Multi-step linear predictors-based blind equalization of multiple-input multiple-output channels

Blind equalization of MIMO (multiple-input multiple-output) communications channels is considered using primarily the second-order statistics of the data. In several applications the underlying equivalent discrete-time mathematical model is that of a MIMO linear system where the number of inputs equals the number of users (sources) and the number of outputs is related to the number of sensors and the sampling rate. Previously we investigated the structure of multi-step linear predictors for IIR/FIR MIMO systems with irreducible transfer functions and derived an upper bound on its length (Tugnait 1998). In the past multi-step linear predictors have been considered in the literature only for single-input multiple-output models. In this paper we apply the results of Tugnait (1998) for blind equalization of MIMO channels using MMSE linear equalizers. Extensions to the case where the “subchannel” transfer functions have common zeros/factors is also investigated. An illustrative simulation example is provided