Modulation classification is one of the key tasks for communications systems monitoring, management, and control for addressing technical issues, including spectrum awareness, adaptive transmissions, and interference avoidance. Recently, deep learning (DL)-based modulation classification has attracted significant attention due to its superiority in feature extraction and classification accuracy. In DL-based modulation classification, one major challenge is to preprocess a received signal and represent it in a proper format before feeding the signal into deep neural networks. This article provides a comprehensive survey of the state-of-the-art DL-based modulation classification algorithms, especially the techniques of signal representation and data preprocessing utilized in these algorithms. Since a received signal can be represented by either features, images, sequences, or a combination of them, existing algorithms of DL-based modulation classification can be categorized into four groups and are reviewed accordingly in this article. Furthermore, the advantages as well as disadvantages of each signal representation method are summarized and discussed. 

A Survey of Modulation Classification Using Deep Learning: Signal Representation and Data Preprocessing

The advent of the 5G mobile network has brought a lot of benefits. However, it prompted new challenges on the 5G network cybersecurity defense system, resource management, energy, cache, and mobile network, therefore making the existing approaches obsolete to tackle the new challenges. As a result of that, research studies were conducted to investigate deep learning approaches in solving problems in 5G network and 5G powered Internet of Vehicles (IoVs). In this article, we present a survey on the applications of deep learning algorithms for solving problems in 5G mobile network and 5G powered IoV. The survey pointed out the recent advances on the adoption of deep learning variants in solving the challenges of 5G mobile network and 5G powered IoV. The deep learning algorithm solutions for security, energy, resource management, 5G-enabled IoV, and mobile network in 5G communication systems were presented including several other applications. New comprehensive taxonomies were created, and new comprehensive taxonomies were suggested, analysed, and presented. The challenges of the approaches are already discussed in the literature, and new perspective for solving the challenges was outlined and discussed. We believed that this article can stimulate new interest in practical applications of deep learning in 5G network and provide clear direction for novel approaches to expert researchers. 

Deep Learning-Based Solutions for 5G Network and 5G-Enabled Internet of Vehicles: Advances, Meta-Data Analysis, and Future Direction

Spectrum awareness allows the understanding of the wireless systems environment and it gives engineers and designers better control in systems design and analysis. Phase noise is one of the characteristics of the channel distortion or device distortion, which causes transmission errors. In this paper, a deep learning network is utilized to study and identify different phase noise levels for quadrature phase shift keying (QPSK) signals. Our experiment results show that the deep learning neural network is capable of classifying a wide range of phase noise levels.

Classification of QPSK Signals with Different Phase Noise Levels Using Deep Learning

In the high mobility environment, the channel state information (CSI) in the last part of the packet is different from the beginning part’s actual channel. This phenomenon degrades channel estimation accuracy, and hence it is necessary to be compensated to realize reliable communications. Decision feedback channel estimation (DFCE) has been widely considered as the channel tracking approach. It still causes estimation errors due to the decision-making process in the presence of time and frequency selective fading environments. To address these issues, this paper newly proposes a generalized regression neural network (GRNN) based channel tracking scheme incorporated with frequency-domain CSI smoothing. The latter part is the key to improve the dependability of the training data sets. Computer simulation results confirm that the proposed scheme can achieve superior BER performance and the lower root mean square error (RMSE) value of estimated CSI better than the conventional ones.

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Generalized Regression Neural Network Based Fast Fading Channel Tracking Using Frequency-Domain CSI Smoothing

Currently, deep learning methods with stacking small size convolutional filters are widely used for automatic modulation classification (AMC). In this report, we find some experienced improvements by using large kernel size for convolutional deep convolution neural network based AMC, which is more efficient in extracting multi-scale features of the raw signal I/Q sequence data. Also, Squeeze-and-Excitation (SE) mechanisms can significantly help AMC networks to focus on the more important features of the signal. As a result, we propose a multi-scale feature network with large kernel size and SE mechanism (SE-MSFN) in this paper. SE-MSFN achieves state-of-the-art classification performance on the public well-known RADIOML 2018.01A dataset, with average classification accuracy of 64.50%, surpassing CLDNN by 1.42%, maximum classification accuracy of 98.5%, and an average classification accuracy of 85.53% in the lower SNR range OdB to 10dB, surpassing CLDNN by 2.85%. In addition, we also verified that ensemble learning can help further improve classification performance. We hope this report can provide some references for developers and researchers in practical scenes. 

