Relevant bibliographies by topics / Digital modulation classification

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Digital modulation classification'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Digital modulation classification.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Digital modulation classification"

1

Faek, Fatima K. "Digital Modulation Classification Using Wavelet Transform and Artificial Neural Network." Journal of Zankoy Sulaimani - Part A 13, no. 1 (2009): 59–70. http://dx.doi.org/10.17656/jzs.10211.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Mobasseri, Bijan G. "Digital modulation classification using constellation shape." Signal Processing 80, no. 2 (2000): 251–77. http://dx.doi.org/10.1016/s0165-1684(99)00127-9.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Swami, A., and B. M. Sadler. "Hierarchical digital modulation classification using cumulants." IEEE Transactions on Communications 48, no. 3 (2000): 416–29. http://dx.doi.org/10.1109/26.837045.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Ghauri, Sajjad Ahmed. "KNN BASED CLASSIFICATION OF DIGITAL MODULATED SIGNALS." IIUM Engineering Journal 17, no. 2 (2016): 71–82. http://dx.doi.org/10.31436/iiumej.v17i2.641.

Full text
Abstract:
Demodulation process without the knowledge of modulation scheme requires Automatic Modulation Classification (AMC). When receiver has limited information about received signal then AMC become essential process. AMC finds important place in the field many civil and military fields such as modern electronic warfare, interfering source recognition, frequency management, link adaptation etc. In this paper we explore the use of K-nearest neighbor (KNN) for modulation classification with different distance measurement methods. Five modulation schemes are used for classification purpose which is Binary Phase Shift Keying (BPSK), Quadrature Phase Shift Keying (QPSK), Quadrature Amplitude Modulation (QAM), 16-QAM and 64-QAM. Higher order cummulants (HOC) are used as an input feature set to the classifier. Simulation results shows that proposed classification method provides better results for the considered modulation formats.
APA, Harvard, Vancouver, ISO, and other styles
5

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Zhang, Min, Zhongwei Yu, Hai Wang, Hongbo Qin, Wei Zhao, and Yan Liu. "Automatic Digital Modulation Classification Based on Curriculum Learning." Applied Sciences 9, no. 10 (2019): 2171. http://dx.doi.org/10.3390/app9102171.

Full text
Abstract:
Neural network shows great potential in modulation classification because of its excellent accuracy and achievability but overfitting and memorizing data noise often happen in previous researches on automatic digital modulation classifier. To solve this problem, we utilize two neural networks, namely MentorNet and StudentNet, to construct an automatic modulation classifier, which possesses great performance on the test set with −18–20 dB signal-to-noise ratio (SNR). The MentorNet supervises the training of StudentNet according to curriculum learning, and deals with the overfitting problem in StudentNet. The proposed classifier is verified in several test sets containing additive white Gaussian noise (AWGN), Rayleigh fading, carrier frequency offset and phase offset. Experimental results reveal that the overall accuracy of this classifier for common eleven modulation types was up to 99.3% while the inter-class accuracy could be up to 100%, which was much higher than many other classifiers. Besides, in the presence of interferences, the overall accuracy of this novel classifier still could reach 90% at 10 dB SNR indicting its excellent robustness, which makes it suitable for applications like military electronic warfare.
APA, Harvard, Vancouver, ISO, and other styles
7

Dobre, Octavia A., Ali Abdi, Yeheskel Bar-Ness, and Wei Su. "Cyclostationarity-Based Modulation Classification of Linear Digital Modulations in Flat Fading Channels." Wireless Personal Communications 54, no. 4 (2009): 699–717. http://dx.doi.org/10.1007/s11277-009-9776-2.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Ghauri, Sajjad Ahmed, Ijaz Mansoor Qureshi, Tanveer Ahmed Cheema, and Aqdas Naveed Malik. "A Novel Modulation Classification Approach Using Gabor Filter Network." Scientific World Journal 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/643671.

