TY - JOUR
T1 - An MGF-based unified framework to determine the joint statistics of partial sums of ordered random variables
AU - Nam, Sungsik
AU - Alouini, Mohamed-Slim
AU - Yang, Hongchuan
N1 - KAUST Repository Item: Exported on 2020-10-01
PY - 2010/11
Y1 - 2010/11
N2 - Order statistics find applications in various areas of communications and signal processing. In this paper, we introduce an unified analytical framework to determine the joint statistics of partial sums of ordered random variables (RVs). With the proposed approach, we can systematically derive the joint statistics of any partial sums of ordered statistics, in terms of the moment generating function (MGF) and the probability density function (PDF). Our MGF-based approach applies not only when all the K ordered RVs are involved but also when only the Ks(Ks < K) best RVs are considered. In addition, we present the closed-form expressions for the exponential RV special case. These results apply to the performance analysis of various wireless communication systems over fading channels. © 2006 IEEE.
AB - Order statistics find applications in various areas of communications and signal processing. In this paper, we introduce an unified analytical framework to determine the joint statistics of partial sums of ordered random variables (RVs). With the proposed approach, we can systematically derive the joint statistics of any partial sums of ordered statistics, in terms of the moment generating function (MGF) and the probability density function (PDF). Our MGF-based approach applies not only when all the K ordered RVs are involved but also when only the Ks(Ks < K) best RVs are considered. In addition, we present the closed-form expressions for the exponential RV special case. These results apply to the performance analysis of various wireless communication systems over fading channels. © 2006 IEEE.
UR - http://hdl.handle.net/10754/561554
UR - http://ieeexplore.ieee.org/document/5605378/
UR - http://www.scopus.com/inward/record.url?scp=77958573544&partnerID=8YFLogxK
U2 - 10.1109/TIT.2010.2070271
DO - 10.1109/TIT.2010.2070271
M3 - Article
SN - 0018-9448
VL - 56
SP - 5655
EP - 5672
JO - IEEE Transactions on Information Theory
JF - IEEE Transactions on Information Theory
IS - 11
ER -