5 papers
Large Artificial Intelligence Model Guided Deep Reinforcement Learning for Resource Allocation in Non Terrestrial Networks
Abdikarim Mohamed Ibrahim, Rosdiadee Nordin
Large AI Model (LAM) have been proposed to applications of Non-Terrestrial Networks (NTN), that offer better performance with its great generalization and reduced task specific tra…
A Safety-Constrained Reinforcement Learning Framework for Reliable Wireless Autonomy
Abdikarim Mohamed Ibrahim, Rosdiadee Nordin
Artificial intelligence (AI) and reinforcement learning (RL) have shown significant promise in wireless systems, enabling dynamic spectrum allocation, traffic management, and large…
Geometry-Aware LoRaWAN Gateway Placement in Dense Urban Cities Using Digital Twins
Abdikarim Mohamed Ibrahim, Rosdiadee Nordin
LoRaWAN deployments rely on rough range estimates or simplified propagation models to decide where to place/mount gateways. As a result, operators have limited visibility into how…
Digital Twin for Ultra-Reliable & Low-Latency 6G Wireless Communications in Dense Urban City
Abdikarim Mohamed Ibrahim, Rosdiadee Nordin
High-frequency deployments in dense cities are difficult to plan because coverage, interference, and service reliability depend sensitively on local morphology. This paper develops…
URLLC for 6G Enabled Industry 5.0: A Taxonomy of Architectures, Cross Layer Techniques, and Time Critical Applications
Abdikarim Mohamed Ibrahim, Rosdiadee Nordin, Yahya S. M. Khamayseh +2
The evolution from Industry 4.0 to Industry 5.0 introduces stringent requirements for ultra reliable low latency communication (URLLC) to support human centric, intelligent, and re…