LAUSR

To improve the deteriorated capacity gain and source recovery performance due to channel mismatch problem, this paper reports a research about blind separation method against channel mismatch in multiple-input multiple-output (MIMO) systems. The channel mismatch problem can be described as a channel with bounded fluctuant errors due to channel distortion or channel estimation errors. The problem of blind signal separation/extraction with channel mismatch is formulated as a cost function of blind source separation (BSS) subject to the second-order cone constraint, which can be called as second-order cone programing optimization problem. Then the resulting cost function is solved by approximate negentropy maximization using quasi-Newton iterative methods for blind separation/extraction source signals. Theoretical analysis demonstrates that the proposed algorithm has low computational complexity and improved performance advantages. Simulation results verify that the capacity gain and bit error rate (BER) performance of the proposed blind separation method is superior to those of the existing methods in MIMO systems with channel mismatch problem.