A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Introduction <s> The automatic recognition of the modulation format of a detected signal, the intermediate step between signal detection and demodulation, is a major task of an intelligent receiver, with various civilian and military applications. Obviously, with no knowledge of the transmitted data and many unknown parameters at the receiver, such as the signal power, carrier frequency and phase offsets, timing information and so on, blind identification of the modulation is a difficult task. This becomes even more challenging in real-world scenarios with multipath fading, frequency-selective and time-varying channels. With this in mind, the authors provide a comprehensive survey of different modulation recognition techniques in a systematic way. A unified notation is used to bring in together, under the same umbrella, the vast amount of results and classifiers, developed for different modulations. The two general classes of automatic modulation identification algorithms are discussed in detail, which rely on the likelihood function and features of the received signal, respectively. The contributions of numerous articles are summarised in compact forms. This helps the reader to see the main characteristics of each technique. However, in many cases, the results reported in the literature have been obtained under different conditions. So, we have also simulated some major techniques under the same conditions, which allows a fair comparison among different methodologies. Furthermore, new problems that have appeared as a result of emerging wireless technologies are outlined. Finally, open problems and possible directions for future research are briefly discussed. <s> BIB001 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Introduction <s> The paper presents a new algorithm of digital modulation signals based on high order cumulants (HOC) and support vector machines (SVM). The parameters which are picked up from the signals' fourth order and sixth order cumulants are used as the classification feature vectors. Using SVM based on binary tree as classifiers, recognition of the 2ASK, 4ASK, QPSK, 2FSK and 4FSK signals is efficient. The computer simulation results justify that the success rate is over 97.5% at SNR=10 dB. <s> BIB002 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Introduction <s> A cognitive cellular network, which integrates conventional licensed cellular radio and cognitive radio into a holistic system, is a promising paradigm for the fifth generation mobile communication systems. Understanding the trade-off between energy efficiency, EE, and spectral efficiency, SE, in cognitive cellular networks is of fundamental importance for system design and optimization. This article presents recent research progress on the EE-SE trade-off of cognitive cellular networks. We show how EE-SE trade-off studies can be performed systematically with respect to different architectures, levels of analysis, and capacity metrics. Three representative examples are given to illustrate how EE-SE trade-off analysis can lead to important insights and useful design guidelines for future cognitive cellular networks. <s> BIB003 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Introduction <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> BIB004 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Introduction <s> Recent research and technology trends are shifting toward IoT and CRNs. However, we think that the things-oriented, Internet-oriented, and semantic-oriented versions of IoT are meaningless if IoT objects are not equipped with cognitive radio capability. Equipping IoT objects with CR capability has lead to a new research dimension of CR-based IoT. In this article, we present an overview of CR-based IoT systems. We highlight potential applications of CR-based IoT systems. We survey architectures and frameworks of CR-based IoT systems. We furthermore discuss spectrum-related functionalities for CR-based IoT systems. Finally, we present open issues, research challenges, and future direction for these CR-based IoT networks. <s> BIB005 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Introduction <s> Due to the under-utilization problem of the allocated radio spectrum, cognitive radio (CR) communications have recently emerged as a reliable and effective solution. Among various network models, this survey paper focuses on the enabling techniques for interweave CR networks which have received great attention from standards perspective due to its reliability to achieve the required quality-of-service. Spectrum sensing provides the essential information to enable this interweave communications in which primary and secondary users are not allowed to access the medium concurrently. Several researchers have already considered various aspects to realize efficient techniques for spectrum sensing. In this direction, this survey paper provides a detailed review of the state-of-the-art related to the application of spectrum sensing in CR communications. Starting with the basic principles and the main features of interweave communications, this paper provides a classification of the main approaches based on the radio parameters. Subsequently, we review the existing spectrum sensing works applied to different categories such as narrowband sensing, narrowband spectrum monitoring, wideband sensing, cooperative sensing, practical implementation considerations for various techniques, and the recent standards that rely on the interweave network model. Furthermore, we present the latest advances related to the implementation of the legacy spectrum sensing approaches. Finally, we conclude this survey paper with some suggested open research challenges and future directions for the CR networks in next generation Internet-of-Things applications. <s> BIB006 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Introduction <s> Modulation scheme recognition occupies a crucial position in the civil and military application. In this paper, we present boosting algorithm as an ensemble frame to achieve a higher accuracy than a single classifier. To evaluate the effect of boosting algorithm, eight common communication signals are yet to be identified. And five kinds of entropy are extracted as the training vector. And then, AdaBoost algorithm based on decision tree is utilized to confirm the idea of boosting algorithm. The results illustrate AdaBoost is always a superior classifier, while, as a weak estimator, decision tree is barely satisfactory. In addition, the performance of three diverse boosting members is compared by experiments. Gradient boosting has better behavior than AdaBoost, and xgboost creates optimal cost performance especially. <s> BIB007 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Introduction <s> This Paper reveals the information about Deep Neural Network (DNN) and concept of deep learning in field of natural language processing i.e. machine translation. Now day's DNN is playing major role in machine leaning technics. Recursive recurrent neural network (R2NN) is a best technic for machine learning. It is the combination of recurrent neural network and recursive neural network (such as Recursive auto encoder). This paper presents how to train the recurrent neural network for reordering for source to target language by using Semi-supervised learning methods. Word2vec tool is required to generate word vectors of source language and Auto encoder helps us in reconstruction of the vectors for target language in tree structure. Results of word2vec play an important role in word alignment of the input vectors. RNN structure is very complicated and to train the large data file on word2vec is also a