Full text
Abstract:
A Gabor filter network based approach is used for feature extraction and classification of digital modulated signals by adaptively tuning the parameters of Gabor filter network. Modulation classification of digitally modulated signals is done under the influence of additive white Gaussian noise (AWGN). The modulations considered for the classification purpose are PSK 2 to 64, FSK 2 to 64, and QAM 4 to 64. The Gabor filter network uses the network structure of two layers; the first layer which is input layer constitutes the adaptive feature extraction part and the second layer constitutes the signal classification part. The Gabor atom parameters are tuned using Delta rule and updating of weights of Gabor filter using least mean square (LMS) algorithm. The simulation results show that proposed novel modulation classification algorithm has high classification accuracy at low signal to noise ratio (SNR) on AWGN channel.
APA, Harvard, Vancouver, ISO, and other styles
9

Prakasam, P., and M. Madheswaran. "Digital Modulation Identification Model Using Wavelet Transform and Statistical Parameters." Journal of Computer Systems, Networks, and Communications 2008 (2008): 1–8. http://dx.doi.org/10.1155/2008/175236.

Full text
Abstract:
A generalized modulation identification scheme is developed and presented. With the help of this scheme, the automatic modulation classification and recognition of wireless communication signals with a priori unknown parameters are possible effectively. The special features of the procedure are the possibility to adapt it dynamically to nearly all modulation types, and the capability to identify. The developed scheme based on wavelet transform and statistical parameters has been used to identify M-ary PSK, M-ary QAM, GMSK, and M-ary FSK modulations. The simulated results show that the correct modulation identification is possible to a lower bound of 5 dB. The identification percentage has been analyzed based on the confusion matrix. When SNR is above 5 dB, the probability of detection of the proposed system is more than 0.968. The performance of the proposed scheme has been compared with existing methods and found it will identify all digital modulation schemes with low SNR.
APA, Harvard, Vancouver, ISO, and other styles
10

Wong, M. L. Dennis, and Asoke K. Nandi. "Semi-blind algorithms for automatic classification of digital modulation schemes." Digital Signal Processing 18, no. 2 (2008): 209–27. http://dx.doi.org/10.1016/j.dsp.2007.02.007.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Digital modulation classification"

1

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Hatzichristos, George. "Classification of digital modulation types in multipath environments." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2001. http://handle.dtic.mil/100.2/ADA390810.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Young, Andrew F. "Classification of digital modulation types in multipath environments." Thesis, Monterey California. Naval Postgraduate School, 2008.

Abstract:
Approved for public release, distribution unlimited
As the digital communications industry continues to grow and evolve, the applications of this discipline continue to grow as well. This growth, in turn, has spawned an increasing need to seek automated methods of classifying digital modulation types. This research is a revision of previous work, using the latest mathematical software including MATLAB version 7 and Simulink ®. The program considers the classification of nine different modulation types. Specifically, the classification scheme can differentiate between 2, 4, and 8 PSK, 256-QAM from other types of M-QAM signals, and also M-FSK signals from PSK and QAM signals in various types of propagation channels, including multipath fading and a variety of signal-to-noise levels. This method successfully identifies these modulation types without the benefit of a priori information. Higher-order statistical parameters are selected as class features and are tested in a classifier for their ability to identify the above modulation types. This study considers the effects due to realistic multipath propagation channels and additive white Gaussian noise. Using these features, and considering all fading conditions, it was determined that the classifier was correct for a randomly sent signal under randomly high or low SNR levels (low: 0dB to 8dB; high: 50dB to 100dB) over 83.9% of the time.
APA, Harvard, Vancouver, ISO, and other styles
4

Erdem, Erem. "Digital Modulation Recognition." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12611281/index.pdf.

Full text
Abstract:
In this thesis work, automatic recognition algorithms for digital modulated signals are surveyed. Feature extraction and classification algorithm stages are the main parts of a modulation recognition system. Performance of the modulation recognition system mainly depends on the prior knowledge of some of the signal parameters, selection of the key features and classification algorithm selection. Unfortunately, most of the features require some of the signal parameters such as carrier frequency, pulse shape, time of arrival, initial phase, symbol rate, signal to noise ratio, to be known or to be extracted. Thus, in this thesis, features which do not require prior knowledge of the signal parameters, such as the number of the peaks in the envelope histogram and the locations of these peaks, the number of peaks in the frequency histogram, higher order moments of the signal are considered. Particularly, symbol rate and signal to noise ratio estimation methods are surveyed. A method based on the cyclostationarity analysis is used for symbol rate estimation and a method based on the eigenvector decomposition is used for the estimation of signal to noise ratio. Also, estimated signal to noise ratio is used to improve the performance of the classification algorithm. Two methods are proposed for modulation recognition: 1) Decision tree based method 2) Bayesian based classification method A method to estimate the symbol rate and carrier frequency offset of minimum-shift keying (MSK) signal is also investigated.
APA, Harvard, Vancouver, ISO, and other styles
5

Zhu, Zhechen. "Automatic classification of digital communication signal modulations." Thesis, Brunel University, 2014. http://bura.brunel.ac.uk/handle/2438/9246.

