Top

Wireless Personal Communications

Published in:

02-03-2022

Blind Signal Modulation Classification Using Constellation Pattern Analysis with Oversampling Factor Alteration

Authors: Yogesh Kumar, Gaurav Jajoo, Ashok Kumar, Sandeep Kumar Yadav

Published in: Wireless Personal Communications | Issue 1/2022

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Automatic modulation classification finds its application in many military and civil areas. It is an integral part of cognitive radio and software defined radio technologies. In this paper, LabVIEW based Field Programmable Gate Array (FPGA) implementation of modulation classification algorithm is proposed. Any modulation scheme among BPSK, QPSK, 8PSK, 8QAM, 16QAM and 4ASK is classified by alteration of oversampling factor and further error minimization between extracted constellation and ideal constellation of considered modulation schemes. Study results reveal that the developed method detects the above mentioned modulation schemes reliably above 12 dB SNR in Additive White Gaussian Noise (AWGN) channel. Comparative analysis of the proposed method with existing methods based on higher order statistics, mel-frequency ceptral coefficients and naive based modulation classification shows an overall improvement in classification accuracy.

previous article DC-CAMP: Dynamic Controller Creation, Allocation and Management Protocol in SDN

next article NMRMG: Nonlinear Multiple-Recursive Matrix Generator Design Approaches and Its Randomness Analysis

Dont have a licence yet? Then find out more about our products and how to get one now:

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!

inform now

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!

inform now

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!

inform now

Ali, A., Yangyu, F., & Liu, S. (2017). Automatic modulation classification of digital modulation signals with stacked autoencoders. Digital Signal Processing, 71, 108–116.MathSciNetCrossRef

Arain, S., Shaikh, M. N., Waqas, A., Ali, Q., Chowdhry, B. S., & Themistos, C. (2017). Comparative study and packet error rate analysis of advance modulation schemes for optical wireless communication networks. Wireless Personal Communications, 95(2), 593–606.CrossRef

Aslam, M.W., Zhu, Z & Nandi, A.K. (2010). Automatic digital modulation classification using genetic programming with k-nearest neighbor. In: Military Communications Conference, 2010-MILCOM 2010, pp. 1731–1736. IEEE

Aslam, M. W., Zhu, Z., & Nandi, A. K. (2012). Automatic modulation classification using combination of genetic programming and knn. IEEE Transactions on wireless communications, 11(8), 2742–2750.

Avci, E. (2009). Selecting of the optimal feature subset and kernel parameters in digital modulation classification by using hybrid genetic algorithm-support vector machines: Hgasvm. Expert Systems with Applications, 36(2), 1391–1402.CrossRef

Dobre, O. A., Abdi, A., Bar-Ness, Y., & Su, W. (2007). Survey of automatic modulation classification techniques: classical approaches and new trends. IET communications, 1(2), 137–156.CrossRef

Dobre, O.A., Bar-Ness, Y & Su, W. (2003). Higher-order cyclic cumulants for high order modulation classification. In: Military Communications Conference, 2003. MILCOM’03. 2003 IEEE, vol. 1, pp. 112–117. IEEE

Fehske, A., Gaeddert, J & Reed, J.H. (2005). A new approach to signal classification using spectral correlation and neural networks. In: New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005. 2005 First IEEE International Symposium on, pp. 144–150. IEEE

Hameed, F., Dobre, O. A., & Popescu, D. C. (2009). On the likelihood-based approach to modulation classification. IEEE Transactions on Wireless Communications, 8(12), 5884–5892.CrossRef

10.

Hazza, A., Shoaib, M., Alshebeili, S. A., & Fahad, A. (2013). An overview of feature-based methods for digital modulation classification (pp. 1–6). IEEE.

11.

Hong, L & Ho, K. (1999). Identification of digital modulation types using the wavelet transform. In: Military Communications Conference Proceedings, 1999. MILCOM 1999. IEEE, vol. 1, pp. 427–431. IEEE

12.

Huan, C. Y., & Polydoros, A. (1995). Likelihood methods for mpsk modulation classification. IEEE Transactions on Communications, 43(234), 1493–1504.CrossRef

13.

Jajoo, G., Kumar, Y., Kumar, A., & Yadav, S. K. (2020). Blind signal modulation recognition through density spread of constellation signature. Wireless Personal Communications, 114(4), 3137–3156.CrossRef

14.

Jajoo, G., Kumar, Y., Kumar, A., & Yadav, S. K. (2020). Implementation of modulation classifier over software defined radio. IET Communications, 14(9), 1467–1475.CrossRef

15.

Jajoo, G., Kumar, Y., & Yadav, S. K. (2019). Blind signal psk/qam recognition using clustering analysis of constellation signature in flat fading channel. IEEE Communications Letters, 23(10), 1853–1856.CrossRef

16.

Jajoo, G., Kumar, Y., Yadav, S. K., Adhikari, B., & Kumar, A. (2017). Blind signal modulation recognition through clustering analysis of constellation signature. Expert Systems with Applications, 90, 13–22.CrossRef

17.

