Modulation classification of mixed signals using fast independent component analysis

In military and civilian communications, signals are often interfered by hostile jamming or illegal transmission. In these situations, determining the modulation format of mixed signals is a challenging task, which is tackled using a three step algorithm named PFCC(PCA, FICA, Cumulants Based Classification Algorithm) in this paper. In the first step, centering and whitening is conducted using principal component analysis (PCA) to suppress noise. In the second step, mixed signals are separated using fast independent component analysis (FICA), which can transform received signal into components that are maximally independent from each other. In the third step, high-order cumulants (HOC) are calculated to determine the modulation format of each signal. Through extensive simulation, the convergence speed and performance of PFCC are validated. We also notice that the relative power of mixed signals has a big influence on performance.

[1]  Andrzej Cichocki,et al.  Adaptive blind signal and image processing , 2002 .

[2]  Takeshi Yamada,et al.  Estimation of the number of sound sources using support vector machines and its application to sound source separation , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[3]  Kiyohiro Shikano,et al.  Blind source separation based on a fast-convergence algorithm combining ICA and beamforming , 2006, IEEE Transactions on Audio, Speech, and Language Processing.

[4]  Hsiao-Chun Wu,et al.  Robust automatic modulation classification using cumulant features in the presence of fading channels , 2006, IEEE Wireless Communications and Networking Conference, 2006. WCNC 2006..

[5]  Zhifeng Yun,et al.  Novel Automatic Modulation Classification Using Cumulant Features for Communications via Multipath Channels , 2008, IEEE Transactions on Wireless Communications.

[6]  Masoud Zaerin,et al.  Modulation classification in the presence of interference , 2009, 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems.

[7]  Claudio R. C. M. da Silva,et al.  Maximum-Likelihood Classification of Digital Amplitude-Phase Modulated Signals in Flat Fading Non-Gaussian Channels , 2011, IEEE Transactions on Communications.

[8]  C. M. Sandiko,et al.  A Blind Source Separation of instantaneous acoustic mixtures using Natural Gradient Method , 2012, 2012 IEEE International Conference on Control System, Computing and Engineering.

[9]  Seungjin Choi,et al.  Independent Component Analysis , 2009, Handbook of Natural Computing.

[10]  Masoud Zaerin,et al.  Multiuser modulation classification based on cumulants in additive white Gaussian noise channel , 2012, IET Signal Process..

[11]  Mohamed Marey,et al.  Classification of Space-Time Block Codes Based on Second-Order Cyclostationarity with Transmission Impairments , 2012, IEEE Transactions on Wireless Communications.