Full text
Abstract:
Automatic modulation classification detects the modulation type of received communication signals. It has important applications in military scenarios to facilitate jamming, intelligence, surveillance, and threat analysis. The renewed interest from civilian scenes has been fuelled by the development of intelligent communications systems such as cognitive radio and software defined radio. More specifically, it is complementary to adaptive modulation and coding where a modulation can be deployed from a set of candidates according to the channel condition and system specification for improved spectrum efficiency and link reliability. In this research, we started by improving some existing methods for higher classification accuracy but lower complexity. Machine learning techniques such as k-nearest neighbour and support vector machine have been adopted for simplified decision making using known features. Logistic regression, genetic algorithm and genetic programming have been incorporated for improved classification performance through feature selection and combination. We have also developed a new distribution test based classifier which is tailored for modulation classification with the inspiration from Kolmogorov-Smirnov test. The proposed classifier is shown to have improved accuracy and robustness over the standard distribution test. For blind classification in imperfect channels, we developed the combination of minimum distance centroid estimator and non-parametric likelihood function for blind modulation classification without the prior knowledge on channel noise. The centroid estimator provides joint estimation of channel gain and carrier phase o set where both can be compensated in the following nonparametric likelihood function. The non-parametric likelihood function, in the meantime, provide likelihood evaluation without a specifically assumed noise model. The combination has shown to have higher robustness when different noise types are considered. To push modulation classification techniques into a more timely setting, we also developed the principle for blind classification in MIMO systems. The classification is achieved through expectation maximization channel estimation and likelihood based classification. Early results have shown bright prospect for the method while more work is needed to further optimize the method and to provide a more thorough validation.
APA, Harvard, Vancouver, ISO, and other styles
6

Geisinger, Nathan P. "Classification of digital modulation schemes using linear and nonlinear classifiers." Thesis, Monterey, California : Naval Postgraduate School, 2010. http://edocs.nps.edu/npspubs/scholarly/theses/2010/Mar/10Mar%5FGeisinger.pdf.

Full text
Abstract:
Thesis (Electrical Engineer and M.S. in Electrical Engineering)--Naval Postgraduate School, March 2010.
Thesis Advisor(s): Fargues, Monique P. ; Cristi, Roberto ; Robertson, Ralph C. "March 2010." Description based on title screen as viewed on .April 27, 2010. Author(s) subject terms: Blind Modulation Classification, Cumulants, Principal Component Analysis, Linear Discriminant Analysis, Kernel-based functions. Includes bibliographical references (p. 211-212). Also available in print.
APA, Harvard, Vancouver, ISO, and other styles
7

Puengnim, Anchalee. "Classification de modulations linéaires et non-linéaires à l'aide de méthodes bayésiennes." Toulouse, INPT, 2008. http://ethesis.inp-toulouse.fr/archive/00000676/.

Full text
Abstract:
La reconnaissance de modulations numériques consiste à identifier, au niveau du récepteur d'une chaîne de transmission, l'alphabet auquel appartiennent les symboles du message transmis. Cette reconnaissance est nécessaire dans de nombreux scénarios de communication, afin, par exemple, de sécuriser les transmissions pour détecter d'éventuels utilisateurs non autorisés ou bien encore de déterminer quel terminal brouille les autres. Le signal observé en réception est généralement affecté d'un certain nombre d'imperfections, dues à une synchronisation imparfaite de l'émetteur et du récepteur, une démodulation imparfaite, une égalisation imparfaite du canal de transmission. Nous proposons plusieurs méthodes de classification qui permettent d'annuler les effets liés aux imperfections de la chaîne de transmission. Les symboles reçus sont alors corrigés puis comparés à ceux du dictionnaire des symboles transmis
This thesis studies classification of digital linear and nonlinear modulations using Bayesian methods. Modulation recognition consists of identifying, at the receiver, the type of modulation signals used by the transmitter. It is important in many communication scenarios, for example, to secure transmissions by detecting unauthorized users, or to determine which transmitter interferes the others. The received signal is generally affected by a number of impairments. We propose several classification methods that can mitigate the effects related to imperfections in transmission channels. More specifically, we study three techniques to estimate the posterior probabilities of the received signals conditionally to each modulation
APA, Harvard, Vancouver, ISO, and other styles
8

