Adaptive Reduced-Rank Equalization Algorithms Based on Alternating Optimization Design Techniques for Multi-Antenna Systems

TL;DR

This paper introduces a new adaptive reduced-rank MIMO equalization scheme using alternating optimization, which improves performance over existing methods with efficient algorithms and automatic model order adjustment.

Contribution

It proposes a novel joint iterative reduced-rank equalization architecture with alternating optimization and adaptive algorithms for MIMO systems.

Findings

Outperforms existing reduced-rank and full-rank algorithms in simulations.

Provides computationally efficient adaptive estimation algorithms.

Includes an automatic model order adjustment algorithm.

Abstract

This paper presents a novel adaptive reduced-rank {multi-input multi-output} (MIMO) equalization scheme and algorithms based on alternating optimization design techniques for MIMO spatial multiplexing systems. The proposed reduced-rank equalization structure consists of a joint iterative optimization of two equalization stages, namely, a transformation matrix that performs dimensionality reduction and a reduced-rank estimator that retrieves the desired transmitted symbol. The proposed reduced-rank architecture is incorporated into an equalization structure that allows both decision feedback and linear schemes for mitigating the inter-antenna and inter-symbol interference. We develop alternating least squares (LS) expressions for the design of the transformation matrix and the reduced-rank estimator along with computationally efficient alternating recursive least squares (RLS) adaptive estimation algorithms. We then present an algorithm for automatically adjusting the model order of the proposed scheme. An analysis of the LS algorithms is carried out along with sufficient conditions for convergence and a proof of convergence of the proposed algorithms to the reduced-rank Wiener filter. Simulations show that the proposed equalization algorithms outperform the existing reduced-rank and full-rank algorithms, while requiring a comparable computational cost.