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Journal articles on the topic 'Automatic modulation'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

M.sabbar, Bayan, and Hussein A. Rasool. "AUTOMATIC MODULATION CLASSIFIER: REVIEW." Iraqi Journal of Information & Communications Technology 3, no. 4 (2020): 11–32. http://dx.doi.org/10.31987/ijict.3.4.111.

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The automatic modulation classification (AMC) is highly important to develop intelligent receivers in different military and civilian applications including signal intelligence, spectrum management, surveillance, signal confirmation, monitoring, interference identification, as well as counter channel jamming. Clearly, without knowing much information related to transmitted data and various indefinite parameters at receiver, like timing information, carrier frequency, signal power, phase offsets, and so on, the modulation’s blind identification has been a hard task in the real world situations

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2

Rong, Li. "Automatic Recognition of M-Nary Digital Modulation Signals." Applied Mechanics and Materials 336-338 (July 2013): 1665–69. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.1665.

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For the using of multi-modulation, the precondition of receiving and demodulating signal is to decide the type of the modulation. So automatic recognition of modulation signal has significant influences on the analysis of communication signals. In this paper, nine types of M-nary digital modulations are recognized by using four effective key features and utilizing the decision-theoretic approachThe simulation results shows that overall success rate is over 99% at SNR4dBThis algorithm is verified its good performance. It has simple structure, less calculation and good performance of real time.

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3

Ge, Qian, Qian Wang, Xiao Yan, and Ling He. "Algorithms for Automatic Modulation Recognition in Wireless Monitoring Applications." Applied Mechanics and Materials 241-244 (December 2012): 1772–78. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.1772.

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The paper proposes an automatic modulation recognition scheme based on instantaneous features of intercepted signals. The modulation classifier can discriminate modulations such as Amplitude Modulation (AM), Double Side Band (DSB), Single Side Band (SSB), Frequency Modulation (FM), M-ary Amplitude Shift Keying (M-ASK), M-ary Frequency Shift Keying (M-FSK), M-ary Phase Shift Keying (M-PSK) and M-ary Quadrature Amplitude Modulation (M-QAM) without any prior information. The scheme is with simple structure, computationally simpler, and suitable for real-time processing. And the recognition parame

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4

Luţă, Alexandru-Daniel, and Paul Bechet. "An Algorithm for Automatic Recogniton of Digital QAM Modulations." International conference KNOWLEDGE-BASED ORGANIZATION 25, no. 3 (2019): 36–41. http://dx.doi.org/10.2478/kbo-2019-0114.

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Abstract This paper proposes a new Matlab-developed algorithm for automatic recognition of digital modulations using the constellation of states. Using this technique the automatic distinction between four digital modulation schemes (8-QAM, 16-QAM, 32-QAM and 64-QAM) was made. It has been seen that the efficiency of the algorithm is influenced by the type of modulation, the value of the signal-to-noise ratio and the number of samples. In the case of an AWGN noise channel the simulation results indicated that the value of SNR (signal-to-noise ratio) has a small influence on the recognition rate

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5

Jafar, Norolahi, Azmi Paeiz, and Ahmadi Farzaneh. "Automatic modulation classification using modulation fingerprint extraction." Journal of Systems Engineering and Electronics 32, no. 4 (2021): 799–810. http://dx.doi.org/10.23919/jsee.2021.000069.

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6

Azzouz, E. E., and A. K. Nandi. "Automatic modulation recognition—I." Journal of the Franklin Institute 334, no. 2 (1997): 241–73. http://dx.doi.org/10.1016/s0016-0032(96)00069-5.

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7

Azzouz, E. E., and A. K. Nandi. "Automatic modulation recognition—II." Journal of the Franklin Institute 334, no. 2 (1997): 275–305. http://dx.doi.org/10.1016/s0016-0032(96)00070-1.

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8

Nandi, A. K., and E. E. Azzouz. "Automatic analogue modulation recognition." Signal Processing 46, no. 2 (1995): 211–22. http://dx.doi.org/10.1016/0165-1684(95)00083-p.

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9

Abdulkarem, Ahmed Mohammed, Firas Abedi, Hayder M. A. Ghanimi, et al. "Robust Automatic Modulation Classification Using Convolutional Deep Neural Network Based on Scalogram Information." Computers 11, no. 11 (2022): 162. http://dx.doi.org/10.3390/computers11110162.

