Wireless signal recognition plays an important role in cognitive radio, which promises a broad prospect in spectrum monitoring and management with the coming applications for the 5G and Internet of Things networks. Therefore, a great deal of research and exploration on signal recognition has been done and a series of effective schemes has been developed. In this paper, a brief overview of signal recognition approaches is presented. More specifically, classical methods, emerging machine learning, and deep leaning schemes are extended from modulation recognition to wireless technology recognition with the continuous evolution of wireless communication system. In addition, the opening problems and new challenges in practice are discussed. Finally, a conclusion of existing methods and future trends on signal recognition is given.

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A Survey on Deep Learning Techniques in Wireless Signal Recognition

Automatic modulation classification (AMC), which aims to blindly identify the modulation type of an incoming signal at the receiver in wireless communication systems, is a fundamental signal processing technique in the physical layer to improve the spectrum utilization efficiency. Motivated by deep learning (DL) high-impact success in many informatics domains, including radio signal processing for communications, numerous recent AMC methods exploiting deep networks have been proposed to overcome the existing drawbacks of traditional approaches. DL is capable of learning the underlying characteristics of radio signals effectively for modulation pattern recognition, which in turn improves the modulation classification performance under the presence of channel impairments. In this work, we first provide the fundamental concepts of various architectures, such as neural networks, recurrent neural networks, long short-term memory, and convolutional neural networks as the necessary background. We then convey a comprehensive study of DL for AMC in wireless communications, where technical analysis is deliberated in the perspective of state-of-the-art deep architectures. Remarkably, several sophisticated structures and advanced designs of convolutional neural networks are investigated for different data types of sequential radio signals, spectrum images, and constellation images to deal with various channel impairments. Finally, we discuss some primary research challenges and potential future directions in the area of DL for modulation classification.

Automatic Modulation Classification: A Deep Architecture Survey

With the development of the Internet of Things (IoT), the IoT devices are increasing day by day, resulting in increasingly scarce spectrum resources. At the same time, many IoT devices are facing inevitable malicious attacks. The cognitive Radio-enabled IoT (CR-IoT) is proposed as an effective method for spectrum resource allocation and risk monitoring in the IoT. The signal detection and modulation recognition are the key technologies for CR-IoT, addressing the problem of multisignal detection and automatic modulation classification (AMC) is one of the prerequisites for realizing secure dynamic spectrum access. Based on sliding window and deep learning (DL), this study proposes a multisignal frequency domain detection and recognition method. The frequency spectrum of the time-domain overlapping signal is obtained through the fast Fourier transform (FFT), and the frequency spectrum is segmented based on the signal energy detection method. Finally a complex convolutional neural network (CNN) is constructed for the identification of signal spectrum information. The proposed method can recognize 264 time-domain aliasing and frequency-closed signals with an accuracy of 97.3% under the influence of −2 dB corresponding to the noise of the calibration signal. In addition, the proposed method eliminates the influence of bandwidth, which can effectively detect and recognize the signal types of each component in the frequency band. This method has wide applicability and provides an effective scheme for the IoT cognitive technology. 

Multisignal Modulation Classification Using Sliding Window Detection and Complex Convolutional Network in Frequency Domain

Intelligent signal processing for wireless communications is a vital task in modern wireless systems, but it faces new challenges because of network heterogeneity, diverse service requirements, a massive number of connections, and various radio characteristics. Owing to recent advancements in big data and computing technologies, artificial intelligence (AI) has become a useful tool for radio signal processing and has enabled the realization of intelligent radio signal processing . This survey covers four intelligent signal processing topics for the wireless physical layer, including modulation classification, signal detection, beamforming, and channel estimation. In particular, each theme is presented in a dedicated section, starting with the most fundamental principles, followed by a review of up-to-date studies and a summary. To provide the necessary background, we first present a brief overview of AI techniques such as machine learning, deep learning, and federated learning. Finally, we highlight a number of research challenges and future directions in the area of intelligent radio signal processing. We expect this survey to be a good source of information for anyone interested in intelligent radio signal processing, and the perspectives we provide therein will stimulate many more novel ideas and contributions in the future.

