Network level simulation results demonstrate that the deep learning network based handover can reduce 74% of unnecessary handovers in ultra-dense scenarios.
Network level simulation results demonstrate that the deep learning network based handover can reduce 74% of unnecessary handovers in ultra-dense scenarios.
This paper presents a handover decision solution for connected vehicles to 5G Ultra-Dense Networks (5G-UDN). Its main goal is to treat high vehicular mobility.
Sep 10, 2024 · Their study demonstrated that LSTM networks could effectively learn from historical signal data to predict future handover events, ...
In this paper, we developed a Q-learning framework exploiting user radio condition, that is, reference signal receiving power (RSRP), signal to inference and ...
Feb 23, 2023 · The simulation results show an average reduction of 30% in handover times by utilizing ML-based MP, with RFC showing the most reduction up to 70 ...
Missing: Deep | Show results with:Deep
This comprehensive review examines the latest developments in the field of machine learning based handover (HO) decision-making for connected drones in future ...
Handover is vital for cellular networks, allowing User. Equipment (UE) to have their sessions transferred from one serving cell to another without any ...
High mobility travelling trains and drones connected via ultra-dense mobile networks may lead to frequent handovers (HOs). As a consequence, this could ...
Missing: Vehicles | Show results with:Vehicles
The proposed mechanism has the objectives of enhancing user mobility robustness while maintaining other high-level key performance indicators (KPIs). • Second, ...