Sinyanskiy, Alexander. "Automatická klasifikace digitálních modulací pomocí neuronových sítí." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2017. http://www.nusl.cz/ntk/nusl-317005.

Full text
Abstract:
This master’s thesis is about automatic digital modulation recognition using artificial neural networks. The paper briefly describes the issue and existing algorithms for solving the problem of modulation recognition. It was found that the best results are achieved when using the feature-recognition methods and artificial neural networks. The digital modulations that were chosen for recognition are described theoretically and they are ASK, FSK, BPSK, QPSK and 16QAM. These modulations are most commonly used today. Later was briefly described theory of neural networks. In another part was given to the characteristic features of modulation for modulation recognition using artificial neural networks. The penultimate part describes the parameters for signal simulation in Matlab, how to create the key features in Matlab and results after experimental simulation. The last part contains neural network optimization experiments.
APA, Harvard, Vancouver, ISO, and other styles
9

Fontes, Aluisio Igor R?go. "Classifica??o Autom?tica de Modula??o Digital com uso de Correntropia para Ambientes de R?dio Cognitivo." Universidade Federal do Rio Grande do Norte, 2012. http://repositorio.ufrn.br:8080/jspui/handle/123456789/15452.

Full text
Abstract:
Made available in DSpace on 2014-12-17T14:56:07Z (GMT). No. of bitstreams: 1 AluisioIRF_DISSERT.pdf: 1128206 bytes, checksum: 18eb3a8fe85de21077cb33d691adb61b (MD5) Previous issue date: 2012-12-14
Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior
Modern wireless systems employ adaptive techniques to provide high throughput while observing desired coverage, Quality of Service (QoS) and capacity. An alternative to further enhance data rate is to apply cognitive radio concepts, where a system is able to exploit unused spectrum on existing licensed bands by sensing the spectrum and opportunistically access unused portions. Techniques like Automatic Modulation Classification (AMC) could help or be vital for such scenarios. Usually, AMC implementations rely on some form of signal pre-processing, which may introduce a high computational cost or make assumptions about the received signal which may not hold (e.g. Gaussianity of noise). This work proposes a new method to perform AMC which uses a similarity measure from the Information Theoretic Learning (ITL) framework, known as correntropy coefficient. It is capable of extracting similarity measurements over a pair of random processes using higher order statistics, yielding in better similarity estimations than by using e.g. correlation coefficient. Experiments carried out by means of computer simulation show that the technique proposed in this paper presents a high rate success in classification of digital modulation, even in the presence of additive white gaussian noise (AWGN)
Os modernos sistemas de comunica??o sem fio empregam, frequentemente, t?cnicas adaptativas para proporcionar uma alta taxa de transmiss?o, enquanto asseguram qualidade de servi?o (QoS) e abrang?ncia de cobertura. Estudos recentes t?m mostrado que esses sistemas podem se tornar ainda mais eficientes com a incorpora??o de t?cnicas de intelig?ncia artificial e de conceitos de r?dio definido por software. Os sistemas que seguem essa linha, conhecidos como Sistemas de R?dio Cognitivo, podem idealmente explorar de forma din?mica e oportun?stica por??es do espectro de frequ?ncias n?o utilizadas, conhecidas como buracos espectrais, com o objetivo de prover altas taxas de transmiss?o de dados com elevada confiabilidade e disponibilidade de servi?o. A Classifica??o Autom?tica de Modula??o (AMC) seria uma habilidade muito ?til nesses sistemas. Normalmente, as t?cnicas de AMC utilizam alguma forma de pr?-processamento do sinal que pode introduzir um alto custo computacional ou necessitar de suposi??es fortes, e at? mesmo imprecisas, sobre o sinal recebido. Este trabalho prop?e o uso direto de uma medida de similaridade, baseada na Teoria da Informa??o, conhecida como coeficiente de correntropia, para extrair informa??es estat?sticas de ordem elevada do sinal, com o objetivo de reconhecer automaticamente o formato de modula??es digitais. Experimentos realizados por meio de simula??o computacional demonstram que a t?cnica proposta neste trabalho apresenta uma alta taxa de sucesso na classifica??o de modula??es digitais, mesmo na presen?a de ru?do aditivo gaussiano branco (AWGN)
APA, Harvard, Vancouver, ISO, and other styles
10

