M
Mohsen Guizani
Researcher at Qatar University
Publications - 1337
Citations - 48275
Mohsen Guizani is an academic researcher from Qatar University. The author has contributed to research in topics: Computer science & Cloud computing. The author has an hindex of 79, co-authored 1110 publications receiving 31282 citations. Previous affiliations of Mohsen Guizani include Jaypee Institute of Information Technology & University College for Women.
Papers
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Journal ArticleDOI
Attention-mechanism-based tracking method for intelligent Internet of vehicles:
TL;DR: The experimental results show that the proposed vehicle tracking method improves the tracking accuracy without an increase in tracking time, and strengthens the robustness of algorithm under the condition of the complex background region.
Proceedings ArticleDOI
Deep Reinforcement Learning for Real-Time Trajectory Planning in UAV Networks
TL;DR: An onboard deep reinforcement learning algorithm is proposed to optimize the realtime trajectory planning of the UAV given outdated knowledge on the network states to minimize buffer overflow at the ground sensors and unsuccessful transmission due to lossy airborne channels.
Journal ArticleDOI
Weak many vs. strong few: reducing BER through packet duplication in power-budgeted wireless connections
TL;DR: New energy-aware techniques to lower the packet-level error rates of application-layer connections in wireless ad hoc networks are presented and the relationship between packet error rate, the extent of duplication and the path lengths are described.
Journal ArticleDOI
Topics in internet technology
TL;DR: The article by McGregor Kaczmarek, Mosley, Dease, and Adams discusses the requirements of the next-generation Internet for national security and emergency preparedness, and presents test results of voice over IP for such priority users.
Journal ArticleDOI
Analysis of Beyond 5G Integrated Communication and Ranging Services Under Indoor 3-D mmWave Stochastic Channels
TL;DR: A 3-D stochastic channel model is studied and implemented using the baseline third-generation partnership project model that employs the time-cluster spatial-lobe (TCSL) technique and utilizes the temporal and spatial statistics to create the channel impulse response (CIR), reflecting realistic indoor factory conditions.