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Deep Multi-Scale Representation Learning with Attention for Automatic Modulation Classification

Spectrum awareness, including identifying different types of signals, is very important in a cellular system environment. In this paper, a neural network is utilized to identify 5G signals among different cellular communications signals, including Long-Term Evolution (LTE) and Universal Mobile Telecommunication Service (UMTS). We explore the use of deep learning in wireless communications systems. We consider the effects of training dataset size, features extracted, and channel fading in our study. Experiment results demonstrate the effectiveness of deep learning neural networks in identifying cellular system signals, including UMTS, LTE, and 5G.

5G Signal Identification Using Deep Learning

Wireless communications systems design and its performance depend on the wireless fading channels, which are often characterized using a Rician probability function. A Rician K-factor is used to describe the fading severity in a Rician fading channel and is used in the system design and performance evaluation. Therefore, the estimation of the Rician K-factor is important in wireless communications research and development. Traditionally, a Rician K-factor equation, the statistics of the instantaneous frequency of the received signal with a lookup table, or the James-Stein estimator with the maximum likelihood estimation is used for the K-factor estimation. In this paper, we explore the use of deep learning for K-factor estimation. Specifically, we use the convolutional neural network (CNN) to estimate the Rician K-factor from a waveform signal in a Rician channel. Numerical results demonstrate its good performance in estimating the K-factor of the Rician channel.

Rician K-Factor Estimation Using Deep Learning

Radio spectrum awareness, including understanding radio signal activities, is crucial for improving spectrum utilization, detecting security vulnerabilities, and supporting adaptive transmissions. Related tasks include spectrum sensing, identifying systems and terminals, and understanding various protocol layers. In this paper, we investigate various identification and classification tasks related to fading channel parameters, signal distortions, Medium Access Control (MAC) protocols, radio signal types, and cellular systems. Specifically, we utilize deep learning methods in those identification and classification tasks. Performance evaluations demonstrate the effectiveness of deep learning in those radio spectrum awareness tasks.

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Radio spectrum awareness using deep learning: Identification of fading channels, signal distortions, medium access control protocols, and cellular systems

Radio spectrum awareness is an important topic to overcome many challenges, such as spectrum utilization and sharing appearing with the development of technologies in wireless communications. Some practical tasks of radio spectrum awareness are related to signal detection and identification for improving the system’s reliability, efficiency, and security. Eye diagrams are essential for measurement tools used by engineers to simulate, evaluate and debug systems. Eye diagrams reflect many vital parameters for signal integrity degradation, such as timing jitter, crosstalk, and inter-symbol interference. Therefore, using an eye diagram containing a valuable feature from the system could be helpful for spectrum awareness tasks. In this paper, we use deep learning to study and identify classes within quadrature amplitude modulation using eye diagrams and explored related impacts to enable radio spectrum awareness. Our results show that deep learning neural networks capable of classifying quadrature amplitude modulation types with eye diagrams at presenting timing jitter and varying signal-to-noise ratios.

Mohsen H. Alhazmi

Papers

5G Signal Identification Using Deep Learning

Rician K-Factor Estimation Using Deep Learning

Radio spectrum awareness using deep learning: Identification of fading channels, signal distortions, medium access control protocols, and cellular systems

Classification of QPSK Signals with Different Phase Noise Levels Using Deep Learning

QAM Signal Classification and Timing Jitter Identification Based on Eye Diagrams and Deep Learning