Design and development of robust automated modulation recognition
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Date
2019-06
Authors
Siti Nur Izzati Binti Ismail
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Abstract
Automatic Modulation Classification (AMC) is the process of automatically identifying the modulation type of an unknown intercepted signal, Through many years, a lot of studies had been conducted to look for the alternative for the improvement of classification accuracy of the AMR system. However, there is no research about the effect the varying the delay tap time between each sample pair and the sampling time for the ADTS. Thus, this project will study the effect of varying the delay tap time between each sample pair and the sampling time for ADTS also improve percentage of modulation accuracy of system using MATLAB and DoE method. In this project, asynchronous delay tap sampling (ADTS) is proposed as a technique in modulation classification. From the ADTS, unique and distinct asynchronous delay tap plot (ADTP) is generated for each of the QPSK and 16-QAM digital modulated signal. There are two types of channel involved in this project, AWGN and Rician channel. These data are then reconstructed to become the input of a built-in support vector machine (SVM) classifier in MATLAB. Design of experiment (DoE) method is applied to improve the accuracy of the AMR system. In DoE, 2 2 factorial design method is applied. The results of the classification showed that the accuracy of the classifier is accuracy for this classifier is increase from 93.1% to 96.0% for AWGN and from 91.5% to 94.5% for Rician. There is an increase in accuracy before DoE is applied. This shows an improvement in the accuracy of the AMR system by using the DoE method. In conclusion, the proposed techniques are able to improve the accuracy of the AMR system.