3 citations · 4 across the 10 of their papers we have counts for
5 papers · 1 filter
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…
Optimized Resource Allocation for Cloud-Native 6G Networks: Zero-Touch ML Models in Microservices-based VNF Deployments
Swarna Bindu Chetty, Avishek Nag, Ahmed Al-Tahmeesschi +4
6G, the next generation of mobile networks, is set to offer even higher data rates, ultra-reliability, and lower latency than 5G. New 6G services will increase the load and dynamis…
Energy Saving in 6G O-RAN Using DQN-based xApp
Qiao Wang, Swarna Chetty, Ahmed Al-Tahmeesschi +3
Open Radio Access Network (RAN) is a transformative paradigm that supports openness, interoperability, and intelligence, with the O-RAN architecture being the most recognized frame…
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…
Multi-Objective Deep Reinforcement Learning for 5G Base Station Placement to Support Localisation for Future Sustainable Traffic
Ahmed Al-Tahmeesschi, Jukka Talvitie, Miguel López-Benítez +2
Millimeter-wave (mmWave) is a key enabler for next-generation transportation systems. However, in an urban city scenario, mmWave is highly susceptible to blockages and shadowing. T…