Improving the Classification of MPSK and MQAM Modulations by using Optimized Nonlinear Preprocess in Flat Fading Channels

Kadoun, I.; Khaleghi, H.

doi:10.22061/jecei.2022.8743.550

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	Improving the Classification of MPSK and MQAM Modulations by using Optimized Nonlinear Preprocess in Flat Fading Channels
Journal of Electrical and Computer Engineering Innovations (JECEI)
دوره 11، شماره 1، فروردین 2023، صفحه 141-152 اصل مقاله (1.21 M)
نوع مقاله: Original Research Paper
شناسه دیجیتال (DOI): 10.22061/jecei.2022.8743.550
نویسندگان
I. Kadoun؛ H. Khaleghi^*
Electrical and Computer Engineering Department, Malek Ashtar University of Technology, Tehran, Iran.
تاریخ دریافت: 22 فروردین 1401، تاریخ بازنگری: 24 تیر 1401، تاریخ پذیرش: 08 شهریور 1401
چکیده
Background and Objectives: Intelligent receivers, automatically detect the digital modulation type of the received signals for demodulation purposes where is well known as Automatic Modulation Classification (AMC) module. The performance of AMC algorithms depends on the channel conditions where for example, in fading channel its performance gets worse than the AWGN channel. Methods: We propose a new algorithm for improving the AMC classification accuracy in flat fading channels. The proposed algorithm consists of an optimizable nonlinear preprocess followed by Linear Discriminant Analysis (LDA) technique. Two Lemmas have been found for extracting the optimization rule. And an optimization algorithm has been built based on the previous Lemmas. Results: The simulation results show that the proposed algorithm improves the classification accuracy between 8-Phase Shift Keying (8PSK) and 16PSK (as an example of M-array PSK (MPSK) inter-class) for Signal-to-noise ratio (SNR) values greater than 13 dB, and between 16- quadrature amplitude shift modulation (16QAM) and 64QAM (as an example of M-array QAM (MQAM) inter-class) for SNR values greater than 4 dB. Conclusion: By using the proposed optimization algorithm, the AMC classification accuracy has been improved. Other classification problems can use this algorithm. And other nonlinear preprocess functions or optimization algorithms may be found in future work.
کلیدواژه‌ها
Automatic Modulation Classification؛ Linear Discriminant Analysis؛ Higher-Order Cumulants؛ Mahalanobis Distance

