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

Year 2019, Volume: 7 Issue: 3, 294 - 299, 30.07.2019

Yusuf Kaya Derya Avci Mehmet Gedikpınar

https://doi.org/10.17694/bajece.564960

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

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