Relevant bibliographies by topics / Automatic modulation

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
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Academic literature on the topic 'Automatic modulation'

Author: Grafiati
Published: 5 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Automatic modulation"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Automatic modulation"

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Nash, Christopher. "AUTOMATIC MODULATION RECOGNITION FOR CPM." International Foundation for Telemetering, 2016. http://hdl.handle.net/10150/624250.

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This paper uses detection and estimation theory techniques for automatic modulation recognition of CPM signals. The CPM signals of interest are PCM/FM, SOQPSK-TG, and ARTM/CPM. The modulation recognition problem is formulated as a hypothesis test with the test statistic computed using samples of the observed signal. Using such techniques, simulation results show that correct modulation can be achieved error free at a carrier-to-noise ratio of 19 dB for PCM/FM, 50 dB for SOQPSK-TG, and 25 dB for ARTM CPM.
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Frogget, Jacob, and Michael Rice. "Automatic Modulation Recognition for Aeronautical Telemetry." International Foundation for Telemetering, 2013. http://hdl.handle.net/10150/579662.

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ITC/USA 2013 Conference Proceedings / The Forty-Ninth Annual International Telemetering Conference and Technical Exhibition / October 21-24, 2013 / Bally's Hotel & Convention Center, Las Vegas, NV<br>This paper applies the Bianchi-Loubaton-Sirven technique to classification algorithm capable of distinguishing between PCM/FM and SOQPSK-TG. A happy byproduct of the classification algorithm is a reasonably accurate estimate of the bit rate. The classifier is based on the observation that CPM with an integer modulation index contains harmonics at multiples of the symbol rate. The algorithm is base
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Frogget, Jacob William. "Automatic Modulation Recognition for Aeronautical Telemetry." BYU ScholarsArchive, 2013. https://scholarsarchive.byu.edu/etd/3826.

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This these explores automatic modulation recognition as applied to PCM/FM, SOQPSK- TG and ARTM CPM. It found that the likelihood based approach is intractable. The statistical features of the amplitude, phase and frequency are ineffective at distinguishing these modulation types. A method based on the phase changes between symbols is developed and shows that as long as symbol timing is established, this method can effectively distinguish PCM/FM, SOQPSK-TG and ARTM CPM for signal-to-noise ratios above 30 dB. Another method, the Bianchi-Loubaton- Sirven technique, was able to distinguish PCM/FM
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Wong, Dennis Mou Ling. "Automatic classification of digital communication modulation schemes." Thesis, University of Liverpool, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.400128.

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Price, Matthew. "Automatic Modulation Classification Using Grey Relational Analysis." Thesis, Virginia Tech, 2011. http://hdl.handle.net/10919/42441.

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One component of wireless communications of increasing necessity in both civilian and military applications is the process of automatic modulation classification. Modulation of a detected signal of unknown origin requiring interpretation must first be determined before the signal can be demodulated. This thesis presents a novel architecture for a modulation classifier that determines the most likely modulation using Grey Relational Analysis with the extraction and combination of multiple signal features. An evaluation of data preprocessing methods is conducted and performance of the classifier
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Chen, Yun [Verfasser]. "Automatic Modulation Classification in Mobile OFDM Systems with Adaptive Modulation / Yun Chen." Aachen : Shaker, 2014. http://d-nb.info/1049380568/34.

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Cevik, Gozde. "Feature Based Modulation Recognition For Intrapulse Modulations." Master's thesis, METU, 2006. http://etd.lib.metu.edu.tr/upload/12607676/index.pdf.

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In this thesis study, a new method for automatic recognition of intrapulse modulations has been proposed. This new method deals the problem of modulation recognition with a feature-based approach. The features used to recognize the modulation type are Instantaneous Frequency, Instantaneous Bandwidth, Amplitude Modulation Depth, Box Dimension and Information Dimension. Instantaneous Bandwidth and Instantaneous Frequency features are extracted via Autoregressive Spectrum Modeling. Amplitude Modulation Depth is used to express the depth of amplitude change on the signal. The other features, Box
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Cutno, Patrick. "Automatic Modulation Classifier - A Blind Feature-Based Tool." Miami University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=miami1480079193743277.

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Kalla, Manish. "Mechanistic insights in the automatic modulation of ventricular arrhythmia." Thesis, University of Oxford, 2015. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.714086.

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Malyska, Nicolas 1977. "Automatic voice disorder recognition using acoustic amplitude modulation features." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/30092.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.<br>Includes bibliographical references (p. 114-117).<br>An automatic dysphonia recognition system is designed that exploits amplitude modulations (AM) in voice using biologically-inspired models. This system recognizes general dysphonia and four subclasses: hyperfunction, A-P squeezing, paralysis, and vocal fold lesions. The models developed represent processing in the auditory system at the level of the cochlea, auditory nerve, and inferior colliculus. Recognition experiments usin
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Books on the topic "Automatic modulation"

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Zhu, Zhechen, and Asoke K. Nandi. Automatic Modulation Classification. John Wiley & Sons, Ltd, 2014. http://dx.doi.org/10.1002/9781118906507.

