5 papers
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…
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…
Enhancing Air Quality Monitoring: A Brief Review of Federated Learning Advances
Sara Yarham, Mehran Behjati
Monitoring air quality and environmental conditions is crucial for public health and effective urban planning. Current environmental monitoring approaches often rely on centralized…