Automatic Modulation Recognition of Digital Signals using Wavelet Features and SVM
Cheol-Sun Park, Jun‐Ho Choi, Sun‐Phil Nah, Won Jang, Dae Young Kim · International Conference on Advanced Communication Technology · 2008
This paper presents modulation classification method capable of classifying incident digital signals without a priori information using WT key features and SVM. These key features for modulation classification should have good properties of sensitive with modulation types and insensitive with SNR variation. In this paper, the 4 key features using WT coefficients, which have the property of insensitive to the changing of noise, are selected. The numerical simulations using these features are performed. We investigate the performance of the SVM-DDAG classifier for classifying 8 digitally modulated signals using only 4 WT key features (i.e., 4 level scale), and compare with that of decision tree classifier to adapt the modulation classification module in software radio. Results indicated an overall success rate of 95% at the SNR of 10dB in SVM-DDAG classifier on an AWGN channel.