H
Hussein Al-Shatri
Researcher at Technische Universität Darmstadt
Publications - 58
Citations - 728
Hussein Al-Shatri is an academic researcher from Technische Universität Darmstadt. The author has contributed to research in topics: Relay & Wireless network. The author has an hindex of 13, co-authored 58 publications receiving 629 citations. Previous affiliations of Hussein Al-Shatri include University of Rostock.
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Proceedings ArticleDOI
Reinforcement learning for energy harvesting point-to-point communications
TL;DR: Numerical results show that the performance of the proposed approach, which requires only causal knowledge of the energy harvesting process and channel coefficients, has only a small degradation compared to the optimum case which requires perfect non-causal knowledge.
Proceedings ArticleDOI
Efficient resource allocation in mobile-edge computation offloading: Completion time minimization
TL;DR: This work considers a multi-user MECO system with a base station equipped with a single cloudlet server, and considers parallel sharing of the cloudlet, where each user is allocated a certain fraction of the total computation power.
Journal ArticleDOI
Achieving the Maximum Sum Rate Using D.C. Programming in Cellular Networks
Hussein Al-Shatri,Tobias Weber +1 more
TL;DR: The results show that the proposed algorithm outperforms the known conventional suboptimum schemes and it is shown that the algorithm asymptotically converges to a globally optimum power allocation.
Journal ArticleDOI
Dynamic Self-Optimization of the Antenna Tilt for Best Trade-off Between Coverage and Capacity in Mobile Networks
TL;DR: A method for capacity and coverage optimization using base station antenna electrical tilt in mobile networks is proposed, which has the potential to improve network performance while reducing operational costs and complexity, and to offer better quality of experience for the mobile users.
Journal ArticleDOI
Reinforcement Learning for Energy Harvesting Decode-and-Forward Two-Hop Communications
TL;DR: The proposed approach has only a small degradation as compared to the offline optimum case and with the use of the proposed feature functions a better performance is achieved compared to standard approximation techniques.