Estimating the covariance matrix from correlated Gaussian samples is important in many applications. Traditional methods work well with independent samples, but not with correlated ones. This study shows that with a certain type of correlation structure, you only need a number of samples proportional to the signal dimension to accurately estimate the covariance matrix. This finding has practical implications for various fields where correlated data is common.