Limiting spectral distribution for a type of sample covariance matrices

This paper focuses on the limiting spectral distribution of the sample covariance matrices with information plus noise type data, which is interesting in the area of array signal processing. Assume that the noise data comes from a multivariate population with an isotropic and log-concave probability law. It is shown that in probability, the empirical spectral distribution converges weakly to a non-random probability distribution, whose Stieltjes transform satisfies a certain equation.