Blind MIMO channel identification from second order statistics using rank deficient channel convolution matrix

Abstract

For multiuser systems, several direct blind identification algorithms require that the linear multiple-input multiple-output (MIMO) system have a full rank convolution matrix. This condition requires that the system transfer function be irreducible and column reduced. We show that this restrictive identification condition can be relaxed for some direct blind identification methods to accommodate more practical scenarios. Algorithms such as the outer-product decomposition algorithm only require minor length adjustment to its processing window without the column-reduced condition. This result allows direct blind identification methods to be applicable to MIMO without requiring a full-rank channel convolution matrix.