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This paper presents novel adaptive space-time reduced-rank interferencesuppression least squares algorithms based on joint iterative optimization ofparameter vectors. The proposed space-time reduced-rank scheme consists of ajoint iterative optimization of a projection matrix that performsdimensionality reduction and an adaptive reduced-rank parameter vector thatyields the symbol estimates. The proposed techniques do not require singularvalue decomposition (SVD) and automatically find the best set of basis forreduced-rank processing. We present least squares (LS) expressions for thedesign of the projection matrix and the reduced-rank parameter vector and weconduct an analysis of the convergence properties of the LS algorithms. We thendevelop recursive least squares (RLS) adaptive algorithms for theircomputationally efficient estimation and an algorithm for automaticallyadjusting the rank of the proposed scheme. A convexity analysis of the LSalgorithms is carried out along with the development of a proof of convergencefor the proposed algorithms. Simulations for a space-time interferencesuppression application with a DS-CDMA system show that the proposed schemeoutperforms in convergence and tracking the state-of-the-art reduced-rankschemes at a comparable complexity.

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