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 approximate the outage probability. Numerical results show that the modified CLT significantly outperforms the previous results in approximating the distribution of the MI and can accurately determine the outage probability.
翻译:利用随机矩阵理论(RMT)对高西多投入多产出(MI)渠道的相互信息(MI)进行了评价,并显示对高西的分布进行随机矩阵理论(RMT)的不现性跟踪,在高西的分布中,ERGodic 相互信息(EMI)汇集到一个确定的数量,然而,在非加西语渠道中,EMI及其确定等同(DE)之间有偏差,文献中没有提供这种偏差。EMI的偏差与大RMT中确定点的痕迹的偏差有关。在本文中,我们首先得出对确定点的偏差,然后进一步扩展,以计算线性光谱统计(LSS)的偏差。然后,我们将上述结果应用于非加西语的MIMO频道,以确定EMI的偏差是MI差异的0.5倍。最后,从结果的偏差被用来修改中央限理理论(CLT),并近MI值概率的近似差差差差差差结果,显示以前的CFMIF的准确性结果。