Ivan A. Hashim , Prof. Dr. Jafar W. Abdul Sadah and Assist. Prof. Dr. Thamir R. Saeed

This paper presents the recognition of digital signal modulation by combining four cumulants as signal features and a threshold discriminator assigned for each cumulant. In this paper, the problem of recognition has been solved by finding a suitable relationship among these features and their classifier. The decision of each classifier has been compared with other classifier outputs. Then, the classifier with the highest priority has been considered as the final efficient decision. The priority of each classifier is based on how its cumulant is robust against a wide range of signal to noise ratio (SNR), for different types of digital modulations under investigation. Many types of quadrature amplitude modulation (16, 32, 64, 128, and 256) have been recognized with low SNR down to ¡5 dB. Using such combination, the average recognition percentage was increased with low SNR. The recognition gained 81.166%
100% for 4096 samples while the percentages are from 86.9% to 98.6% with a variable number of samples (256, 512, 1024, and 2048).