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Azzouz, Elsayed Elsayed, and Asoke Kumar Nandi. Automatic Modulation Recognition of Communication Signals. Springer US, 1996. http://dx.doi.org/10.1007/978-1-4757-2469-1.

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Azzouz, Elsayed Elsayed. Automatic Modulation Recognition of Communication Signals. Springer US, 1996.

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Azzouz, Elsayed Elsayed. Automatic modulation recognition of communication signals. Kluwer Academic Publishers, 1996.

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Nandi, Asoke K., and Zhechen Zhu. Automatic Modulation Classification: Principles, Algorithms and Applications. Wiley & Sons, Limited, John, 2014.

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Nandi, Asoke K., and Zhechen Zhu. Automatic Modulation Classification: Principles, Algorithms and Applications. Wiley & Sons, Incorporated, John, 2014.

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Nandi, Asoke K., and Zhechen Zhu. Automatic Modulation Classification: Principles, Algorithms and Applications. Wiley & Sons, Incorporated, John, 2014.

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Nandi, Asoke K., and Zhechen Zhu. Automatic Modulation Classification: Principles, Algorithms and Applications. Wiley, 2015.

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Sira-Ramírez, Hebertt. Sliding Mode Control: The Delta-Sigma Modulation Approach. Springer International Publishing AG, 2016.

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Sira-Ramírez, Hebertt. Sliding Mode Control: The Delta-Sigma Modulation Approach. Birkhäuser, 2015.

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Book chapters on the topic "Automatic modulation"

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Genschow, Oliver, and Emiel Cracco. "Social Modulation of Imitative Behavior." In Automatic Imitation. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-62634-0_11.

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AbstractGiven that imitative behavior is a social phenomenon, an often put forward claim in the literature is that imitation should be modulated by social factors. Motivational theories explain social modulation with the notion that people use imitation as a tool to affiliate with others. As a result, individuals are expected to imitate others more when they have an affiliation goal. Self-other overlap theories suggest that imitative tendencies are learned responses that develop as a result of self-observation and interaction with other, often similar individuals. As a consequence, imitation i
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Poliakoff, Ellen, and Emma Gowen. "Automatic Imitation of Hand Movements in Clinical and Neurodiverse Populations." In Automatic Imitation. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-62634-0_12.

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AbstractAutomatic imitation is related to both motor and social-cognitive processes and hence is highly relevant to a range of clinical and neurodiverse populations including neurodegenerative, psychiatric or mental health, and neurodevelopmental conditions. In this chapter, we review investigations of automatic imitation of hand and arm movements in these populations. For many of the conditions reviewed, there are relatively small numbers of studies in the literature and/or existing studies only include relatively small participant numbers. There is, however, some evidence for intact automati
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Azzouz, Elsayed Elsayed, and Asoke Kumar Nandi. "Modulation Recognition Using Artificial Neural Networks." In Automatic Modulation Recognition of Communication Signals. Springer US, 1996. http://dx.doi.org/10.1007/978-1-4757-2469-1_5.

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Azzouz, Elsayed Elsayed, and Asoke Kumar Nandi. "Introduction." In Automatic Modulation Recognition of Communication Signals. Springer US, 1996. http://dx.doi.org/10.1007/978-1-4757-2469-1_1.

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Azzouz, Elsayed Elsayed, and Asoke Kumar Nandi. "Recognition of Analogue Modulations." In Automatic Modulation Recognition of Communication Signals. Springer US, 1996. http://dx.doi.org/10.1007/978-1-4757-2469-1_2.

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Azzouz, Elsayed Elsayed, and Asoke Kumar Nandi. "Recognition of Digital Modulations." In Automatic Modulation Recognition of Communication Signals. Springer US, 1996. http://dx.doi.org/10.1007/978-1-4757-2469-1_3.

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Azzouz, Elsayed Elsayed, and Asoke Kumar Nandi. "Recognition of Analogue & Digital Modulations." In Automatic Modulation Recognition of Communication Signals. Springer US, 1996. http://dx.doi.org/10.1007/978-1-4757-2469-1_4.

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Azzouz, Elsayed Elsayed, and Asoke Kumar Nandi. "Summary and Suggestions for Future Directions." In Automatic Modulation Recognition of Communication Signals. Springer US, 1996. http://dx.doi.org/10.1007/978-1-4757-2469-1_6.

