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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)

—In this paper, we deal with the estimation of the er-godic 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)

The MUSIC method is widely used in the field of DoA estimation using an array of M sensors, and is known to perform well as long as the number of available samples N is much larger than M. Nevertheless , in the scenario where N is of the same order of magnitude than M , its performance degrades, essentially because the sample covariance matrix (SCM) is no… (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 " G-MUSIC… (More)

- Pascal Vallet
- 2011