Performance of cumulant based inverse filter criteria for blind deconvolution of multi-input multi-output linear time-invariant systems

Tugnait (1997), and Chi and Chen proposed multi-input multi-output inverse filter criteria (MIMO-IFC) using higher-order statistics for blind deconvolution of multi-input multi-output (MIMO) linear time-invariant (LTI) systems. This paper proposes a performance analysis for the MIMO linear equalizer associated with MIMO-IFC for finite SNR, including (P1) perfect phase equalization property, (P2) a relation to MIMO minimum mean square error (MIMO-MMSE) equalizer, and (P3) a connection with the one obtained by Yeung and Yau's MIMO super-exponential algorithm (MIMO-SEA) that usually converges fast but has no guarantee of convergence for finite data. Furthermore, based on (P3), a MIMO-IFC based algorithm with performance similar to that of the MIMO-SEA and with guaranteed convergence is proposed. Finally, some simulation results are presented to support the analytic results and the proposed algorithm.