Richter, Raik. "Ein Beitrag zur Modellierung und Realisierung der direkten digitalen Frequenzsynthese." Doctoral thesis, [S.l. : s.n.], 1999. http://deposit.ddb.de/cgi-bin/dokserv?idn=963112023.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Digital modulation classification"

1

Classification of Digital Modulation Types in Multipath Environments. Storming Media, 2001.

APA, Harvard, Vancouver, ISO, and other styles
2

A Hierarchical Approach to the Classification of Digital Modulation Types in Multipath Environments. Storming Media, 2001.

APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Digital modulation classification"

1

Zong, Wenbo, Edmund M.-K. Lai, and Chai Quek. "Digital Modulation Classification using Fuzzy Neural Networks." In Chance Discoveries in Real World Decision Making. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-34353-0_7.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Digital modulation classification"

1

Li, Yuanxuan, Fanggang Wang, and Gang Zhu. "Hybrid digital modulation classification." In 2012 8th International Wireless Communications and Mobile Computing Conference (IWCMC 2012). IEEE, 2012. http://dx.doi.org/10.1109/iwcmc.2012.6314340.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Zhang, Wei, and Hu Yang. "Automatic Digital Modulation Classification Algorithms." In 2006 8th international Conference on Signal Processing. IEEE, 2006. http://dx.doi.org/10.1109/icosp.2006.345918.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Marinovic, Nenad M., Douglas J. Nelson, Leon Cohen, and Srinivasan Umesh. "Classification of digital modulation types." In SPIE's 1995 International Symposium on Optical Science, Engineering, and Instrumentation, edited by Franklin T. Luk. SPIE, 1995. http://dx.doi.org/10.1117/12.211392.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Deng, Hongyang, Milos Doroslovacki, Hussam Mustafa, Jinghao Xu, and Sunggy Koo. "Automatic digital modulation classification using instantaneous features." In Proceedings of ICASSP '02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.5745605.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Hongyang Deng, Doroslovacki, Mustafa, Jinghao Xu, and Sunggy Koo. "Automatic digital modulation classification using instantaneous features." In IEEE International Conference on Acoustics Speech and Signal Processing ICASSP-02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.1004866.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Tabatabaei, Talieh S., Sridhar Krishnan, and Alagan Anpalagan. "SVM-based classification of digital modulation signals." In 2010 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2010. http://dx.doi.org/10.1109/icsmc.2010.5642249.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Sun, Gangcan, Pingping Li, and Zhongyong Wang. "Digital modulation classification using constellation shape reconstruction." In International Conference on Communication Technology. WIT Press, 2014. http://dx.doi.org/10.2495/icct130651.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

An, Qi, Wei Xia, Zi-shu He, and Hui-yong Li. "Algorithm for modulation classification of PSK signals." In 2014 International Conference on Digital Signal Processing (DSP). IEEE, 2014. http://dx.doi.org/10.1109/icdsp.2014.6900741.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Zhechen Zhu, Muhammad Waqar Aslam, and Asoke Kumar Nandi. "Augmented Genetic Programming for automatic digital modulation classification." In 2010 IEEE International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2010. http://dx.doi.org/10.1109/mlsp.2010.5588920.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Gouldieff, Vincent, Jacques Palicot, and Steredenn Daumont. "Blind automatic modulation classification in multipath fading channels." In 2017 22nd International Conference on Digital Signal Processing (DSP). IEEE, 2017. http://dx.doi.org/10.1109/icdsp.2017.8096116.

Full text
APA, Harvard, Vancouver, ISO, and other styles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography