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We consider the problem of subspace estimation in situations where the number of available snapshots and the observation dimension are comparable in magnitude. In this context, traditional subspace methods tend to fail because the eigenvectors of the sample correlation matrix are heavily biased with respect to the true ones. It has recently been suggested(More)
This paper deals with the problem of parameter estimation based on certain eigenspaces of the empirical covariance matrix of an observed multidimensional time series, in the case where the time series dimension and the observation window grow to infinity at the same pace. In the area of large random matrix theory, recent contributions studied the behavior(More)
This paper is devoted to the subspace DoA estimation using a large antennas array when the number of available snapshots is of the same order of magnitude than the number of sensors. In this context, the traditional subspace methods fail because the empirical covariance matrix of the observations is a poor estimate of the true covariance matrix. Mestre et(More)
In this paper, we deal with the estimation of the ergodic capacity of large MIMO systems, using training sequences whose lengths are of the same order of magnitude than the number of antennas. In this context, the traditional estimator becomes inconsistent. Following the ideas developed by Girko in the context of the so-called theory of G-estimation, we(More)
An improved estimator of certain bilinear forms of the logarithm of the covariance matrix is presented. The new estimator is shown to be consistent, not only for increasing sample size (as traditional estimators), but also when the observation dimension scales up at the same rate as the number of available observations. This characteristic provides very(More)
This paper is devoted to subspace DoA estimation, when the number of available snapshots N is of the same order of magnitude as the number of sensors M. In this context, traditional subspace methods fail because the empirical covariance matrix of the observations is a poor estimate of the true covariance matrix. The goal of the paper is to propose a new(More)
Subspace methods (e.g. MUSIC) are widely used in the context of DoA estimation using an array of M antennas. These methods perform well as long as the number of available samples N is much larger than M. However, their performance severely degrades when N is of the same order of magnitude than M. In this context, a DoA estimation method (called(More)
The problem of correlation detection of multivariate Gaussian observations is considered. The problem is formulated as a binary hypothesis test, where the null hypothesis corresponds to a diagonal correlation matrix with possibly different diagonal entries, whereas the alternative would be associated to any other form of positive covariance. Using tools(More)
Traditional estimators of the eigen-subspaces of sample co-variance matrices are known to be consistent only when the sample volume increases for a fixed observation dimension. Due to this fact, their accuracy tends to be rather poor in practical settings where the number of samples and the observation dimension are comparable in magnitude. To overcome this(More)