Multiple input multiple output (MIMO) approach in fiber optical communication has emerged as an effective proposition to address the ever increasing demand for information exchange. In the ergodic case, the multiple channels, associated with multiple modes or cores or both in the optical fiber, is modeled by the Jacobi ensemble of random matrices. A key quantity for assessing the performance of MIMO systems is the mutual information (MI). We focus here on the case of an arbitrary transmission covariance matrix and derive exact determinant based results for the moment generating function (MGF) of mutual information (MI), and thereby address the scenario of unequal power per excited mode. The MGF is used to obtain Gaussian- and Weibull-distribution based approximations for the probability density function (PDF), cumulative distribution function (CDF) or, equivalently, the outage probability, and also the survival function (SF) or reliability function. Moreover, a numerical Fourier inversion approach is implemented to obtain the PDF, CDF, and SF directly from the MGF. The MGF is further used to investigate the ergodic capacity, which is the first moment (mean) of the mutual information. The analytical results are found to be in excellent agreement with Monte Carlo simulations. Our study goes beyond the earlier investigations where covariance matrix proportional to identity matrix has been considered which corresponds to equal power allocation per excited mode.
翻译:在光学纤维通信中,多种输入多重输出(MIIM)方法已成为解决对信息交流需求不断增加的有效建议,在这种情况中,多种渠道,与多种模式或核心相关,或光纤中两者兼有,均以随机矩阵合体为模型。评估MIMO系统性能的关键数量是相互信息(MI)。我们在此侧重于任意传输共变矩阵的情况,并直接从MGF获取基于瞬时生成功能(MGF)的确切决定因素结果。MGF进一步用于调查ERgodic能力(MGF),其间,MGF用于获得基于概率密度函数(PDF)的Gaussian和Weibull-分配的近似值、累积分布函数(CDF),或等值外差概率,以及生存函数(SF)或可靠性功能。此外,我们采用数字四变法系方法直接从MGFF获得PDF、CDF和SF。MF进一步用于调查ERG能力不平等的情况。MGF用于调查基于概率和Wegusal Streal的配置模型,这是我们分析模型的早期分析结果。