[HTML][HTML] Reinforcement learning-based unmanned aerial vehicle trajectory planning for ground users' mobility management in heterogeneous networks
The surge of data traffic in wireless networks necessitates the provision of high-quality data
services to meet users' satisfaction levels. However, the limited spectral resources of the …
services to meet users' satisfaction levels. However, the limited spectral resources of the …
Deep Reinforcement Learning Algorithms for Location Optimization in Multi-RAT UAV-Assisted Heterogeneous Networks
MG Anany, MM Elmesalawy, II Ibrahim… - 2023 5th Novel …, 2023 - ieeexplore.ieee.org
Due to the unprecedented growth in wireless mobile connectivity, the next generations of
wireless networks are required to meet the different requirements of the diverse devices …
wireless networks are required to meet the different requirements of the diverse devices …
Bayesian optimization enhanced deep reinforcement learning for trajectory planning and network formation in multi-UAV networks
In this paper, we employ multiple UAVs coordinated by a base station (BS) to help the
ground users (GUs) to offload their sensing data. Different UAVs can adapt their trajectories …
ground users (GUs) to offload their sensing data. Different UAVs can adapt their trajectories …
HAPS-UAV-enabled heterogeneous networks: A deep reinforcement learning approach
The integrated use of non-terrestrial network (NTN) entities such as the high-altitude
platform station (HAPS) and low-altitude platform station (LAPS) has become essential …
platform station (HAPS) and low-altitude platform station (LAPS) has become essential …
A Hybrid Scheme Using TOPSIS and Q-Learning for Handover Decision Making in UAV Assisted Heterogeneous Network
An increasing number of users are expected to be served by wireless network with
heterogeneous requirements. Unmanned aerial vehicles (UAV) can be deployed to …
heterogeneous requirements. Unmanned aerial vehicles (UAV) can be deployed to …
Machine Learning Techniques for UAV-Assisted Networks
A Shahbazi - 2022 - theses.hal.science
The main focus of this thesis is on modeling, performance evaluation and system-level
optimization of next-generation cellular networks empowered by Unmanned Aerial Vehicles …
optimization of next-generation cellular networks empowered by Unmanned Aerial Vehicles …
Mobility management for cellular-connected UAVs: A learning-based approach
MMU Chowdhury, W Saad… - 2020 IEEE international …, 2020 - ieeexplore.ieee.org
The pervasiveness of the wireless cellular network can be a key enabler for the deployment
of autonomous unmanned aerial vehicles (UAVs) in beyond visual line of sight scenarios …
of autonomous unmanned aerial vehicles (UAVs) in beyond visual line of sight scenarios …
Empowering adaptive geolocation-based routing for UAV networks with reinforcement learning
Since unmanned aerial vehicles (UAVs), such as drones, are used in various fields due to
their high utilization and agile mobility, technologies to deal with multiple UAVs are …
their high utilization and agile mobility, technologies to deal with multiple UAVs are …
Efficient deployment with throughput maximization for UAVs communication networks
The article presents a throughput maximization approach for UAV assisted ground networks.
Throughput maximization involves minimizing delay and packet loss through UAV trajectory …
Throughput maximization involves minimizing delay and packet loss through UAV trajectory …
Aero5GBS—Deep learning-empowered ground users handover in aerial 5G and beyond systems
AAL Queiroz, MKS Barbosa, KL Dias - IEEE Access, 2023 - ieeexplore.ieee.org
Recently, Unmanned Aerial Vehicles (UAVs) have been envisioned as aerial base stations,
UAV-BS, serving ground users independently of the traditional cellular infrastructure. It is …
UAV-BS, serving ground users independently of the traditional cellular infrastructure. It is …
相关搜索
- ground users heterogeneous networks
- mobility management in heterogeneous networks
- uav networks reinforcement learning
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- path selection reinforcement learning
- heterogeneous network hybrid scheme
- trajectory planning network formation
- trajectory planning deep reinforcement
- location optimization heterogeneous networks
- trajectory planning bayesian optimization
- uav networks path selection
- multi-uav networks
- deep reinforcement network formation
- efficient deployment communication networks
- throughput maximization communication networks
- bayesian optimization network formation