Digital Modulation Classification Based On BAT Swarm Optimization and Random Forest

Authors

  • Batool abd alhadi sultan College of Science, Department of computer University of Diyala, Diyala, Iraq
  • Taha Mohammed Hasan College of Science, Department of computer University of Diyala, Diyala, Iraq

DOI:

https://doi.org/10.31642/JoKMC/2018/070104

Keywords:

Classification, Bat Swarm Optimization Algorithm, Random Forests, Automatic Modulation Identification

Abstract

The applications of digitally modulated signals are still in progress and expansion. Automatic Modulation Identification (AMI) is important to classify the digitally modulated signals ..To get better results of the system suggested optimization the features to discard weak or irrelevant features in the system and keep only strong relevant features .In this work, present hybrid intelligent system for the recognition related to the digitally modulated signals where used . The proposed (AMI) had been built to classify ten most popular schemes of digitally modulated signals, namely (2ASK, , 2PSK, 4PSK, 8PSK, 8QAM,16QAM ,32QAM, 64 QAM, 128QAM, and 256QAM), with the signal to noise ratio ranging from (-2 to 13) dB. High-order cumulants (HOCs) as well as high-order moments (HOMs) were utilized. .In this thesis used , Bat Swarm Optimization (BA).The Random Forest ( RF) classifier was introduced for the first time in this work. Simulation results of the System proposed , under additive white Gaussian noise channel, show that .While algorithm ( BA Swarm Optimization ) for the modulated signals we obtained a classification accuracy of around 92% for the SNR between (-2....12 ) dB.

Downloads

Download data is not yet available.

References

M. Azarbad, S. Hakimi, and A. Ebrahimzadeh, “Automatic recognition of digital communication signal,” Int. J. energy, Inf. Commun., vol. 3, no. 4, pp. 21–34, 2012.

W. Su, J. L. Xu, and M. Zhou, “Real-time modulation classification based on maximum likelihood,” IEEE Commun. Lett., vol. 12, no. 11, pp. 801–803, 2008. DOI: https://doi.org/10.1109/LCOMM.2008.081107

P. K. HL and L. Shrinivasan, “Automatic digital modulation recognition using minimum feature extraction,” in 2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom), 2015, pp. 772–775.

S. Almaspour and M. R. Moniri, “Automatic modulation recognition and classification for digital modulated signals based on ANN algorithms,” nine, vol. 3, no. 12, 2016.

S. Rajendran, W. Meert, D. Giustiniano, V. Lenders, and S. Pollin, “Deep learning models for wireless signal classification with distributed low-cost spectrum sensors,” IEEE Trans. Cogn. Commun. Netw., vol. 4, no. 3, pp. 433–445, 2018. DOI: https://doi.org/10.1109/TCCN.2018.2835460

M. Bellanger and M. Bellanger, “Adaptive digital filters,” 2001. DOI: https://doi.org/10.1201/9780203903841

J. Bagga and N. Tripathi, “Automatic modulation classification using statistical features in fading environment,” Int. J. Adv. Res. Electr. Electron. Instrum. Eng., vol. 2, no. 8, pp. 3701–3709, 2013.

S. B. Sadkhan-Smieee, A. Q. Hameed, and H. A. Hamed, “Digitallymodulated signals recognition based on adaptive neural-fuzzy inference system (ANFIS),” Int. J. Adv. Comput. Technol., vol. 7, no. 5, p. 57, 2015.

C. Qu, S. Zhao, Y. Fu, and W. He, “Chicken swarm optimization based on elite opposition-based learning,” Math. Probl. Eng., vol. 2017, 2017. DOI: https://doi.org/10.1155/2017/2734362

A. Liaw and M. Wiener, “Classification and regression by randomForest,” R news, vol. 2, no. 3, pp. 18–22, 2002.

A. Liaw and M. Wiener, “Classification and regression by randomForest. R News. 2002; 2: 18–22.” 2016.

S. Revathi

Downloads

Published

2020-09-19

How to Cite

sultan, B., & Hasan, T. (2020). Digital Modulation Classification Based On BAT Swarm Optimization and Random Forest. Journal of Kufa for Mathematics and Computer, 7(1), 26–30. https://doi.org/10.31642/JoKMC/2018/070104

Similar Articles

1 2 3 4 5 6 > >> 

You may also start an advanced similarity search for this article.