Maximization of Nonlinear Autocorrelation for Blind Source Separation of Non-stationary Complex Signals

Abstract

Blind source separation of complex-valued signals has been a vital issue especially in the field of digital communication signal processing. This paper proposes a novel method based on nonlinear autocorrelation to solve the problem. Relying on the temporal structure with nonlinear autocorrelation of the signals, the method has a potential capability of extracting non-stationary complex sources with Gaussian or non-Gaussian distribution. Most traditional methods would fail in separating this kind of sources. We also analyze the stability conditions of the method in theory. Numerical simulations on artificial complex Gaussian data and orthogonal frequency division multiplexing sources corroborate the validity and efficiency of the proposed method. Moreover, with respect to classical methods, including cumulant-based approach using the non-stationarity of variance and complexity pursuit, our method offers equally good results with lower computational cost and better robustness. Finally, experiments for the separation of real communication signals illustrate that our method has good prospects in real-world applications.