Topic

Attai Ibrahim Abubakar

Also published under:Attai I. Abubakar

Affiliation

School of Computing, Engineering and Physical Sciences, University of the West of Scotland, Paisley, U.K.
James Watt School of Engineering, University of Glasgow, Glasgow, U.K.

Topic

Energy Consumption,Energy Efficiency,Optimal Policy,Reinforcement Learning Algorithm,Wireless Networks,5G HetNets,Access Network,Aggregate Data,Application Of Reinforcement Learning,Autonomous Underwater Vehicles,Autonomous Vehicles,Base Station,Battery Level,Benchmark Methods,Caching,Central Unit,Cloud Processing,Communication Protocol,Computation Offloading,Computational Complexity,Computational Cost,Consumption Cost,Current Solution,Curse Of Dimensionality,Data Processing,Data Transmission,Deep Reinforcement Learning,Delay Tolerance,Device Level,Distributed Unit,Dynamic Environment,Dynamic Policy,Dynamic Pricing,Dynamic Resource Allocation,Dynamic Spectrum,Earth Model,Energy Conservation,Energy Consumption Cost,Energy Consumption Of Devices,Environmental Model,Exhaustive Search Algorithm,Functional Networks,Hardware Components,Heterogeneous Network,Heuristic Algorithm,Heuristic Approach,High Overhead,Intelligent Control,Internet Of Things,Internet Of Things Applications,

Biography

Attai Ibrahim Abubakar (Student Member, IEEE) received the B.Eng. degree (Hons.) in electrical and electronics engineering from Joseph Sarwuan Tarka University (formerly Federal University of Agriculture), Makurdi, Nigeria, in 2011, and the M.Sc. degree (Hons.) in wireless communication systems from The University of Sheffield, U.K., in 2015. He is currently pursuing the Ph.D. degree with the James Watt School of Engineering, University of Glasgow, U.K. He is also an Associate Fellow of Recognising Excellence in Teaching. His research interests include energy performance optimization of 5G and beyond heterogeneous cellular networks, radio resource management, cognitive radio, unmanned aerial vehicle (UAV) aided communications, self-organizing networks (SON), and application of machine learning to wireless communications networks.