Comparing of K-Means, K-Medodis and Fuzzy C Means Cluster Method for Analog Modulation Recognition

Abstract

A modulation process is required to transmit analog signals with higher quality. Modulation is the process of transporting the signal by another carrier signal. This study aims to process analog signals. Using 200 samples of each of the six types of analog modulation modules. Nowadays these are Amplitude Modulation (AM), Double Side Band (DSB), Upper Side Band (USB), Lower Side Band (LSB), Frequency Modulation (FM) and Phase Modulation(PM) respectively. In the study an intelligent clustering method has been developed. The 5th level Discrete Wavelet Transform (DWT), Norm Entropy and Energy properties of AM, DSB, USB, LSB, FM and PM analog modulated signals have been removed during feature extraction phase. The results have been compared using K-Means, k-Medoid and Fuzzy C Mean (FCM) algorithms using a feature vector of 6x2x1200 obtained at the feature extraction stage and carrying out smart intelligent clustering for recognition. The most successful result has been obtained with FCM of 85.75%.00

Keywords

• Analog modulation,wavelet transform,entropy,energy,K- Means,k-Medoid

References

1. [1] Modulation techniques used in communication, https://slideplayer.biz.tr/slide/2683446[2] Erdem Yakut, S., (2007). An intelligent classification system based on wavelet transform in analog modulations, M.Sc. Thesis, F.Ü. Graduate School of Natural and Applied Sciences, Elazığ,[3] Guldemır, H., Sengur, A. (2006). Comparison of clustering algorithms for analog modulation classification. Expert Systems with Applications, 30(4), 642-649[4] Avcı, E., (2005). Intelligent radar target recognition system, Ph.D. Thesis, F.Ü. Graduate School of Natural and Applied Sciences, Elazığ,[5] Şengür, A., Türkoğlu, İ. (2003). Classifying Analogue Modulated Communication Signals Using Bayes Decision Criterion. Sakarya University Journal of Science, 7(3), 32-36.[6] Fidan, S., (2006). Modeling of the electromagnetic wave emitted in the waveguide by wavelet transformation, M.Sc. Institute of Science and Technology, Ankara.[7] Demren, E., 2015. Comparison of wavelet transform with fourier transform and its application, MS Thesis, İ.T.Ü. Institute of Science and Technology, Istanbul.[8] Işık, M., 2006. Data mining applications with partitioned clustering methods, M.Sc. Institute of Science and Technology, Istanbul.[9] Polat, H., Akin, M. ve Özerdem, M. S. (2017). The comparison of wavelet and empirical mode decomposition method in prediction of sleep stages from EEG signals. In Artificial Intelligence and Data Processing Symposium (IDAP), 2017 International (pp. 1-5). IEEE. [10] Avcı, E., Türkoğlu, İ. ve Poyraz, M., (2005). Intelligent Target Recognition Based on Wavelet Adaptive Network Based Fuzzy Inference System, Lecture Notes in Computer Science, Springer-Verlag , 3522, 594-601.[11] Arslan, Ö., (2014). Investigating the most appropriate main wavelet function for Turkish phonemes, M.Sc. Institute of Science and Technology, İzmir.Gray, R. M., (1990). Entropy and information. In Entropy and Information Theory, Publisher: Springer, (pp. 21-55) New York, ISBN-13: 978-1441979698.[12] Coifman, R.R. ve Wickerhauser, M.V., (1992). Entropy based algorithms for best basis selection, IEEE Transaction on Information Theory, 38, 2, 713-718, 1992].[13] Çokgüngördü, A., (2017). Base stations with the help of clustering method, using the assisted site, M.Sc. Institute of Science and Technology, Istanbul.[14] Atal, S., (2015). Fuzzy clustering analysis and clustering of OECD countries in terms of development, Master Thesis, O.G.Ü. Graduate School of Natural and Applied Sciences, Eskişehir.[15] Çağlar, B., (2018). Evaluation of spatial data by clustering analysis, Master Thesis, N.E.Ü. Institute of Science and Technology, Konya.[16] Kırmızıgül Çalışkan S., (2008). K.KNN: detection of penetration in networks by clustering and closest neighboring method, Master Thesis, G.Y.T.E. Institute of Engineering and Science, Gebze.[17] Taşova, O., (2011). Face recognition with artificial neural networks, MS Thesis, D.E.Ü. Institute of Science and Technology, İzmir.[18] Eset, K., (2016). Investigation of segmentation methods in lung pet images, M.Sc. Institute of Science and Technology, Kayseri.[19] Alper, A.T., (2010). Analog communication, Lecture Notes, M.Ü. Faculty of Engineering, Mersin.[20] Saygili, A., ve Albayrak, S. (2017). Meniscus segmentation and tear detection in the knee MR images by fuzzy c-means method. In Signal Processing and Communications Applications Conference (SIU), 2017 25th (pp. 1-4). IEEE.[21] MATLAB Company, 2018