Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Wireless Personal Communications
Erschienen in: Wireless Personal Communications 3/2017

24.05.2017

A Novel Approach for Automatic Modulation Classification via Hidden Markov Models and Gabor Features

verfasst von: Sajjad Ahmed Ghauri, Ijaz Mansoor Qureshi, Aqdas Naveed Malik

Erschienen in: Wireless Personal Communications | Ausgabe 3/2017

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

In this paper, a novel approach is proposed for classification of pulse amplitude modulated (PAM) and quadrature amplitude modulated (QAM) signals. The automatic modulation classification is the intermediate step between detection and demodulation of the signal. In this paper, we propose a classifier for digital modulated signals such as PAM and QAM that differs with the existing classifiers. The gabor parameters have been used as input features and the proposed classifier uses hidden markov model in conjunction with genetic algorithm (GA). The fitness function for the genetic algorithm is probability of observation sequence given the model. The objective is to maximize the probability of observation using Baum–Welch algorithm and GA. To improve the classification accuracy, classification process has been divided in two phases. Simulation results shows the significant performance improvement while compare with other existing techniques.
Vorheriger Artikel Nonlinear Cryptosystem Based on QC-LDPC Codes for Enhanced Security and Reliability with Low Hardware Complexity and Reduced Key Size
Nächster Artikel A Hierarchical Multipath Routing Protocol in Clustered Wireless Sensor Networks

