The use of the L-curve in the regularization of discrete ill-posed problems
SIAM Journal on Scientific Computing
Conjugate Gradient Methods for Toeplitz Systems
SIAM Review
Rank-deficient and discrete ill-posed problems: numerical aspects of linear inversion
Rank-deficient and discrete ill-posed problems: numerical aspects of linear inversion
Choosing Regularization Parameters in Iterative Methods for Ill-Posed Problems
SIAM Journal on Matrix Analysis and Applications
Superlinear Convergence of Conjugate Gradients
SIAM Journal on Numerical Analysis
Preconditioning techniques for large linear systems: a survey
Journal of Computational Physics
Iterative Methods for Sparse Linear Systems
Iterative Methods for Sparse Linear Systems
Toeplitz And Circulant Matrices: A Review (Foundations and Trends(R) in Communications and Information Theory)
Linear Estimation and Detection in Krylov Subspaces
Linear Estimation and Detection in Krylov Subspaces
IEEE Transactions on Signal Processing
Low complexity equalization for doubly selective channels modeled by a basis expansion
IEEE Transactions on Signal Processing
BER degradation of MC-CDMA at high SNR with MMSE equalization and residual frequency offset
EURASIP Journal on Wireless Communications and Networking
Robust minimum variance beamforming
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Subspace Expansion and the Equivalence of Conjugate Direction and Multistage Wiener Filters
IEEE Transactions on Signal Processing - Part II
IEEE Transactions on Signal Processing
On robust Capon beamforming and diagonal loading
IEEE Transactions on Signal Processing
Asymptotic Analysis of Reduced-Rank Chip-Level MMSE Equalizers in the Downlink of CDMA Systems
IEEE Transactions on Signal Processing
A projection approach for robust adaptive beamforming
IEEE Transactions on Signal Processing
Data adaptive rank-shaping methods for solving least squaresproblems
IEEE Transactions on Signal Processing
Complexity Reduction of Iterative Receivers Using Low-Rank Equalization
IEEE Transactions on Signal Processing
On the Performance of the Mismatched MMSE and the LS Linear Equalizers
IEEE Transactions on Signal Processing - Part I
An Efficient Method to Determine the Diagonal Loading Factor Using the Constant Modulus Feature
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Optimal multistage linear multiuser receivers
IEEE Transactions on Wireless Communications
Low-Cost Approximate LMMSE Equalizer Based on Krylov Subspace Methods for HSDPA
IEEE Transactions on Wireless Communications
A multistage representation of the Wiener filter based on orthogonal projections
IEEE Transactions on Information Theory
Robust multiuser detection for multicarrier CDMA systems
IEEE Journal on Selected Areas in Communications
IEEE Journal on Selected Areas in Communications
A Low-Complexity Detector for Large MIMO Systems and Multicarrier CDMA Systems
IEEE Journal on Selected Areas in Communications
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Two concerns often arise simultaneously when applying linear estimation in communication systems: the computational complexity can be prohibitively high when the system size is large, and the performance may degrade dramatically when the presumed model is mismatched with the actual system. In this paper, we introduce a subspace expansion framework to jointly address these concerns, in which the observation is first projected onto a lower-dimensional subspace and then the solution of the projected problem is regularized. We discuss two projection methods based on eigensubspace and Krylov subspace expansions. We show that the Krylov subspace projection provides an economical solution to regularized linear estimation. We also compare different regularization methods, such as principal components and diagonal loading. We show that diagonal loading generally outperforms other alternatives and that Krylov subspace rank reduction can yield a regularization effect close to diagonal loading. Finally, we investigate the impact of preconditioning on the performance and complexity for mismatched modeling and propose a loaded preconditioner, which can reduce complexity as well as preserve the regularization effect. Under the proposed framework, various regularization schemes are studied and some guidelines for choosing the right scheme are provided.