# Asymptotic eigenvalue analysis for a sparse random matrix

We have an asymptotic analysis problem for the eigenvalue performance of the following random matrix: $H=\{h_{ij}\}_{N_r\times N_t}$,

where each entry $h_{ij}$ is with a probability $p$ to obey the Gaussian distribution $N(0,σ^2)$, and with a probability $1-p$ to be zero.

Then we have following questions

a)Can we use a specific distribution (pdf) to describe the entries of this $N_r \times N_t$ matrix as $h_{ij}\sim (1-p)\delta(0)+p\frac{1}{\sigma\sqrt{2\pi}}e^{-j\frac{x^2}{2\sigma^2}}\delta(x)$?

b)When $N_r,N_t\rightarrow \infty$, and $N_r/N_t\rightarrow \beta$ (determined value), does the eigenvalues’ PDF go to a deterministic distribution? If the answer is Yes, what is the explicit expression of this deterministic distribution?

For question b), one potentially useful tool maybe the Mar˘cenko-Pastur law in the random matrix theory, but I do not know if it can be used indeed.

• The matrices aren't square. Do you mean singular values instead of eigenvalues? Sep 29, 2014 at 13:06
• Yes, for a general case, singular values should be considered, and the Marchenko-Pastur law may can be used. But from my comparison between the asymptotic results and the numerical results, there is a mismatch, I cannot explain the reason. Sep 30, 2014 at 0:12