Reduced-Rank Space-Time Interference Suppression with Joint Iterative Least Squares Algorithms for Spread Spectrum Systems

TL;DR

This paper introduces joint iterative least squares algorithms for reduced-rank space-time interference suppression in spread spectrum systems, improving convergence and tracking without requiring SVD.

Contribution

It proposes a novel reduced-rank scheme with joint iterative optimization that automatically finds the best basis, avoiding SVD and enhancing adaptive filtering performance.

Findings

Outperforms existing reduced-rank schemes in convergence speed

Provides efficient recursive least squares algorithms for real-time adaptation

Demonstrates superior interference suppression in DS-CDMA simulations

Abstract

This paper presents novel adaptive space-time reduced-rank interference suppression least squares algorithms based on joint iterative optimization of parameter vectors. The proposed space-time reduced-rank scheme consists of a joint iterative optimization of a projection matrix that performs dimensionality reduction and an adaptive reduced-rank parameter vector that yields the symbol estimates. The proposed techniques do not require singular value decomposition (SVD) and automatically find the best set of basis for reduced-rank processing. We present least squares (LS) expressions for the design of the projection matrix and the reduced-rank parameter vector and we conduct an analysis of the convergence properties of the LS algorithms. We then develop recursive least squares (RLS) adaptive algorithms for their computationally efficient estimation and an algorithm for automatically adjusting the rank of the proposed scheme. A convexity analysis of the LS algorithms is carried out along with the development of a proof of convergence for the proposed algorithms. Simulations for a space-time interference suppression application with a DS-CDMA system show that the proposed scheme outperforms in convergence and tracking the state-of-the-art reduced-rank schemes at a comparable complexity.