12 citations · 44 across the 41 of their papers we have counts for
6 papers · 1 filter
Density-Aware Reinforcement Learning to Optimise Energy Efficiency in UAV-Assisted Networks
Babatunji Omoniwa, Boris Galkin, Ivana Dusparic
Unmanned aerial vehicles (UAVs) serving as aerial base stations can be deployed to provide wireless connectivity to mobile users, such as vehicles. However, the density of vehicles…
Optimising Energy Efficiency in UAV-Assisted Networks using Deep Reinforcement Learning
Babatunji Omoniwa, Boris Galkin, Ivana Dusparic
In this letter, we study the energy efficiency (EE) optimisation of unmanned aerial vehicles (UAVs) providing wireless coverage to static and mobile ground users. Recent multi-agen…
Enabling Deep Reinforcement Learning on Energy Constrained Devices at the Edge of the Network
Jernej Hribar, Ivana Dusparic
Deep Reinforcement Learning (DRL) solutions are becoming pervasive at the edge of the network as they enable autonomous decision-making in a dynamic environment. However, to be abl…
Analyse or Transmit: Utilising Correlation at the Edge with Deep Reinforcement Learning
Jernej Hribar, Ryoichi Shinkuma, George Iosifidis +1
Millions of sensors, cameras, meters, and other edge devices are deployed in networks to collect and analyse data. In many cases, such devices are powered only by Energy Harvesting…
Mobility for Cellular-Connected UAVs: challenges for the network provider
Erika Fonseca, Boris Galkin, Marvin Kelly +2
Unmanned Aerial Vehicle (UAV) technology is becoming more prevalent and more diverse in its application. 5G and beyond networks must enable UAV connectivity. This will require the…
Experimental Evaluation of a UAV User QoS from a Two-Tier 3.6GHz Spectrum Network
Boris Galkin, Erika Fonseca, Gavin Lee +4
Unmanned Aerial Vehicle (UAV) technology is becoming increasingly used in a variety of applications such as video surveillance and deliveries. To enable safe and efficient use of U…