Automatic Modulation Recognition Based on Multi-Dimensional Feature Extraction

As an intermediate step between signal detection and demodulation, automatic modulation recognition (AMR) is commonly used in cognitive radio networks to identify different types of communication modulation. A new automatic modulation scheme is proposed, based on decision tree theory, which is a general method for different types of band-limited Gaussian noise modulation types. In particular, by combining the instantaneous statistic feature and high-order cumulants feature, the key features are extracted to realize the blind recognition of analog and digital signals. In addition, a new characteristic parameter AT is proposed to improve the performance of modulation recognition under low signal-to-noise ratio (SNR). The simulation results show that, for all the analyzed signals, when the SNR reaches 3dB, the recognition success rate can reach more than 95%, reflecting the superiority of the method proposed in this paper.

[1]  Mohammed Amine Azza,et al.  Implementation of an Automatic Modulation Recognition System on a Software Defined Radio Platform , 2018, 2018 International Symposium on Advanced Electrical and Communication Technologies (ISAECT).

[2]  Octavia A. Dobre,et al.  Signal identification for emerging intelligent radios: classical problems and new challenges , 2015, IEEE Instrumentation & Measurement Magazine.

[3]  Badreldeen Ismail Dahap,et al.  Advanced algorithm for automatic modulation recognition for analogue & digital signals , 2015, 2015 International Conference on Computing, Control, Networking, Electronics and Embedded Systems Engineering (ICCNEEE).

[4]  Wang Tingting,et al.  Identification of cognitive radio modulation , 2011, 2011 International Conference on Mechatronic Science, Electric Engineering and Computer (MEC).

[5]  Eleftherios Kofidis,et al.  Preamble-Based Estimation of Highly Frequency Selective Channels in FBMC/OQAM Systems , 2015, IEEE Transactions on Signal Processing.

[6]  Xin Zhang,et al.  Modulation Recognition of Underwater Acoustic Communication Signals Based on Joint Feature Extraction , 2019, 2019 IEEE International Conference on Signal, Information and Data Processing (ICSIDP).

[7]  Nandi K. Nandi,et al.  Automatic analogue modulation recognition , 1995, Signal Process..

[8]  Alagan Anpalagan,et al.  SVM-based classification of digital modulation signals , 2010, 2010 IEEE International Conference on Systems, Man and Cybernetics.

[9]  Hua Wu,et al.  Feature extraction for complicated radar PRI modulation modes based on auto-correlation function , 2016, 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC).

[10]  M.R. Mirarab,et al.  Robust modulation classification for PSK /QAM/ASK using higher-order cumulants , 2007, 2007 6th International Conference on Information, Communications & Signal Processing.

[11]  Xudong Liu,et al.  A modulation recognition method based on carrier frequency estimation and decision theory , 2010, 2010 16th Asia-Pacific Conference on Communications (APCC).

[12]  Rui Ma,et al.  Digital Signal Modulation Recognition Algorithm Based on VGGNet Model , 2019, 2019 IEEE 5th International Conference on Computer and Communications (ICCC).

[13]  Yangqiang Yang,et al.  A Method for Digital Modulation Recognition Based on Mixed Signal Features , 2019, 2019 International Conference on Electronic Engineering and Informatics (EEI).

[14]  Rong Li,et al.  Research on Digital Signal Recognition Based on Higher Order Cumulants , 2019, 2019 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS).

[15]  Yong Zhao,et al.  Recognition of digital modulation signals based on high-order cumulants , 2015, 2015 International Conference on Wireless Communications & Signal Processing (WCSP).

[16]  A Sheykhi,et al.  A NEW METHOD FOR COMMUNICATION SYSTEM RECOGNITION , 2006 .

[17]  Yang Yangqiang,et al.  A Modified Method for Digital Modulation Recognition based on Instantaneous Signal Features , 2019, 2019 3rd International Conference on Electronic Information Technology and Computer Engineering (EITCE).

[18]  Xiaolin Zhang,et al.  Automatic Modulation Recognition of Communication Signals Based on Instantaneous Statistical Characteristics and SVM Classifier , 2018, 2018 IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP).