and IQ dataset for network training. Their method is capable of learning inherent signatures from different wireless transmitters, which are useful for identifying hardware devices. The results demonstrated that the proposed method outperformed ML methods in the recognition of 5 same hardware devices.