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Intelligent Radio Signal Processing: A Survey

This paper proposes to treat the jammer classification problem in the Global Navigation Satellite System bands as a black-and-white image classification problem, based on a time-frequency analysis and image mapping of a jammed signal. The paper also proposes to apply machine learning approaches in order to sort the received signal into six classes, namely five classes when the jammer is present with different jammer types and one class where the jammer is absent. The algorithms based on support vector machines show up to 94.90 % accuracy in classification, and the algorithms based on convolutional neural networks show up to 91.36 % accuracy in classification. The training and test databases generated for these tests are also provided in open access.

https://www.mdpi.com/1424-8220/19/22/4841/pdf

Jammer Classification in GNSS Bands Via Machine Learning Algorithms.

Automatic modulation classification (AMC) lies at the core of cognitive radio and spectrum sensing. In this Letter, the authors propose a novel convolutional neural network (CNN)-based AMC method with multi-feature fusion. First, the modulation signals are transformed into two image representations of cyclic spectra (CS) and constellation diagram (CD), respectively. Then, a two-branch CNN model is developed, a gradient decent strategy is adopted and a multi-feature fusion technique is exploited to integrate the features learned from CS and CD. The proposed method is computationally efficient, benefited from its simple neural network. Experimental results show that the proposed method can achieve identical or better results with much reduced learned parameters and training time, compared with the state-of-the-art deep learning-based methods.

Convolutional neural network and multi-feature fusion for automatic modulation classification

Deep learning architecture has been attracting increasing attention due to the successful applications in various fields. However, its application in radio system has not been well explored. In this paper, we consider the very high frequency (VHF) radio signal modulation classification based on convolution neural networks (CNN). The main principle of CNN is analysed and a five-layer CNN model is built. The proposed CNN-based modulation classification method is proved useful for simulated radio signals generated by MATLAB, that the overall classification accuracy is high even in low SNR. In addition, the proposed CNN-based method is used for real VHF radio signals, and the key factors effecting the classification accuracy

VHF radio signal modulation classification based on convolution neural networks

In such mobile platforms as ships and aircraft, the detection and reconnaissance devices are near to the communication facilities. When working at the same time, they will create interference or jamming themselves. Especially, the high-power radar equipment tend to cause a high bit error rate (BER) in the data link system (DLS), with the result that the DLS cannot operate normally. For this reason, this paper makes a study of the BER performance of the DLS under the pulse linear frequency modulation (LFM) interference in the time-varying Rayleigh channel. In view of the the time-varying characteristics of channel in the practical conditions, the interference channel is modeled as a time-varying Rayleigh fading channel and the formula of BER is derived. Through simulation, the theoretical deduction is proved to be correct. This is of much importance to analyzing the variation of BER performance with the pulse width, power and doppler frequency shift under the pulse LFM interference.

Performance analysis of bit error rate of data link system under pulse LFM interference in time-varying rayleigh channel

Modulation classification is the technological basis of adaptive interference mitigation in communication system. This paper proposes a modulation classification method for very high frequency (VHF) signals, which is based on deep convolutional neural network (CNN) and cyclic spectrum graphs. First, the cyclic spectrum of VHF signals is analyzed. Then, a deep learning method based on CNN is proposed, down-sampling and clipping technologies are used for preprocessing cyclic spectrum images, parameters of the proposed neural network are optimized, and finally the modulation classification is realized. The experimental results show that, the proposed method has high modulation classification accuracy and less computation burden in low SNR.

Modulation Classification of VHF Communication System based on CNN and Cyclic Spectrum Graphs

The invention discloses a linear power amplifier based on analogue cancellation. The amplifier comprises a first coupler, a second coupler, a third coupler, a fourth coupler, a fifth coupler, a sixthcoupler, a seventh coupler, a main power amplifier, an error power amplifier, a first power divider, a second power divider, a first synthesizer, a second synthesizer, a third synthesizer, a first attenuator, a second attenuator, a first delay line, a second delay line, a third delay line, a first vector modulator, a second vector modulator, a third vector modulator, a first correlation detectioncontroller, a second correlation detection controller and a third correlation detection controller. Adaptive ability of a circuit to external environment and circuit parameter variations is kept via dynamic optimization of the correlation detection controllers and the vector modulators, and the optimal and stable linear effect of the power amplifier is achieved.

Hao Wu

Papers

Convolutional neural network and multi-feature fusion for automatic modulation classification

VHF radio signal modulation classification based on convolution neural networks

Performance analysis of bit error rate of data link system under pulse LFM interference in time-varying rayleigh channel

Modulation Classification of VHF Communication System based on CNN and Cyclic Spectrum Graphs

Linear power amplifier based on analogue cancellation