5 citations · 9 across the 10 of their papers we have counts for
12 papers
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,…
Maximising Energy Efficiency in Large-Scale Open RAN: Hybrid xApps and Digital Twin Integration
Ahmed Al-Tahmeesschi, Yi Chu, Gurdeep Singh +4
The growing demand for high-speed, ultra-reliable, and low-latency communications in 5G and beyond networks has significantly driven up power consumption, particularly within the R…
Exploring O-RAN Compression Techniques in Decentralized Distributed MIMO Systems: Reducing Fronthaul Load
Mostafa Rahmani, Junbo Zhao, Vida Ranjbar +4
This paper explores the application of uplink fronthaul compression techniques within Open RAN (O-RAN) to mitigate fronthaul load in decentralized distributed MIMO (DD-MIMO) system…
Enhancing Open RAN Digital Twin Through Power Consumption Measurement
Ahmed Al-Tahmeesschi, Yi Chu, Josh Shackleton +4
The increasing demand for high-speed, ultra-reliable and low-latency communications in 5G and beyond networks has led to a significant increase in power consumption, particularly w…
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…
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…
WHITE PAPER: Protecting GNSS Against Intentional Interference
Arul Elango, Ahmed Al-Tahmeesschi, Mikko Saukkoriipi +2
The vulnerabilities associated with modern systems relying on Global Navigation Satellite Systems (GNSS) due to intentional and unintentional interference is an increasing threat.…