Automatic modulation classification using modulation fingerprint extraction

• 作者: NOROLAHI Jafar AZMI Paeiz AHMADI Farzaneh
• 来源期刊: 系统工程与电子技术（英文版）
• 年，卷（期）: 2021,（4）
• 所属期刊栏目: ELECTRONICS TECHNOLOGY
• 页码范围: 799-810
• 页数: 12页
• 语种: 英文
• DOI: 10.23919/JSEE.2021.000069

摘要

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 simulta-neously using normality test, spectral analysis, and geometrical characteristics of in-phase-quadrature (I-Q) constellation dia-gram. Since the extracted features are unique for each modula-tion, they can be considered as a fingerprint of each modulation. We show that the proposed algorithm outperforms the pre-viously published methods in terms of signal-to-noise ratio (SNR) and success rate. For example, the success rate of the pro-posed 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 pro-cessing gain of 4 dB is obtained. Whatever can be mentioned about the preference of the proposed algorithm are low com-plexity, low SNR, wide range of modulation set, and enhanced recognition at higher-order modulations.