Independent Vector Analysis for Blind Deconvolving of Digital Modulated Communication Signals

For the purpose of overcoming the random permutation ambiguity of the frequency-domain-independent component analysis (FDICA) for blind separation of convolutive mixtures, this paper proposes an independent vector analysis (WA) detection receiver for blindly deconvolving the convolutive mixtures of digitally modulated signals for wireless communications. The foundation of IVA is through jointly carrying out separation work for different frequency bin data fusion, and the dependencies of frequency bins are exploited in solving the random permutation problem of separation signals. In addition, IVA uses multivariate prior distributions instead of the univariate distribution used in FDICA. Multivariate prior distribution is employed to preserve the interfrequency dependencies for individual sources, which can give rise to separation performance enhancement. Simulation results and analysis corroborate the effectiveness of the proposed detection method.

Automated summary

This paper proposes an independent vector analysis (WA) detection receiver for blindly deconvolving the convolutive mixtures of digitally modulated signals for wireless communications. The method jointly carries out separation work for different frequency bin data fusion, and solves the random permutation problem of separation signals by exploiting the dependencies of frequency bins. Simulation results and analysis demonstrate the effectiveness of the proposed detection method.