Matrix analysis
Limiting spectral distribution for a class of random matrices
Journal of Multivariate Analysis
Enumerative combinatorics
Fast Estimation of Principal Eigenspace Using LanczosAlgorithm
SIAM Journal on Matrix Analysis and Applications
On the empirical distribution of eigenvalues of a class of large dimensional random matrices
Journal of Multivariate Analysis
Strong convergence of the empirical distribution of eigenvalues of large dimensional random matrices
Journal of Multivariate Analysis
Matrix computations (3rd ed.)
The symmetric eigenvalue problem
The symmetric eigenvalue problem
SIAM Journal on Scientific Computing
A Krylov Subspace Method for Covariance Approximation and Simulation of Random Processes and Fields
Multidimensional Systems and Signal Processing
Iterative Methods for Sparse Linear Systems
Iterative Methods for Sparse Linear Systems
Random matrix theory and wireless communications
Communications and Information Theory
Reduced-rank adaptive filtering using Krylov subspace
EURASIP Journal on Applied Signal Processing
Linear Estimation and Detection in Krylov Subspaces
Linear Estimation and Detection in Krylov Subspaces
Consistent reduced-rank LMMSE estimation with a limited number of samples per observation dimension
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
Indirect Dominant Mode Rejection: A Solution to Low Sample Support Beamforming
IEEE Transactions on Signal Processing - Part I
Blind multiuser detection: from MOE to subspace methods
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
Performance of reduced-rank linear interference suppression
IEEE Transactions on Information Theory
Design of reduced-rank MMSE multiuser detectors using random matrix methods
IEEE Transactions on Information Theory
Large system transient analysis of adaptive least squares filtering
IEEE Transactions on Information Theory
A Systematic Approach to Multistage Detectors in Multipath Fading Channels
IEEE Transactions on Information Theory
Performance of Reduced-Rank Equalization
IEEE Transactions on Information Theory
Consistent reduced-rank LMMSE estimation with a limited number of samples per observation dimension
IEEE Transactions on Signal Processing
| Hi-index | 35.69 |
The problem of Krylov subspace estimation based on the sample covariance matrix is addressed. The focus is on signal processing applications where the Krylov subspace is defined by the unknown second-order statistics of the observed samples and the signature vector associated with the desired parameter. In particular, the consistency of traditionally optimal sample estimators is revised and analytically characterized under a practically more relevant asymptotic regime, according to which not only the number of samples but also the observation dimension grow without bound at the same rate. Furthermore, an improved construction of a class of Krylov subspace estimators is proposed based on the generalized consistent estimation of a set of vector-valued power functions of the observation covariance matrix. To that effect, an extension of some known results from random matrix theory on the estimation of certain spectral functions of the covariance matrix to the convergence of not only the covariance eigenspectrum but also the associated eigensubspaces is provided. A new family of estimators is derived that generalizes conventional implementations by proving to be consistent for observations of arbitrarily high dimension. The proposed estimators are shown to outperform traditional constructions via the numerical evaluation of the solution to two fundamental problems in sensor array signal processing, namely the problem of estimating the power of an intended source and the estimation of the principal eigenspace and dominant eigenmodes of a structured covariance matrix.