Generalized Reduced-Rank Decompositions Using Switching and Adaptive Algorithms for Space-Time Adaptive Processing

TL;DR

This paper introduces a novel adaptive low-rank decomposition method for space-time processing that avoids eigen-decomposition, improving interference suppression in DS-CDMA systems through switching and iterative algorithms.

Contribution

It proposes a generalized low-rank decomposition framework with switching and adaptive algorithms that do not rely on eigen-decomposition, enhancing computational efficiency and performance.

Findings

Significant performance gains over previous low-rank schemes.

Effective interference suppression in DS-CDMA systems.

Reduced computational complexity through switching and iterative methods.

Abstract

This work presents generalized low-rank signal decompositions with the aid of switching techniques and adaptive algorithms, which do not require eigen-decompositions, for space-time adaptive processing. A generalized scheme is proposed to compute low-rank signal decompositions by imposing suitable constraints on the filtering and by performing iterations between the computed subspace and the low-rank filter. An alternating optimization strategy based on recursive least squares algorithms is presented along with switching and iterations to cost-effectively compute the bases of the decomposition and the low-rank filter. An application to space-time interference suppression in DS-CDMA systems is considered. Simulations show that the proposed scheme and algorithms obtain significant gains in performance over previously reported low-rank schemes.