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
1.
Azzouz, E. E., & Nandi, A. K. (1996). Automatic modulation recognition of communication signals. Kluwer Academic Publishers.
2.
Nandi, A. K., & Azzouz, E. E. (1998). Algorithms for automatic modulation recognition of communication signals. IEEE Transactions on Communications, 46, 431–436.CrossRef
3.
Ramkumar, B. (2009). Automatic modulation classification for cognitive radios using cyclic feature detection. IEEE Circuits and Systems Magazine, 9, 27–45.CrossRef
4.
Dobre, O. A., Abdi, A., Bar-Ness, Y., & Su, W. (2007). A survey of automatic modulation classification techniques: classical approaches and new trends. IET Communication, 1, 137–156.CrossRef
5.
Sue, W., Jefferson, L. X., & Mengchou, Z. (2008). Real-time modulation classification based on maximum likelihood. IEEE Communications letters, 12(11), 781–784.
6.
Yang, Y., Chang, J. N., Liu, J. C., & Liu, C. H. (2007). Maximum log-likelihood function based QAM signal classification over fading channel. Wireless Personal Communications, 28, 77–94.CrossRef
7.
Panagiotou, P., Anastasopoulos, A., & Polydoros, A. (2000). Likelihood ratio tests for modulation classification. In Proceedings of the IEEE MILCOM, vol. 2, (pp. 670–674).
8.
Hameed, F., Dobre, O., & Popescu, D. (2009). On the likelihood based approach to modulation classification. IEEE Transactions on Wireless Communication, 8(12), 5884–5892.CrossRef
9.
Ramezani Kebrya, A., Kim, I.-M., Kim, D. I., Chan, F., & Inkol, R. (2013). Likelihood based modulation classification for multiple antenna receiver. IEEE Transactions on Communications, 61(9), 3816–3829.CrossRef
10.
Sergienko, A. B., & Osipov, A. V. (2014). Digital modulation recognition using circular harmonic approximation of likelihood function. In IEEE international conference on acoustic, speech and signal processing (ICASSP), (pp. 3460–3463).
11.
Zhu, Z., & Nandi, A. K. (2014). Blind digital modulation classification using minimum distance centroid estimator and non-parametric likelihood function. IEEE Transactions on Wireless Communication, 13(8), 4483–4494.CrossRef
12.
Lin, Y. C., & Jay Kuo, C. C. (1997). Classification of quadrature amplitude modulated (QAM) signals via sequential probability ratio test (SPRT). Signal Processing, 60, 263–280.CrossRefMATH
13.
Soltan Mohammadi, E., & Naraghi Pour, M. (2013). Blind modulation classification over fading channels using expectation maximization. IEEE Communication Letters, 17(9), 1692–1695.CrossRef
14.
Wei, Su, Xu, Jefferson L., & Zhou, Mengchu. (2008). Real time modulation classification based on maximum likelihood. IEEE Communication Letters, 12(11), 801–803.CrossRef
15.
Xu, Jefferson L., Wei, Su, & Zhou, Mengchu. (2010). Software defined radio equipped with rapid modulation recognition. IEEE Transactions on Vehicular Technology, 59(4), 1659–1667.CrossRef
16.
Ghauri, S. A., Qureshi, I. M., Malik, A. N., & Cheema, T. A. (2014). Automatic digital modulation recognition technique using higher order cummulants on faded channels. Journal of Basic and Applied Scientific Research, 4(3), 1–12.
17.
Ghauri, S. A., Qureshi, I. M., Cheema, T. A., & Malik, A. N. (2014). A Novel Modulation Classification Approach Using Gabor Filter Network. Cairo: The World Scientific Journal, Hindwai Publishers.
18.
Ghauri, S. A., Qureshi, I. M., Malik, A. N., & Cheema, T. A. (2013). Higher order cummulants based digital modulation recognition scheme. Research Journal of Applied Sciences Engineering and Technology (RJASET), 6(20), 3910–3915.
19.
Ahmadi, Negar. (2011). Modulation recognition based on Constellation shape using TTSAS algorithm and template matching. Journal of Pattern Recognition Research, 1, 43–55.CrossRef
20.
He, Tao, & Zhou, Zheng-Ou. (2008). Classification of modulated signals using multifractal features. Journal of the Chinese Institute of Engineers, 31(2), 335–338.CrossRef
21.
Aslam, M. W., Zhu, Z., & Nandi, A. K. (2012). Automatic modulation classification using combination of Genetic programming and KNN. IEEE Transaction on Wireless Communication, 11(8), 2742–2750.
22.
Puengnim, A., Thomas, N., Tourneret, J. Y., & Vidal, J. (2010). Classification of linear and nonlinear modulation using the Baum–Welch algorithms and MCMC methods. Signal Processing, 90(12), 3342–3355.CrossRefMATH
23.
Dulek, B., Ozdemir, O., Varshney, P. K., & Su, W. (2014). A novel approach to dictionary construction for Automatic modulation classification. Journal of the Franklin Institute, 351, 2991–3012.MathSciNetCrossRef
24.
Dobre, O. A., Abdi, A., Bar Ness, Y., & Su, W. (2010). Cyclostatoinarity based modulation classification of linear digital modulations in flat fading channels. Wireless Personal Communication, 54, 699–717.CrossRef
25.
Swami, A., & Sadler, B. (2000). Hierarchical digital modulation classification using cummulants. IEEE Transactions on Communication, 48(3), 416–429.CrossRef
26.
Liu, P., & Shui, P. L. (2014). Digital modulation classifier with rejection ability via greedy convex hull learning and alternative convex hull shrinkage in feature space. IEEE Transaction on Wireless Communication, 13(5), 2683–2695.CrossRef
27.
Su, W. (2013). Feature space analysis of modulation classification using very higher order statistics. IEEE Communication Letters, 17(9), 1688–1691.CrossRef
28.
Kharbech, S., Dayoub, I., Zwingelstein Colin, M., Simon, E. P., & Hassan, K. (2014). Blind digital modulation identification for time selective MIMO channels. IEEE Wireless Communication Letters, 3(4), 373–376.CrossRef
29.
Marey, M., & Dobre, O. A. (2014). Blind modulation classification algorithm for single and multiple antenna systems over frequency selective channels. IEEE Signal Processing Letters, 21(9), 1098–1102.CrossRef
30.
Avci, E., Hanbay, D., & Varol, A. (2007). An expert discrete wavelet adaptive network based fuzzy inference system for digital modulation recognition. Expert System with Applications, 33, 582–589.CrossRef
31.
Shi, Q., Gong, Y., & Guan, Y. L. (2011). Modulation classification for asynchronous high-order QAM signals. Wireless Communications and Mobile Computing, 11, 1415–1422.CrossRef
32.
Dobre, O. A., Bar-Ness, Y., & Su, W. (2003). Higher-order cyclic cummulants for high order modulation classification. In Proceedings of the 2003 IEEE MILCOM, (pp. 112–117).
33.
Xi, S., & Wu, H. C. (2006). Robust automatic modulation classification using cummulants features in the presence of fading channels. IEEE Wireless Communication and Networking Conference, 4, 2094–2099.
34.
Wu, H., Saquib, M., & Yun, Z. (2008). Novel automatic modulation classification using cumulant features for communications via multipath channels. IEEE Transactions on Wireless Communications, 7, 3098–3105.CrossRef
35.
Chaithanya, V., & Reddy, V. U., (2010). Blind modulation classification in the presence of carrier frequency offset. In Proceedings of the 2010 international conference on signal processing and communications, (pp. 1–5).
36.
Puengnim, A., Thomas, N., Tourneret J. Y., & Vidal, J. (2007). Hidden markov models for digital modulation classification in unknown ISI channels. In 15th European signal processing conference, (pp. 1882–1886).
37.
Mirrarab, M. R., & Sobhani, M. A., Robust modulation classification for PSK/QAM/ASK using higher order cummulants. In International conference on information, communications and signals, (pp.1–4).
Metadaten
Titel
A Novel Approach for Automatic Modulation Classification via Hidden Markov Models and Gabor Features
verfasst von
Sajjad Ahmed Ghauri
Ijaz Mansoor Qureshi
Aqdas Naveed Malik
Publikationsdatum
24.05.2017
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 3/2017
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-017-4378-x

Weitere Artikel der Ausgabe 3/2017

Wireless Personal Communications 3/2017 Zur Ausgabe

OriginalPaper

A Compressive Sensing Channel Estimation for MIMO FBMC/OQAM System

OriginalPaper

Performance of Joint Relay and Antenna Selection in the Full-Duplex Amplify-and-Forward Relay Networks

OriginalPaper

An Incentive-Based Scheme for Mitigating Node Selfishness in Smart Opportunistic Mobile Networks

OriginalPaper

Prioritized Mobile Node Deployment to Enhance the Lifetime of Wireless Sensor Network

OriginalPaper

Characterising an In-Room MIMO System Employing Elevation-Directional Access Point Antennas

OriginalPaper

A Novel Interdependent Source-Channel Coding Technique for Enhanced Energy Efficiency in Communication over Wireless Sensor Networks

Neuer Inhalt

    Bildnachweise