  The mutual information (MI) of Gaussian multi-input multi-output (MIMO)
channels has been evaluated by utilizing random matrix theory (RMT) and shown
to asymptotically follow Gaussian distribution, where the ergodic mutual
information (EMI) converges to a deterministic quantity. However, with
non-Gaussian channels, there is a bias between the EMI and its deterministic
equivalent (DE), whose evaluation is not available in the literature. This bias
of the EMI is related to the bias for the trace of the resolvent in large RMT.
In this paper, we first derive the bias for the trace of the resolvent, which
is further extended to compute the bias for the linear spectral statistics
(LSS). Then, we apply the above results on non-Gaussian MIMO channels to
determine the bias for the EMI. It is also proved that the bias for the EMI is
$-0.5$ times of that for the variance of the MI. Finally, the derived bias is
utilized to modify the central limit theory (CLT) and calculate the outage
probability. Numerical results show that the modified CLT significantly
outperforms previous methods in approximating the distribution of the MI and
improves the accuracy for the outage probability evaluation.
