Krylov-proportionate adaptive filtering techniques not limited to sparse systems
IEEE Transactions on Signal Processing
Stochastic MV-PURE estimator: robust reduced-rank estimator for stochastic linear model
IEEE Transactions on Signal Processing
An adaptive projected subgradient approach to learning in diffusion networks
IEEE Transactions on Signal Processing
Robust reduced-rank adaptive algorithm based on parallel subgradient projection and Krylov subspace
IEEE Transactions on Signal Processing
A unified view of adaptive variable-metric projection algorithms
EURASIP Journal on Advances in Signal Processing
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This paper introduces adaptive filters that are effective to suppress multiple access interference (MAI) in orthogonal space-time block coded/ multiple-input multiple-output (OSTBC-MIMO) systems. We define an optimal linear filter that minimizes the mean-square error between the filter output and a scaled version of the desired output under a constraint defined by the available channel state information (CSI). The adaptive filters refine a given estimate of the optimal filter by suppressing a sequence of closed convex functions with the adaptive projected subgradient method (APSM) at each iteration. To provide robustness against imperfect CSI, the adaptive filters use not only the available CSI but also estimates of previously transmitted symbols, which usually belong to a small finite set in digital communication systems. The resulting algorithms employ computationally efficient projections onto hyperplanes or hyperslabs and do not require any matrix inversion. An efficient recursive scheme based on such an algorithm is also presented. Convergence analysis and simulation results show the excellent performance of the proposed schemes.