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This study proposed a two-stage method, which combines a convolutional neural network (CNN) with the continuous wavelet transform (CWT) for multiclass modulation classification. The modulation signals’ time-frequency information was first extracted using CWT as a data source. The convolutional neural network was fed input from 2D pictures. The second step included feeding the proposed algorithm the 2D time-frequency information it had obtained in order to classify the different kinds of modulations. Six different types of modulations, including amplitude-shift keying (ASK), phase-shift keying

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10

Zhang, Zifeng. "Deep learning-based Automatic Modulation Recognition: a comprehensive study." Advances in Engineering Innovation 16, no. 5 (2025): 69–77. https://doi.org/10.54254/2977-3903/2025.23161.

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Automatic modulation recognition plays a critical role in both civilian and military communication systems. While traditional approaches rely on manual feature extraction with limited accuracy, deep learning methods offer promising alternatives for this pattern recognition task. This paper presents a systematic performance evaluation of classical deep learning models for automatic modulation classification, aiming to establish baseline references for future research. Through comparative experiments using the RadioML2018.01a dataset containing 24 modulation types across SNR levels from -20dB to

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11

Kurbanaliev, V. C., and Yu N. Gorbunov. "AUTOMATIC MODULATION CLASSIFICATION: CUMULANT APPROACH." Bulletin of Russian academy of natural sciences 23, no. 1 (2023): 24–32. http://dx.doi.org/10.52531/1682-1696-2023-23-1-24-32.

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The article describes the apparatus for determining the types of modulation based on cumulants. A new approach based on cumulants up to the eighth order is considered. The rationale for the use of the cumulant method is given. The results of the application of artificial neural networks in the task of automating the detection of signs of intrapulse modulation for the identification (classification) of signals are presented. A mathematical model of characteristic types of modulation used in radar and communications has been developed. The main properties of each type of signals are described an

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12

Liedtke, Ferdinand. "Adaptive procedure for automatic modulation recognition." Journal of Telecommunications and Information Technology, no. 4 (December 30, 2004): 91–97. http://dx.doi.org/10.26636/jtit.2004.4.259.

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An adaptive procedure for automatic modulation recognition is described. With it the automatic modulation classification and recognition of radio communication signals with a priori unknown parameters is possible effectively. The results of modulation recognition are important in the context of radio monitoring or electronic support measurements. The special features of the procedure are the possibility to adapt it dynamically to nearly all modulation types, and the capability to recognize continuous phase modulation (CPM) signals like Gaussian minimum-shift keying (GMSK) too. A time synchroni

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13

Leblebici, Merih, Ali Çalhan, and Murtaza Cicioğlu. "CNN-based automatic modulation recognition for index modulation systems." Expert Systems with Applications 240 (April 2024): 122665. http://dx.doi.org/10.1016/j.eswa.2023.122665.

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14

Xu, Yichen, Liang Huang, Linghong Zhang, Liping Qian, and Xiaoniu Yang. "Diffusion-Based Radio Signal Augmentation for Automatic Modulation Classification." Electronics 13, no. 11 (2024): 2063. http://dx.doi.org/10.3390/electronics13112063.

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Deep learning has become a powerful tool for automatically classifying modulations in received radio signals, a task traditionally reliant on manual expertise. However, the effectiveness of deep learning models hinges on the availability of substantial data. Limited training data often results in overfitting, which significantly impacts classification accuracy. Traditional signal augmentation methods like rotation and flipping have been employed to mitigate this issue, but their effectiveness in enriching datasets is somewhat limited. This paper introduces the Diffusion-based Radio Signal Augm

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15

HULA, Iho, and Oleksiy POLIKAROVSKYKH. "METHOD OF DETERMINING PARAMETERS OF MODULATION OF UAV SIGNALS USING ARTIFICIAL NEURAL NETWORKS." Herald of Khmelnytskyi National University. Technical sciences 315, no. 6 (2022): 47–54. http://dx.doi.org/10.31891/2307-5732-2022-315-6(2)-47-54.

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The article is devoted to the consideration of the issue of determining the parameters of the modulation of signals of unmanned aerial vehicles using artificial neural networks by recognizing the types of digital modulation and is performed by a system that automatically classifies the type of digital modulation of the received signal. Recognition of digital modulation types is used, which automatically classifies the type of digital modulation of the received signal. The following issues are covered in the article: the analysis of existing approaches in the task of automatic recognition of ty

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16

Ma, Pengfei, Yuesen Liu, Lin Li, Zhigang Zhu, and Bin Li. "A Robust Constellation Diagram Representation for Communication Signal and Automatic Modulation Classification." Electronics 12, no. 4 (2023): 920. http://dx.doi.org/10.3390/electronics12040920.

