Reduced-rank adaptive filtering
IEEE Transactions on Signal Processing
Blind multiuser detection: a subspace approach
IEEE Transactions on Information Theory
A multistage representation of the Wiener filter based on orthogonal projections
IEEE Transactions on Information Theory
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In this work, we propose a novel combined set-membership (SM) reduced-rank interference suppression scheme and consider its application to spread-spectrum multiuser direct sequence ultra-wideband (DS-UWB) systems. In the proposed scheme, the theory of set-membership filtering is applied to the training-based powers of R (PoR) multi-stage Wiener filter (MSWF) reduced-rank process and the reduced-rank filter adaptation. We exploit the variable update rates associated with the set-membership framework so that the dimensionality reducing projection matrix and the reduced-rank filter are updated independently, and only when the estimation error at the output of each process exceeds a predefined, nontime-varying bound. Normalized least mean squares (NLMS) and Bounding Ellipsoidal Adaptive Constrained Least-Squares (BEACON) algorithms are then developed and an analysis of their complexity given. Computer simulations show that the interference suppression performance exhibited by the proposed algorithms exceed that of the conventional reduced-rank NLMS and RLS algorithms whilst achieving a significant reduction in computational complexity.