time-consuming task. Hence, a powerful hardware support (GPU) is required. GPU improves the system performance by decreasing training time period. <s> BIB008 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Introduction <s> Deep learning methods employ multiple processing layers to learn hierarchical representations of data, and have produced state-of-the-art results in many domains. Recently, a variety of model designs and methods have blossomed in the context of natural language processing (NLP). In this paper, we review significant deep learning related models and methods that have been employed for numerous NLP tasks and provide a walk-through of their evolution. We also summarize, compare and contrast the various models and put forward a detailed understanding of the past, present and future of deep learning in NLP. <s> BIB009 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Introduction <s> With the development of mobile communication network, the density of networking is getting higher and higher, and there are multiple network coexistence of 2G, 3G, 4G, NB-IoT and so on. The interference problem between networks is more prominent, which seriously reduces the network performance and user perception. Traditional jamming recognition mainly depends on expert experience and artificial judgement. Manual processing cycle is long and efficiency is low. With the increasing number of 5G ultra dense networks, small base stations and even home base stations in the future, how to identify communication network interference quickly and effectively has become an urgent problem to be solved. Therefore, the advantage of deep convolution neural network (deep convolution neural network) to deal with the interference of large data in the communication network is studied, and it is applied to the domain of interference image recognition, and a method based on depth learning for interference image recognition of communication networks is proposed. Based on interference image simplification and jamming image recognition algorithm, a depth model based on interference spectrum recognition is established. The combined learning and overall optimization of the deep convolution neural network greatly improves the efficiency of interference recognition, thus optimizing the quality of the network and improving the user’s perception. <s> BIB010
With the increasing innovation in wireless communication system, numerous wireless terminals and equipment are constantly emerging, which has brought profound changes to our daily life. Unfortunately, the limited spectrum resource can hardly meet the ever-changing demand of the coming 5G BIB003 and Internet of Things (IoT) networks BIB005 , which poses a significant challenge to the spectrum utilization and management. The Federal Communications Commission (FCC) and European Union (EU) authorities have attached high priority to spectrum policy and committed to further improve the performance of spectrum sensing as well as signal recognition algorithms to satisfy the demand of spectrum management. Some concepts including dynamic spectrum access (DSA) and cognitive spectrum-sharing techniques have aroused widespread discussions in academia. However, much work is limited to specific scenarios and poor in adaptability to the different channel conditions and device types. Therefore, new spectrum sensing schemes and novel signal recognition mechanisms have attracted more and more attention, which pave the way to cognitive radio (CR) BIB006 . Wireless signal recognition (WSR) has great promise on military and civilian applications BIB007 , which may include signal reconnaissance and interception, antijamming, and devices identification. Generally, WSR mainly includes modulation recognition (MR) and wireless technology recognition (WTR). MR also known as automatic modulation classification (AMC) is first widely used in military field and later extended to civilian field. MR classifies radio signals by identifying modulation modes, which facilitates to evaluate wireless transmission schemes and device types. What is more, MR is capable of extracting digital baseband information even under the condition of limited prior information. Recently, WTR attracts much attention with the various wireless communication technologies emerging and developing. Wireless technology usually contains various technical standards, not just a single modulation mode. Moreover, different technical standards may adopt the same modulation mode. WTR is able to leverage more comprehensive features to identify technical standards, which has important practical significance in current increasingly tense electromagnetic environment. In addition, the estimation of some parameters, such as frequency, bandwidth, and symbol rate, may also contribute to WSR. Due to the variety and complexity of 2 Wireless Communications and Mobile Computing signal transmission mechanisms, it is difficult to recognize complex signals by only estimating single or few parameters. Therefore, the MR and WTR technologies are focused in this paper. Traditional algorithms of MR could mainly be separated into two groups: likelihood-based (LB) and feature-based (FB) approaches BIB001 . LB approaches are based on hypothesis testing theory; the performance based on decisiontheoretic is optimal but suffers high computation complexity. Therefore, feature-based approaches as suboptimal classifiers were developed for application in practice. In particular, the feature-based approaches usually extracted features for the preprocessing and then employed classifiers to realized modulation classification. Conventional FB approaches heavily rely on the expert's knowledge, which may perform well on specialized solutions but poor in generality and suffer high complexity and time-consuming. To tackle these problems, machine learning (ML) classifiers have been adopted and shown great advantages, for example, support vector machine (SVM) in BIB002 . Although ML methods have the advantage of classification efficiency and performance, the feature engineering to some extent still depends on expert experience, resulting in degradation of accuracy rate. Therefore, the self-learning ability is very important when confronted with unknown environment. A new dawn seems to have arrived, since the DL performs very well in computer vision (CV)  , machine translation (MT) BIB008 , and natural language processing (NLP) BIB009 . DL architecture has also been introduced to MR, for instance; the convolutional neural networks (CNN) model is employed in modulation classification without expert feature extraction BIB004 , which demonstrates excellent performance both on efficiency and accuracy. On the other hand, current communication systems tend to be complex and diverse, various new wireless technologies are constantly updated, and the coexistence of homogeneous and heterogeneous signals is entering a new norm. As a result, the detection and recognition of complex signals will be confronted with new dilemma, and the methods for signal recognition are needed to keep up with the pace. Fortunately, some literature on WTR has emerged, in which ML and DL model with explicit features have also been done to realize the recognition of specific wireless technologies. Meanwhile, some emerging applications are been explored, such as base station detection, interfere signals recognition, and localization in mobile communication network BIB010 . In this paper, the need of deep learning in signal recognition is reviewed in Section 2. Section 3 introduces the modulation recognition with various approaches. In Section 4, wireless technology recognition is presented. Opening problems are discussed in Section 5. Section 6 concludes the paper.