3 citations · 4 across the 10 of their papers we have counts for
10 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…
Scalable machine learning-based approaches for energy saving in densely deployed Open RAN
Xuanyu Liang, Ahmed Al-Tahmeesschi, Swarna Chetty +3
Densely deployed base stations are responsible for the majority of the energy consumed in Radio access network (RAN). While these deployments are crucial to deliver the required da…
Green O-RAN Operation: a Modern ML-Driven Network Energy Consumption Optimisation
Xuanyu Liang, Ahmed Al-Tahmeesschi, Swarna Chetty +1
The increasing energy demand of next-generation mobile networks, especially 6G, is becoming a major concern, particularly due to the high power usage of base station components RU,…
An Explainable AI Framework for Dynamic Resource Management in Vehicular Network Slicing
Haochen Sun, Yifan Liu, Ahmed Al-Tahmeesschi +4
Effective resource management and network slicing are essential to meet the diverse service demands of vehicular networks, including Enhanced Mobile Broadband (eMBB) and Ultra-Reli…
Large-Scale AI in Telecom: Charting the Roadmap for Innovation, Scalability, and Enhanced Digital Experiences
Adnan Shahid, Adrian Kliks, Ahmed Al-Tahmeesschi +132
This white paper discusses the role of large-scale AI in the telecommunications industry, with a specific focus on the potential of generative AI to revolutionize network functions…
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…
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…
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…