An MGF-Based Unified Framework to Determine the Joint Statistics of Partial Sums of Ordered i.n.d. Random Variables

作者：Nam Sung Sik^*; Yang Hong Chuan; Alouini Mohamed Slim; Kim Dong In

来源：IEEE Transactions on Signal Processing, 2014, 62(16): 4270-4283.

DOI：10.1109/TSP.2014.2326624

• Order statistics
• joint statistics
• non-identical distribution
• moment generating MGF)
• probability density PDF)
• exponential distribution

The joint statistics of partial sums of ordered random variables (RVs) are often needed for the accurate performance characterization of a wide variety of wireless communication systems. A unified analytical framework to determine the joint statistics of partial sums of ordered independent and identically distributed (i.i.d.) random variables was recently presented. However, the identical distribution assumption may not be valid in several real-world applications. With this motivation in mind, we consider in this paper the more general case in which the random variables are independent but not necessarily identically distributed (i.n.d.). More specifically, we extend the previous analysis and introduce a new more general unified analytical framework to determine the joint statistics of partial sums of ordered i.n.d. RVs. Our mathematical formalism is illustrated with an application on the exact performance analysis of the capture probability of generalized selection combining (GSC)-based RAKE receivers operating over frequency-selective fading channels with a non-uniform power delay profile.

• 出版日期2014-8-15
• 单位KAUST