H
Hussein Alnuweiri
Researcher at Texas A&M University at Qatar
Publications - 271
Citations - 3255
Hussein Alnuweiri is an academic researcher from Texas A&M University at Qatar. The author has contributed to research in topics: Scheduling (computing) & Wireless network. The author has an hindex of 28, co-authored 270 publications receiving 3043 citations. Previous affiliations of Hussein Alnuweiri include Texas A&M University & King Abdullah University of Science and Technology.
Papers
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Journal ArticleDOI
Opportunistic Relay Selection for Secrecy Enhancement in Cooperative Networks
TL;DR: Analysis of the secrecy performance of opportunistic relay selection systems employing the decode-and-forward protocol over Rayleigh fading channels suggests that both the MRC and DSC schemes achieve the maximum diversity order of K+1 where K is the number of relays.
Patent
Weighted fair queuing scheduler
TL;DR: A scheduler which uses a GPS simulation to determine an order in which to service entities uses a novel dynamic data structure with a sophisticated, but simple, pointer update mechanism as mentioned in this paper.
Journal ArticleDOI
Synchronization procedure in 5G NR systems
TL;DR: The physical layer of the 5G NR physical layer is presented, the required synchronization procedure is described, and the main challenges and issues within the5G NR synchronization are presented.
Journal ArticleDOI
Performance Analysis of Multiuser Multiple Antenna Relaying Networks with Co-Channel Interference and Feedback Delay
TL;DR: A comprehensive performance analysis of multiuser multiple antenna amplify-and-forward relaying networks employing opportunistic scheduling with feedback delay and co-channel interference over Rayleigh fading channels suggests that the full diversity order can only be achieved when there is ideal feedback, i.e., no feedback delay.
Journal ArticleDOI
Toward an efficient and scalable feature selection approach for internet traffic classification
TL;DR: A novel way is proposed to identify efficiently and accurately the ''best'' features by first combining the results of some well-known FS techniques to find consistent features, and then using the proposed concept of support to select a smallest set of features and cover data optimality.