  Let $ X_{n} $ be $ n\times N $ random complex matrices, $R_{n}$ and $T_{n}$
be non-random complex matrices with dimensions $n\times N$ and $n\times n$,
respectively. We assume that the entries of $ X_{n} $ are independent and
identically distributed, $ T_{n} $ are nonnegative definite Hermitian matrices
and $T_{n}R_{n}R_{n}^{*}= R_{n}R_{n}^{*}T_{n} $.
  The general information-plus-noise type matrices are defined by
$C_{n}=\frac{1}{N}T_{n}^{\frac{1}{2}} \left( R_{n} +X_{n}\right)
\left(R_{n}+X_{n}\right)^{*}T_{n}^{\frac{1}{2}} $.
  In this paper, we establish the limiting spectral distribution of the large
dimensional general information-plus-noise type matrices $C_{n}$. Specifically,
we show that as $n$ and $N$ tend to infinity proportionally, the empirical
distribution of the eigenvalues of $C_{n}$ converges weakly to a non-random
probability distribution, which is characterized in terms of a system of
equations of its Stieltjes transform.
