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Found 8 papers, 0 papers with code
Density-Aware Reinforcement Learning to Optimise Energy Efficiency in UAV-Assisted Networks
Unmanned aerial vehicles (UAVs) serving as aerial base stations can be deployed to provide wireless connectivity to mobile users, such as vehicles.
Deep Reinforcement Learning for Combined Coverage and Resource Allocation in UAV-aided RAN-slicing
This work presents a UAV-assisted 5G network, where the aerial base stations (UAV-BS) are empowered with network slicing capabilities aiming at optimizing the Service Level Agreement (SLA) satisfaction ratio of a set of users.
Optimising Energy Efficiency in UAV-Assisted Networks using Deep Reinforcement Learning
In this letter, we study the energy efficiency (EE) optimisation of unmanned aerial vehicles (UAVs) providing wireless coverage to static and mobile ground users.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Multi-Agent Deep Reinforcement Learning For Optimising Energy Efficiency of Fixed-Wing UAV Cellular Access Points
In this paper, we propose a multi-agent deep reinforcement learning approach to optimise the energy efficiency of fixed-wing UAV cellular access points while still allowing them to deliver high-quality service to users on the ground.
Energy-aware optimization of UAV base stations placement via decentralized multi-agent Q-learning
Unmanned aerial vehicles serving as aerial base stations (UAV-BSs) can be deployed to provide wireless connectivity to ground devices in events of increased network demand, points-of-failure in existing infrastructure, or disasters.
Mobility for Cellular-Connected UAVs: challenges for the network provider
Unmanned Aerial Vehicle (UAV) technology is becoming more prevalent and more diverse in its application.
Networking and Internet Architecture
Experimental Evaluation of a UAV User QoS from a Two-Tier 3.6GHz Spectrum Network
Our results show that increasing the UAV height reduces the performance in both tiers, due to issues such as antenna misalignment.
Networking and Internet Architecture
Adaptive Height Optimisation for Cellular-Connected UAVs using Reinforcement Learning
On the other hand, BS density can negatively impact UAV QoS, with higher numbers of BSs generating more interference and deteriorating UAV performance.
