In this study, an algorithm to blind and automatic modulation classification has been proposed. It well benefits combined machine leaning and signal feature extraction to recognize diverse range of modulation in low signal power to noise ratio (SNR). The presented algorithm contains four. First, it advantages spectrum analyzing to branching modulated signal based on regular and irregular spectrum character. Seconds, a nonlinear soft margin support vector (NS SVM) problem is applied to received signal, and its symbols are classified to correct and incorrect (support vectors) symbols. The NS SVM employment leads to discounting in physical layer noise effect on modulated signal. After that, a k-center clustering can find center of each class. finally, in correlation function estimation of scatter diagram is correlated with pre-saved ideal scatter diagram of modulations. The correlation outcome is classification result. For more evaluation, success rate, performance, and complexity in compare to many published methods are provided. The simulation prove that the proposed algorithm can classified the modulated signal in less SNR. For example, it can recognize 4-QAM in SNR=-4.2 dB, and 4-FSK in SNR=2.1 dB with %99 success rate. Moreover, due to using of kernel function in dual problem of NS SVM and feature base function, the proposed algorithm has low complexity and simple implementation in practical issues.
Self-Supervised Autoencoder Network for Robust Heart Rate Extraction from Noisy Photoplethysmogram: Applying Blind Source Separation to Biosignal Analysis
Mellowing Factory Co. Ltd.（梅洛灵工厂有限公司）；Chungnam National University Hospital（全南国立大学医院）；Department of Radiology, Seoul National University College of Medicine（首尔国立大学医学院放射科）
Phase-Aware Code-Aided EM Algorithm for Blind Channel Estimation in PSK-Modulated OFDM
Information and Communication Theory Lab, Eindhoven University of Technology, The Netherlands（信息与通信理论实验室，埃因霍温理工大学，荷兰）；NXP Semiconductors, Eindhoven, The Netherlands（NXP半导体，埃因霍温，荷兰）
Dualformer: Efficient Feature Extractor for Complex-valued Blind Communication Signal Analysis
College of Electronic Science and Technology, National University of Defense Technology（国防科技大学电子科学学院）；Department of Electrical and Computer Engineering, The University of Hong Kong（香港大学电气与电子工程系）
