3 citations · 7 across the 6 of their papers we have counts for
6 papers
Federated Meta-Learning for Traffic Steering in O-RAN
Hakan Erdol, Xiaoyang Wang, Peizheng Li +7
The vision of 5G lies in providing high data rates, low latency (for the aim of near-real-time applications), significantly increased base station capacity, and near-perfect qualit…
Transmit Power Control for Indoor Small Cells: A Method Based on Federated Reinforcement Learning
Peizheng Li, Hakan Erdol, Keith Briggs +7
Setting the transmit power setting of 5G cells has been a long-term topic of discussion, as optimized power settings can help reduce interference and improve the quality of service…
Smart Interference Management xApp using Deep Reinforcement Learning
Mahdi Eskandari, Shipra Kapoor, Keith Briggs +3
Interference continues to be a key limiting factor in cellular radio access network (RAN) deployments. Effective, data-driven, self-adapting radio resource management (RRM) solutio…
D3.2: SPEED-5G enhanced functional and system architecture, scenarios and performance evaluation metrics
Shahid Mumtaz, Kazi Saidul, Huq Jonathan Rodriguez +36
This deliverable contains a detailed description of the use cases considered in SPEED-5G, which will be used as a basis for demonstration in project. These use cases are Dynamic Ch…
Centralized Radio Resource Management for 5G small cells as LSA enabler
Oscar Carrasco, Federico Miatton, Salva Diaz +7
The stringent requirements defined for 5G systems drive the need to promote new paradigms to the existing cellular networks. Dense and ultra-dense networks based on small cells, to…
A Two-Level Graph Partitioning Problem Arising in Mobile Wireless Communications
Jamie Fairbrother, Adam Letchford, Keith Briggs
In the k-partition problem (k-PP), one is given an edge-weighted undirected graph, and one must partition the node set into at most k subsets, in order to minimise (or maximise) th…