5 papers
Adaptive Machine Learning Framework for UAV Trajectory Optimization in O-RAN
Chenrui Sun, Swarna Bindu Chetty, Gianluca Fontanesi +3
The deployment of unmanned aerial vehicles (UAV) as open radio units (O-RUs) in 6G cellular systems presents a promising opportunity to achieve scalable and adaptive network covera…
Optimizing 6G Dense Network Deployment for the Metaverse Using Deep Reinforcement Learning
Jie Zhang, Swarna Chetty, Qiao Wang +4
As the Metaverse envisions deeply immersive and pervasive connectivity in 6G networks, Integrated Access and Backhaul (IAB) emerges as a critical enabler to meet the demanding requ…
Energy Consumption Reduction for UAV Trajectory Training : A Transfer Learning Approach
Chenrui Sun, Swarna Bindu Chetty, Gianluca Fontanesi +4
The advent of 6G technology demands flexible, scalable wireless architectures to support ultra-low latency, high connectivity, and high device density. The Open Radio Access Networ…
Enhancing Energy Efficiency in O-RAN Through Intelligent xApps Deployment
Xuanyu Liang, Ahmed Al-Tahmeesschi, Qiao Wang +3
The proliferation of 5G technology presents an unprecedented challenge in managing the energy consumption of densely deployed network infrastructures, particularly Base Stations (B…
Continuous Transfer Learning for UAV Communication-aware Trajectory Design
Chenrui Sun, Gianluca Fontanesi, Swarna Bindu Chetty +3
Deep Reinforcement Learning (DRL) emerges as a prime solution for Unmanned Aerial Vehicle (UAV) trajectory planning, offering proficiency in navigating high-dimensional spaces, ada…