I will begin by reviewing limiting properties of the eigenvalues of a class of sample covariance matrices, where the vector dimension and the sample size approach infinity, their ratio approaching a positive constant. These properties are relevant in situations in multivariate analysis where the vector dimension is large, but the number of samples needed to adequately approximate the population matrix (as prescribed in standard statistical procedures) cannot be attained. Work has been done in estimating the population eigenvalues from those of the sample covariance matrix. I will introduce a method devised by X. Mestre, and will present an extension of his method to another ensemble of random matrices important in wireless communications.