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Automatic modulation recognition is a necessary part of cooperative and noncooperative communication systems and plays an important role in military and civilian fields. Although the constellation diagram (CD) is an essential feature for different digital modulations, it is hard to be extracted under noncooperative complex communication environment. Frequency offset, especially the nonlinear frequency offset is a vital problem of complex communication environment, which greatly affects the extraction of traditional CD and the performance of modulation recognition methods. In the current paper,

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17

HULA, IHOR, and OLEKSIY POLIKAROVSKYKH. "STUDY NEUR NETWORKS FOR SOFTWARE DEFINED RADIO CONTROL." Herald of Khmelnytskyi National University 303, no. 6 (2021): 31–36. http://dx.doi.org/10.31891/2307-5732-2021-303-6-31-36.

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The scientific article is devoted to the issues of SDR system control. Software Defined Radio is a system designed for software control of information transmission processes in a radio communication channel. Recognition of digital modulation types is used, which automatically classifies the type of digital modulation of the received signal. The following issues are covered in the article: the analysis of existing approaches in the task of automatic recognition of types of digital modulation is carried out; the analysis and classification of informative features in the task of automatic recogni

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18

YONG, Xiao-ju, Deng-fu ZHANG, and Shi-qiang WANG. "Automatic recognition of radar pulse modulation." Journal of Computer Applications 31, no. 6 (2012): 1730–32. http://dx.doi.org/10.3724/sp.j.1087.2011.01730.

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19

Li, Tong, and Yingzhe Xiao. "Domain Adaptation-Based Automatic Modulation Recognition." Scientific Programming 2021 (October 11, 2021): 1–9. http://dx.doi.org/10.1155/2021/4277061.

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Deep learning-based Automatic Modulation Recognition (AMR) can improve the recognition rate compared with traditional AMR methods. However, in practical applications, as training samples and real scenario signal samples have different distributions in practical applications, the recognition rate for target domain samples can deteriorate significantly. This paper proposed an unsupervised domain adaptation based AMR method, which can enhance the recognition performance by adopting labeled samples from the source domain and unlabeled samples from the target domain. The proposed method is validate

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20

Jin, Yong, Shuichi Ohno, and Shinichi Tokuhara. "Automatic Modulation Classification Using PDF Approximation." Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications 2012 (May 5, 2012): 360–63. http://dx.doi.org/10.5687/sss.2012.360.

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21

El-Nady, Nabil, and Emmanoel Hanna. "ZERO-CROSSINGS BASED AUTOMATIC MODULATION IDENTIFICATION." International Conference on Aerospace Sciences and Aviation Technology 4, ASAT CONFERENCE (1991): 1–15. http://dx.doi.org/10.21608/asat.1991.25839.

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22

Lin, Yun, and Chunguang Ma. "Automatic Modulation Recognition of Communication Signals." International Journal of Future Generation Communication and Networking 10, no. 1 (2017): 83–96. http://dx.doi.org/10.14257/ijfgcn.2017.10.1.08.

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23

Hsue, S. Z., and Samir S. Soliman. "Automatic modulation classification using zero crossing." IEE Proceedings F Radar and Signal Processing 137, no. 6 (1990): 459. http://dx.doi.org/10.1049/ip-f-2.1990.0066.

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24

Azzouz, E. E., and A. K. Nandi. "Automatic identification of digital modulation types." Signal Processing 47, no. 1 (1995): 55–69. http://dx.doi.org/10.1016/0165-1684(95)00099-2.

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25

Mathad, Sunil S., and C. Vijaya. "Revised architecture for automatic modulation recognition." International Journal of Information Technology 12, no. 2 (2019): 605–10. http://dx.doi.org/10.1007/s41870-019-00376-w.

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26

Dam Van Nhich. "The OFDM Modulation in the Problem of Automatic Modulation Recognition." INFORMACIONNYE TEHNOLOGII 24, no. 5 (2018): 345–50. http://dx.doi.org/10.17587/it.24.345-350.

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27

Zheng, Jianping, and Yanfang Lv. "Likelihood-Based Automatic Modulation Classification in OFDM With Index Modulation." IEEE Transactions on Vehicular Technology 67, no. 9 (2018): 8192–204. http://dx.doi.org/10.1109/tvt.2018.2839735.

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28

Ali, Afan, Fan Yangyu, and Shu Liu. "Automatic modulation classification of digital modulation signals with stacked autoencoders." Digital Signal Processing 71 (December 2017): 108–16. http://dx.doi.org/10.1016/j.dsp.2017.09.005.

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29

Li, Zixi. "Research on signal modulation based on machine learning intelligent algorithm and computer automatic identification." Journal of Physics: Conference Series 2083, no. 4 (2021): 042092. http://dx.doi.org/10.1088/1742-6596/2083/4/042092.

