Journal of Systems Engineering and Electronics ›› 2021, Vol. 32 ›› Issue (4): 799-810.doi: 10.23919/JSEE.2021.000069

• ELECTRONICS TECHNOLOGY • Previous Articles     Next Articles

Automatic modulation classification using modulation fingerprint extraction

Jafar NOROLAHI, Paeiz AZMI*(), Farzaneh AHMADI()   

  1. 1 Electrical and Engineering Department, Tarbiat Modares University, Tehran 14115, Iran
  • Received:2020-04-29 Online:2021-08-18 Published:2021-09-30
  • Contact: Paeiz AZMI;
  • About author:|NOROLAHI Jafar was born in 1985. He received his M.S. degree in communication engineering from Tarbiat Modares University, Tehran, Iran, in 2015. Since 2012, he has been a researcher with the telecommunication research centre in Tehran. His current research is machine learning implementation in the 5th generation cellular network technology. His research interests include wireless communications, signal processing, antenna array, MIMO, and machine learning.E-mail:||AZMI Paeiz was born in 1974. He received his B.S., M.S., and Ph.D. degrees in electrical engineering from Sharif University of Technology (SUT) in 1996, 1998, and 2002, respectively. Since September 2002, he has been with the Electrical and Computer Engineering Department of Tarbiat Modares University, where he became an associate professor on January 2006 and he is a full professor now. From 1999 to 2001, he was with the Advanced Communication Science Research Laboratory, Iran Telecommunication Research Centre (ITRC). From 2002 to 2005, he was with the Signal Processing Research Group at ITRC. He is a senior member of IEEE. His current research interests include modulation and coding techniques, digital signal processing, wireless communications, resource allocation, molecular communications, and estimation and detection theories.E-mail:||AHMADI Farzaneh was born in 1988. She received her master degree in the field of telecommunication engineering from Tarbiat Modares University, Tehran, Iran. She is a lecturer and researcher. Her research interests are radio frequency, microwave and antenna design. E-mail:


An automatic method for classifying frequency shift keying (FSK), minimum shift keying (MSK), phase shift keying (PSK), quadrature amplitude modulation (QAM), and orthogonal frequency division multiplexing (OFDM) is proposed by simultaneously using normality test, spectral analysis, and geometrical characteristics of in-phase-quadrature (I-Q) constellation diagram. Since the extracted features are unique for each modulation, they can be considered as a fingerprint of each modulation. We show that the proposed algorithm outperforms the previously published methods in terms of signal-to-noise ratio (SNR) and success rate. For example, the success rate of the proposed method for 64-QAM modulation at SNR=11 dB is 99%. Another advantage of the proposed method is its wide SNR range; such that the probability of classification for 16-QAM at SNR=3 dB is almost 1. The proposed method also provides a database for geometrical features of I-Q constellation diagram. By comparing and correlating the data of the provided database with the estimated I-Q diagram of the received signal, the processing gain of 4 dB is obtained. Whatever can be mentioned about the preference of the proposed algorithm are low complexity, low SNR, wide range of modulation set, and enhanced recognition at higher-order modulations.

Key words: automatic modulation classification, in-phase-quadrature (I-Q) constellation diagram, spectral analysis, feature based modulation classification