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Rao, N. Venkateswara, and B. T. Krishna. "Automatic Modulation Recognition of Analog Modulation Signals Using Convolutional Neural Network." In Lecture Notes in Electrical Engineering. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8554-5_39.

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Pan, Jiawei, Junsheng Mu, and Xiaojun Jing. "Automatic Modulation Classification Based on Knowledge Graph." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4775-9_138.

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Conference papers on the topic "Automatic modulation"

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Chennagiri, Rakshit, Sahil Sehgal, and Yerram Ravinder. "A Survey on Automatic Modulation Classification Techniques." In 2024 Intelligent Systems and Machine Learning Conference (ISML). IEEE, 2024. https://doi.org/10.1109/isml60050.2024.11007395.

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Zhou, Haopeng, Hongguang Zhang, Jia Liu, Ziyan Duan, Daigao Chen, and Xi Xiao. "Automatic Wavelength Locking Stabilization of Silicon Microring Modulator With 240Gb/s PAM4 Modulation." In 2024 Asia Communications and Photonics Conference (ACP) and International Conference on Information Photonics and Optical Communications (IPOC). IEEE, 2024. https://doi.org/10.1109/acp/ipoc63121.2024.10809775.

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Guo, Chengyu, Shuai Han, Weixiao Meng, and Cheng Li. "ABConv: Attention Based Convolution for Automatic Modulation Recognition." In 2024 IEEE International Conference on Communications Workshops (ICC Workshops). IEEE, 2024. http://dx.doi.org/10.1109/iccworkshops59551.2024.10615659.

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Li, Yanfu, Yuan Ma, Huanhuan Yang, and Qingyan Huang. "Automatic Modulation Recognition of Multi-type Communication Signals." In 2024 4th International Conference on Electronics, Circuits and Information Engineering (ECIE). IEEE, 2024. http://dx.doi.org/10.1109/ecie61885.2024.10627225.

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Xu, Bo, Chen Luo, Hao Tang, Uzair Aslam Bhatti, Xianpeng Wang, and Wenchao Jiang. "Advancing Transparency in AI-based Automatic Modulation Classification." In 2024 IEEE/CIC International Conference on Communications in China (ICCC). IEEE, 2024. http://dx.doi.org/10.1109/iccc62479.2024.10682007.

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Li, Yingkai, Shufei Wang, Yibin Zhang, et al. "Multi-Modal Fusion for Enhanced Automatic Modulation Classification." In 2024 IEEE 99th Vehicular Technology Conference (VTC2024-Spring). IEEE, 2024. http://dx.doi.org/10.1109/vtc2024-spring62846.2024.10683086.

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Singh, Brahmjit, Shalu, and Chandra Prakash. "Deep Learning Enabled Algorithm for Automatic Modulation Classification." In 2024 IEEE Space, Aerospace and Defence Conference (SPACE). IEEE, 2024. http://dx.doi.org/10.1109/space63117.2024.10668355.

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Xie, Lei, Mingyuan Shao, Dingzhao Li, Jie Qi, Shaohua Hong, and Haixin Sun. "ZSLformer: Zero-Shot Learning for Automatic Modulation Recognition." In 2025 IEEE 5th International Conference on Power, Electronics and Computer Applications (ICPECA). IEEE, 2025. https://doi.org/10.1109/icpeca63937.2025.10928809.

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Wang, Chuan, and Zhuoran Cai. "TADmobileNet: A More Reliable Automatic Modulation Classification Network." In 2024 Global Reliability and Prognostics and Health Management Conference (PHM-Beijing). IEEE, 2024. https://doi.org/10.1109/phm-beijing63284.2024.10874475.

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Singh, Brahmjit, Poonam Jindal, Pankaj Verma, Vishal Sharma, and Chandra Prakash. "Automatic Modulation Recognition Using Modified Convolutional Neural Network." In 2025 3rd International Conference on Device Intelligence, Computing and Communication Technologies (DICCT). IEEE, 2025. https://doi.org/10.1109/dicct64131.2025.10986502.

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Reports on the topic "Automatic modulation"

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Lehotay, Steven J., and Aviv Amirav. Fast, practical, and effective approach for the analysis of hazardous chemicals in the food supply. United States Department of Agriculture, 2007. http://dx.doi.org/10.32747/2007.7695587.bard.

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Background to the topic: For food safety and security reasons, hundreds of pesticides, veterinary drugs, and environmental pollutants should be monitored in the food supply, but current methods are too time-consuming, laborious, and expensive. As a result, only a tiny fraction of the food is tested for a limited number of contaminants. Original proposal objectives: Our main original goal was to develop fast, practical, and effective new approaches for the analysis of hazardous chemicals in the food supply. We proposed to extend the QuEChERS approach to more pesticides, veterinary drugs and pol
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