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Abstract In the process of communication, modulation signal recognition and classification are an important part of non-cooperative communication. Automatic modulation recognition technology of communication signals based on feature extraction and pattern recognition is a key research object in the radio field. The use of neural network can achieve automatic recognition of a variety of modulation signals and achieve good results. In this method, the received signal is preprocessed to obtain the complex baseband signal including in-phase component and orthogonal component. As the data set of th

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30

Benremdane, Yahya, Said Jamal, Oumaima Taheri, Jawad Lakziz, and Said Ouaskit. "Ensemble Learning Approach for Digital Communication Modulation’s Classification." International Journal of Artificial Intelligence & Applications 15, no. 1 (2024): 43–55. http://dx.doi.org/10.5121/ijaia.2024.15103.

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This work uses artificial intelligence to create an automatic solution for the modulation's classification of various radio signals. This project is a component of a lengthy communications intelligence process that aims to find an automated method for demodulating, decoding, and deciphering communication signals. As a result, the work we did involved selecting the database required for supervised deep learning, assessing the performance of current methods on unprocessed communication signals, and suggesting a deep learning network-based method that would enable the classification of modulation

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31

Harper, Clayton A., Mitchell A. Thornton, and Eric C. Larson. "Automatic Modulation Classification with Deep Neural Networks." Electronics 12, no. 18 (2023): 3962. http://dx.doi.org/10.3390/electronics12183962.

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Automatic modulation classification is an important component in many modern aeronautical communication systems to achieve efficient spectrum usage in congested wireless environments and other communications systems applications. In recent years, numerous convolutional deep learning architectures have been proposed for automatically classifying the modulation used on observed signal bursts. However, a comprehensive analysis of these differing architectures and the importance of each design element has not been carried out. Thus, it is unclear what trade-offs the differing designs of these conv

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32

Ouamna, Hamza, Anass Kharbouche, Zhour Madini, and Younes Zouine. "Deep Learning-assisted Automatic Modulation Classification using Spectrograms." Engineering, Technology & Applied Science Research 15, no. 1 (2025): 19925–32. https://doi.org/10.48084/etasr.9334.

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With the increasing demand for reliable and efficient V2X (Vehicle-to-Everything) communications in cognitive radio environments, spectrum sharing becomes imperative. In this context, accurate modulation classification serves as a fundamental component for efficient spectrum sensing and allocation. This paper proposes a novel approach utilizing Convolutional Neural Networks (CNNs) trained on spectrograms of BPSK and QPSK modulation schemes for automatic modulation classification in V2X scenarios. Experimental results demonstrated the effectiveness of the proposed CNN-based framework in accurat

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33

Wu, Chang Fu. "Analysis, Design and Implementation of FSK Modulate Systems." Advanced Materials Research 765-767 (September 2013): 2625–28. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.2625.

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The hybrid signals are the combined signals with multi-modulation mode. With multi-modulation, the disadvantages of single-modulation (such as FSK, PSK and PPM) can be eliminated. By taking the advantages of different modulations, the performance of range resolving, Doppler resolving and low probability of intercept (LPI) of the signal can be improved. FSK/PSK radar signal is the signal with both FSK and PSK modulation at one time. It is with large Bandwidth Time product (BT), thumbtack-like ambiguity function and well ability of Anti-EMI. The FSK/PSK signals of which the frequency-hopping and

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34

Bin, Ren, Zhu Ping, Cheng Ying, and Song Lei. "Analysis and Research of Modulation Recognition Algorithm in Satellite Communications." Advanced Materials Research 756-759 (September 2013): 1961–67. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.1961.

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Automatic identification of the communication signal modulation mode is a hot topic of research in the signal processing field in recent years. It is an important part of electronic countermeasures, and also a rapidly developed field of signal analysis. Communication signal modulation recognition is widely used in signal confirmation, interference identification, radio listening, signal monitoring, as well as software-defined radio, satellite communications and other fields. This paper proposed a digital modulation signal automatic recognition algorithm for satellite communication applications

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35

Wang, Feng, Shanshan Huang, Hao Wang, and Chenlu Yang. "Automatic Modulation Classification Exploiting Hybrid Machine Learning Network." Mathematical Problems in Engineering 2018 (December 4, 2018): 1–14. http://dx.doi.org/10.1155/2018/6152010.

