Deteksi Tipe Modulasi Digital Pada Automatic Modulation Recognition Menggunakan Support Vector Machine dan Conjugate Gradient Polak Ribiere-Backpropagation
Abstract
This research uses randomly generated digital data to detect modulation types. The types of modulation are tested QPSK, 16-QAM, and 64-QAM.In the characteristic extraction process uses a statistical feature set approach with the mean, varian, kurtosis and skewness, while feature selection uses Multi-Class Support Vector Machine(SVM) with 5 classes in the classification including (i)Not a feature, (ii)Mean, (iii)Varian, (iv)Kurtosis and (v)Skewness.In detecting the type of modulation in this study using Backpropagation Artificial Neural Networks by learning process used the Conjugate Gradient Polak Ribiere algorithm. This study, researcher also compared the learning with Conjugate Gradient Polak Ribiere and learning using Gradient Discent. The results of the comparison learning was 401 training data using Conjugate Gradient Polak Ribiere much better with a higher accurancy value of 86,20% and lower error rate of 13,80% in the iteration to 781, while the Gradient Discent in the same iteration the accuracy rate is 67,83% and the error rate is 32,17%. From the test results there are 4 feature groups that was able to recognize the type of modulation including (i) Mean,Variant,Kurtosis, (ii)Mean, Variant, Skewness, (iii)Variant, Kurtosis, Skewness and (iv)Mean, Kurtosis, Skewness.
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References
[2] Afif Dias Pambudi, Suhartono T, Heroe Wijanto, “Deteksi Automatis Skema Modulasi Sinyal OFDM Menggunakan Ciri Statistik dan Klasifikasi PSO”, Jurnal ELKOMIKA, 2015.
[3] Azminuddin I.S Azis, Vincent Suharto, H.Himawan,”Model Multi-Class SVM menggunakan strategi 1V1 Untuk Klasifikasi Wall-Following Robot Navigation Data”, Jurnal Teknologi Informasi, Volume 13 Nomor 2, 2017
[4] B. Gou and X. W. Huang, “SVM multi-class classification [J],” Journal of Southern Yangtze University, Vol. 21, pp. 334–339, September 2006.
[5] Christian Dwi Suhendra, Retantyo Wardoyo,”Penentu Arsitektur Jaringan Syaraf Tiruan Backpropagation (Bobot Awal dan Bias Awal) Menggunakan Algoritma Genetika”. IJCC Vol.9, No.1., 2015
[6] Dickie Z.H, Heroe Wijanto, Afief Dias P, “Analisis Deteksi Skema Modulasi Digital Single Carrier Dan Multi Carrier Pada Kanal Fading Dan AWGN”, e-Proceeding of Enginering: Vol.1,No.1, 2014
[7] H. Mustafa and M. Doroslovacki, “Digital modulation recognition using support vector machine classifier [C],” Proceedings of The Thirty-Eighth Asilomar Conference on Signals, Systems & Computers, 2004.
[8] K. Nandi and E. E. Azzouz, “Automatic modulation recognition [J],” Signal Processing, Vol. 46, No. 2, pp. 211– 222, 1995.
[9] M.H Valipour, M.Mehdi, “Automatic Digital Modulation Recognition in Presentace of Noise Using SVM and PSO ”, 6’th International Symposium on Telecommunication (IST 2012),2012,
[10] M. Cilimkovic, “Neural Networks and Back Propagation Algorithm,” Fett.Tu-Sofia.Bg, 2010
[11] R.R Kharhe, Jyoti P.Bari, Neha M, “Automatic Modulation Recognition for digital Communication Signal”, Proceedings Of International Conference On Modeling And Simulation In Engineering & Technology (ICMSET-2014), 2014.
[12] Sahyar, “Algoritma & Pemrograman Menggunakan MATLAB (Matrix Laboratory)”, Kencana, Jakarta, 2016.
[13] Utari N.Wisesty, Adiwijaya, Tjokorda Agung B.W, “Algoritma Conjugate Gradient Polak Ribiere Untuk Meningkatkan Performasi Backpropagation Pada Sistem Prediksi Temperatur Udara”, Jurnal Penelitian dan Pengembangan Telekomunikasi Vol.15, 2010.
[14] Wahyu Setiawan, Kusworo Adi, Aris Sugiharto, “Sistem Deteksi Retinopati Diabetik Menggunakan Support Vector Machine”, http://ejournal.undip.ac.id/index.php/jsinbis, 2012.
[15] W. C. Han, H. Han, L. N. Wu, et al., “A 1-dimension structure adaptive self-organizing neural network for QAM signal classification [C],” Third International Conference on Natural Computation (ICNC 2007), HaiKou, August 24–27, 2007.
[16] X. Z. Feng, J. Yang, F. L. Luo, J. Y. Chen, and X. P. Zhong, “Automatic modulation recognition by support vector machines using wavelet kernel [J],” Journal of Physics, International Symposium on Instrumentation Science and Technology, pp. 1264–1267, 2006.
[17] Z. L. Wu, X. X. Wang, Z. Z. Gao, and G. H. Ren, “Automatic digital modulation recognition based on support vector machine [C],” IEEE Conference on Neural Networks and Brain, pp. 1025–1028, 2005.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International License
