12 papers
A Multi-Agent System for 5G Throughput Prediction in Multi-Operator Urban Environments
Muhammad Kabeer, Rosdiadee Nordin, Nadiva Nuriftitah +1
Throughput prediction is foundational for artificial intelligence-driven 6G resource orchestration. Conventional monolithic machine learning models struggle to generalize across di…
IoT-Driven Building Energy Management Systems (BEMS) for Net Zero Energy Buildings: Concept, Integration and Future Directions
Haizum Hanim Ab Halim, Dalila Alias, Akmal Zaini Arsad +3
Construction and operating of buildings is one of the major contributors to global greenhouse emissions. With the inefficient usage of energy due to human behavior and manual opera…
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…
Maximizing UAV Cellular Connectivity with Reinforcement Learning for BVLoS Path Planning
Mehran Behjati, Rosdiadee Nordin, Nor Fadzilah Abdullah
This paper presents a reinforcement learning (RL) based approach for path planning of cellular connected unmanned aerial vehicles (UAVs) operating beyond visual line of sight (BVLo…
Corrosion Risk Estimation for Heritage Preservation: An Internet of Things and Machine Learning Approach Using Temperature and Humidity
Reginald Juan M. Mercado, Muhammad Kabeer, Haider Al-Obaidy +1
Proactive preservation of steel structures at culturally significant heritage sites like the San Sebastian Basilica in the Philippines requires accurate corrosion forecasting. This…
Sequence-Based Deep Learning for Handover Optimization in Dense Urban Cellular Network
Muhammad Kabeer, Rosdiadee Nordin, Mehran Behjati +1
Efficient handover management remains a critical challenge in dense urban cellular networks, where high cell density, user mobility, and diverse service demands increase the likeli…
An Urban Multi-Operator QoE-Aware Dataset for Cellular Networks in Dense Environments
Muhammad Kabeer, Rosdiadee Nordin, Mehran Behjati +1
Urban cellular networks face complex performance challenges due to high infrastructure density, varied user mobility, and diverse service demands. While several datasets address ne…
Empirical 3D Channel Modeling for Cellular-Connected UAVs: A Triple-Layer Machine Learning Approach
Haider A. H. Alobaidy, Mehran Behjati, Rosdiadee Nordin +3
This work proposes an empirical air to ground (A2G) propagation model specifically designed for cellular connected unmanned aerial vehicles (UAVs). An in depth aerial drive test wa…