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It is a research hot spot in cognitive electronic warfare systems to classify the electromagnetic signals of a radar or communication system according to their modulation characteristics. We construct a multilayer hybrid machine learning network for the classification of seven types of signals in different modulation. We extract the signal modulation features exploiting a set of algorithms such as time-frequency analysis, discrete Fourier transform, and instantaneous autocorrelation and accomplish automatic modulation classification using naive Bayesian and support vector machine in a hybrid m

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36

Yu, Mengze. "Application of Deep Learning to Automatic Modulation Recognition." Applied and Computational Engineering 175, no. 1 (2025): 18–29. https://doi.org/10.54254/2755-2721/2025.ast24988.

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With the rapid advancement of wireless communication technologies, the increasing diversity of modulation schemes poses significant challenges for traditional modulation recognition methods in complex communication environments. To address this, this research proposes a hybrid deep learning model that integrates Convolutional Neural Networks (CNN) and Transformers. The CNN module is employed to extract local time-frequency features from the modulated signals, enhancing the model's capacity to capture short-term dependencies. Meanwhile, the Transformer module leverages its self-attention mechan

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37

Chu, Peng, Lijin Xie, Chuanjin Dai, and Yarong Chen. "Automatic Modulation Recognition for Secondary Modulated Signals." IEEE Wireless Communications Letters 10, no. 5 (2021): 962–65. http://dx.doi.org/10.1109/lwc.2021.3051803.

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38

Huynh-The, Thien, Quoc-Viet Pham, Toan-Van Nguyen, et al. "Automatic Modulation Classification: A Deep Architecture Survey." IEEE Access 9 (2021): 142950–71. http://dx.doi.org/10.1109/access.2021.3120419.

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39

Xie, Lijin, and Qun Wan. "Automatic Modulation Recognition Using Compressive Cyclic Features." Algorithms 10, no. 3 (2017): 92. http://dx.doi.org/10.3390/a10030092.

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40

Weber, C., M. Peter, and T. Felhauer. "Automatic modulation classification technique for radio monitoring." Electronics Letters 51, no. 10 (2015): 794–96. http://dx.doi.org/10.1049/el.2015.0610.

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41

Xu, Jefferson L., Wei Su, and Mengchu Zhou. "Likelihood-Ratio Approaches to Automatic Modulation Classification." IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 41, no. 4 (2011): 455–69. http://dx.doi.org/10.1109/tsmcc.2010.2076347.

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42

ASANO, D., and M. OHARA. "Automatic Modulation Identification Using a Frequency Discriminator." IEICE Transactions on Communications E91-B, no. 2 (2008): 575–78. http://dx.doi.org/10.1093/ietcom/e91-b.2.575.

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43

Tu, Ya, Yun Lin, Changbo Hou, and Shiwen Mao. "Complex-Valued Networks for Automatic Modulation Classification." IEEE Transactions on Vehicular Technology 69, no. 9 (2020): 10085–89. http://dx.doi.org/10.1109/tvt.2020.3005707.

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44

CHEN, Mei, and Qi ZHU. "Cooperative automatic modulation recognition in cognitive radio." Journal of China Universities of Posts and Telecommunications 17, no. 2 (2010): 46–71. http://dx.doi.org/10.1016/s1005-8885(09)60445-3.

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45

Aisbett, Janet. "Automatic modulation recognition using time domain parameters." Signal Processing 13, no. 3 (1987): 323–28. http://dx.doi.org/10.1016/0165-1684(87)90130-7.

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46

Sherme, Ataollah Ebrahimzade. "A novel method for automatic modulation recognition." Applied Soft Computing 12, no. 1 (2012): 453–61. http://dx.doi.org/10.1016/j.asoc.2011.08.025.

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Zhang, Yuan, Xiurong Ma, and Duo Cao. "Automatic Modulation Recognition Based on Morphological Operations." Circuits, Systems, and Signal Processing 32, no. 5 (2013): 2517–25. http://dx.doi.org/10.1007/s00034-013-9577-4.

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Nie, Jinbo, Yan Zhang, Zunwen He, Shiyao Chen, Shouliang Gong, and Wancheng Zhang. "Deep Hierarchical Network for Automatic Modulation Classification." IEEE Access 7 (2019): 94604–13. http://dx.doi.org/10.1109/access.2019.2928463.

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49

Xu, Jefferson L., Wei Su, and MengChu Zhou. "Distributed Automatic Modulation Classification With Multiple Sensors." IEEE Sensors Journal 10, no. 11 (2010): 1779–85. http://dx.doi.org/10.1109/jsen.2010.2049487.

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50

Parmar, Ashok, Ankit Chouhan, Kamal Captain, and Jignesh Patel. "Deep multilevel architecture for automatic modulation classification." Physical Communication 64 (June 2024): 102361. http://dx.doi.org/10.1016/j.phycom.2024.102361.

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