Jajoo, G., Yadav, Y. K., & Yadav, S. (2018). Blind signal digital modulation classification through k-medoids clustering (pp. 1–5). IEEE.

18.

Keshk, M. E. H. M., El-Naby, M. A., Al-Makhlasawy, R. M., El-Khobby, H. A., Hamouda, W., Elnaby, M. M. A., El-Rabaie, E. S. M., Dessouky, M. I., Alshebeili, S. A., & El-Samie, F. E. A. (2015). Automatic modulation recognition in wireless multi-carrier wireless systems with cepstral features. Wireless Personal Communications, 81(3), 1243–1288.CrossRef

19.

Kumar, Y., Jajoo, G., & Yadav, S. K. (2020). 2d-fft based modulation classification using deep convolution neural network (pp. 1–6). IEEE.

20.

Kumar, Y., Sheoran, M., Jajoo, G., & Yadav, S. K. (2020). Automatic modulation classification based on constellation density using deep learning. IEEE Communications Letters, 24(6), 1275–1278.CrossRef

21.

Mirarab, M. & Sobhani, M (2007). Robust modulation classification for psk/qam/ask using higher-order cumulants. In: 2007 6th International Conference on Information, Communications & Signal Processing, pp. 1–4. IEEE (2007)

22.

Nandi, A. K., & Azzouz, E. E. (1998). Algorithms for automatic modulation recognition of communication signals. IEEE Transactions on communications, 46(4), 431–436.CrossRef

23.

Osman, W. R. S., Nisar, K., & Altrad, A. M. (2014). Evaluation of broadband plc technology over malaysia’s indoor power line network. (pp. 275–280). https://doi.org/10.1109/ICED.2014.7015813

24.

Osman, W. R. S., Nisar, K., & Altrad, A. M. M. (2014). Demonstrate broadband over power line network in malaysia (pp. 1–6). China. https://doi.org/10.1109/ICCE-China.2014.7029864

25.

Peng, S., Jiang, H., Wang, H., Alwageed, H., Zhou, Y., Sebdani, M. M., & Yao, Y. D. (2018). Modulation classification based on signal constellation diagrams and deep learning. IEEE transactions on neural networks and learning systems, 99, 1–10.

26.

Popoola, J. J., & van Olst, R. (2013). The performance evaluation of a spectrum sensing implementation using an automatic modulation classification detection method with a universal software radio peripheral. Expert Systems with Applications, 40(6), 2165–2173.CrossRef

27.

Sethi, A. & Ray, B (2013). Blind carrier/timing recovery and detection of modulation scheme (2013). US Patent 8,605,830

28.

Wei, W., & Mendel, J. M. (2000). Maximum-likelihood classification for digital amplitude-phase modulations. IEEE Transactions on Communications, 48(2), 189–193.CrossRef

29.

Wong, M. D., & Nandi, A. K. (2004). Automatic digital modulation recognition using artificial neural network and genetic algorithm. Signal Processing, 84(2), 351–365.CrossRef

30.

Wong, M.D., Ting, S.K. & Nandi, A.K. (2008). Naive bayes classification of adaptive broadband wireless modulation schemes with higher order cumulants. In: Signal Processing and Communication Systems, 2008. ICSPCS 2008. 2nd International Conference on, pp. 1–5. IEEE

31.

Yadav, Y. K., Jajoo, G., & Yadav, S. K. (2017). Modulation scheme detection of blind signal using constellation graphical representation (pp. 231–235). IEEE.

32.

Yu, H., Shi, L., Qian, Y., Shu, F., Li, J., Zhao, Y., & Jayakody, D. N. K. (2017). A cooperative modulation recognition: New paradigm for power line networks in smart grid. Physical Communication, 25, 268–276.CrossRef

33.

Yucek, T., & Arslan, H. (2009). A survey of spectrum sensing algorithms for cognitive radio applications. IEEE communications Surveys & Tutorials, 11(1), 116–130.CrossRef

Title: Blind Signal Modulation Classification Using Constellation Pattern Analysis with Oversampling Factor Alteration
Authors: Yogesh Kumar
Gaurav Jajoo
Ashok Kumar
Sandeep Kumar Yadav
Publication date: 02-03-2022
Publisher: Springer US
Published in: Wireless Personal Communications / Issue 1/2022
Print ISSN: 0929-6212
Electronic ISSN: 1572-834X
DOI: https://doi.org/10.1007/s11277-022-09564-7

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 1/2022

Euler Phi Function and Gamma Function Based Elliptic Curve Encryption for Secured Group Communication

Dynamic S-Box and PWLCM-Based Robust Watermarking Scheme

A Review on Wireless Sensor Networks: Routing

PaSOFuAC: Particle Swarm Optimization Based Fuzzy Associative Classifier for Detecting Phishing Websites

Impact of Smart Power Efficient Modes on Multimedia Streaming Data Beyond 5G Networks

Deep Learning Approach for Encryption Techniques in Vehicular Networks