Adaptive MIMO Reduced-Rank Equalization Based on Joint Iterative Least Squares Optimization of Estimators

Abstract

This paper presents a novel adaptive reduced-rank multi-input-multi-output (MIMO) linear equalization structure based on joint iterative optimization of adaptive filters. The proposed reduced-rank linear equalization structure consists of a joint iterative optimization of two equalization stages, namely, a projection matrix that performs dimensionality reduction and a reduced-rank linear equalization filter that retrieves the desired transmitted symbol. The novel linear reduced-rank structure is responsible for cancelling the inter- antenna interference caused by the associated data streams and exploiting the available degrees of freedom at the antenna-array receiver. We describe least squares (LS) expressions for the design of the projection matrix and the reduced-rank filter along with computationally efficient recursive least squares (RLS) adaptive estimation algorithms. Simulations for a MIMO linear equalization application show that the proposed scheme outperforms the state-of-the-art reduced-rank and the conventional estimation algorithms at about the same complexity.