Generalized consistent estimation on low-rank Krylov subspaces of arbitrarily high dimension
IEEE Transactions on Signal Processing
Robust reduced-rank adaptive algorithm based on parallel subgradient projection and Krylov subspace
IEEE Transactions on Signal Processing
A Krylov subspace based low-rank channel estimation in OFDM systems
Signal Processing
Iterative Schur Complement and Multistage Wiener Filtering
SIAM Journal on Matrix Analysis and Applications
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This book focuses on the foundations of linear estimation theory which is essential for effective signal processing. In its first part, it gives a comprehensive overview of several key methods like reduced-rank signal processing and Krylov subspace methods of numerical mathematics. Based on the derivation of the multistage Wiener filter in its most general form, the relationship between statistical signal processing and numerical mathematics is presented. In the second part, the theory is applied to iterative multiuser detection receivers (Turbo equalization) which are typically desired in wireless communications systems. The investigations include exact computational complexity considerations and performance analysis based on extrinsic information transfer charts as well as Monte-Carlo simulations.