مراجع
[1] S. A. Ghauri, I. M. Qureshi, A. Aziz, T. A. Cheema, "Classification of digital modulated signals using linear discriminant analysis on faded channel," World App. Sci. J., 29(10): 1220-1227, 2014. [2] O. A. Dobre, A. Abdi, Y. Bar-Ness, W. J. W. P. C. Su, "Cyclostationarity-based modulation classification of linear digital modulations in flat fading channels," Wirel. Pers. Commun., 54(4): 699-717, 2010. [3] O. A. Dobre, A. Abdi, Y. Bar-Ness, W. Su, "Survey of automatic modulation classification techniques: classical approaches and new trends," IET commun., 1(2): 137-156, 2007. [4] O. Dobre, F. Hameed, "Likelihood-based algorithms for linear digital modulation classification in fading channels," in Proc. 2006 Canadian Conference on Electrical and Computer Engineering: 1347-1350, 2006. [5] A. Hazza, M. Shoaib, S. A. Alshebeili, A. Fahad, "An overview of feature-based methods for digital modulation classification," in Proc. 2013 1st International Conference on Communications, Signal Processing, and their Applications (ICCSPA): 1-6, 2013. [6] X.R. Jiang, H. Chen, Y.D. Zhao, W. Q. J. I. J. o. E. Wang, "Automatic modulation recognition based on mixed-type features," Int. J. Electron., 108(1): 105-114, 2021. [7] D.-C. Chang and P.-K. J. I. C. Shih, "Cumulants-based modulation classification technique in multipath fading channels," IET Commun., 9(6): 828-835, 2015. [8] P. S. Thakur, S. Madan, M. Madan, "Trends in automatic modulation classification for advanced data communication networks," Int. J. Adv. Res. Comput. Eng. Technol. (IJARCET), 4(2): 496-507, 2015. [9] Y. Wei, S. Fang, X. Wang, "Automatic modulation classification of digital communication signals using SVM based on hybrid features, cyclostationary, and information entropy," Entropy, 21(8): 745, 2019. [10] Y. Kumar, M. Sheoran, G. Jajoo, S. K. Yadav, "Automatic modulation classification based on constellation density using deep learning," IEEE Commun. Lett., 24(6): 1275-1278, 2020. [11] X. Zhang, J. Sun, X. Zhang, "Automatic modulation classification based on novel feature extraction algorithms," IEEE Access, 8: 16362-16371, 2020. [12] D. H. Al-Nuaimi, I. A. Hashim, I. S. Zainal Abidin, L. B. Salman, N. A. Mat Isa, "Performance of feature-based techniques for automatic digital modulation recognition and classification—A review," Electronics, 8(12): 1407, 2019. [13] S. Sobolewski, W. L. Adams, and R. Sankar, "Universal nonhierarchical automatic modulation recognition techniques for distinguishing bandpass modulated waveforms based on signal statistics, cumulant, cyclostationary, multifractal and Fourier-wavelet transforms features," in 2014 IEEE Military Communications Conference: 748-753, 2014. [14] A. Smith, M. Evans, J. Downey, "Modulation classification of satellite communication signals using cumulants and neural networks," in Proc. 2017 Cognitive Communications for Aerospace Applications Workshop (CCAA): 1-8, 2017. [15] I. KADOUN, H. K. BIZAKI, "Advanced features generation algorithm for MPSK and MQAM classification in flat fading channel," Radioengineering, 31(1): 127, 2022. [16] V. D. Orlic, M. L. Dukic, "Automatic modulation classification algorithm using higher-order cumulants under real-world channel conditions," IEEE Commun. Lett., 13(12): 917-919, 2009. [17] A. E. Abdelmutalab, "Learning-based automatic modulation classification," 2015. [18] S. A. Ghauri, I. M. Qureshi, A. N. Malik, T. A. Cheema, "Higher order cummulants based digital modulation recognition scheme," Res. J. Appl. Sci. Eng. Tech., 6(20): 3910-3915, 2013. [19] A. Abdelmutalab, K. Assaleh, M. J. P. C. El-Tarhuni, "Automatic modulation classification based on high order cumulants and hierarchical polynomial classifiers," Phys. Commun., 21: 10-18, 2016. [20] A. Tharwat, T. Gaber, A. Ibrahim, A. E. Hassanien, "Linear discriminant analysis: A detailed tutorial," AI Commun., 30(2): 169-190, 2017. [21] B. Ghojogh, F. Karray, M. Crowley, "Fisher and kernel Fisher discriminant analysis: Tutorial," arXiv Prepr. arXiv1906.09436, 2019. [22] A. Lahav, R. Talmon, Y. J. I. Kluger, I. A. J. o. t. IMA, "Mahalanobis distance informed by clustering," Inf. Inference A J. IMA., 8(2): 377-406, 2019. [23] C. D. Meyer, Matrix analysis and applied linear algebra, 71. Siam, 2000. [24] G. Strang, "Introduction to linear algebra Fifth," ed: Wellesley-Cambridge Press, 2016. [25] A. C. Atkinson, M. Riani, A. J. S. S. Corbellini, "The Box–Cox transformation: Review and extensions," Stat. Sci., 36(2): 239-255, 2021. [26] A. H. Namin, K. Leboeuf, R. Muscedere, H. Wu, M. Ahmadi, "Efficient hardware implementation of the hyperbolic tangent sigmoid function," in Proc. 2009 IEEE International Symposium on Circuits and Systems: 2117-2120, 2009. [27] D. Cai, X. He, J. Han, "SRDA: An efficient algorithm for large-scale discriminant analysis," IEEE Trans. Knowl. Data Eng., 20(1): 1-12, 2007. [28] A. Rupp, J. Pelzl, C. Paar, M. Mertens, A. Bogdanov, "A parallel hardware architecture for fast Gaussian elimination over GF (2)," in Proc. 2006 14th Annual IEEE Symposium on Field-Programmable Custom Computing Machines: 237-248, 2006. [29] L. Zhao, W. Lin, Y. Wang, X. Li, "Recursive local summation of rx detection for hyperspectral image using sliding windows," Remote Sens., 10(1): 103, 2018. [30] X. Li, S. Wang, Y. Cai, "Tutorial: Complexity analysis of Singular Value Decomposition and its variants," arXiv paper. arXiv1906.12085, 2019.
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Improving the Classification of MPSK and MQAM Modulations by using Optimized Nonlinear Preprocess in Flat Fading Channels