Adaptive Set-Membership Reduced-Rank Interference Suppression for DS-UWB Systems

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, non-time-varying bound. Normalized least mean squares (NLMS) and Bounding Elhpsoidal Adaptive Constrained Least-Squares (BEACON) algorithms am 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.