Wang et al., 2022 - Google Patents
Technical report for trend prediction based intelligent UAV trajectory planning for large-scale dynamic scenariosWang et al., 2022
View PDF- Document ID
- 10666313649747034609
- Author
- Wang J
- Wang X
- Publication year
- Publication venue
- arXiv preprint arXiv:2209.08235
External Links
Snippet
The unmanned aerial vehicle (UAV)-enabled communication technology is regarded as an efficient and effective solution for some special application scenarios where existing terrestrial infrastructures are overloaded to provide reliable services. To maximize the utility …
- 238000004891 communication 0 abstract description 22
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Sun et al. | AoI-energy-aware UAV-assisted data collection for IoT networks: A deep reinforcement learning method | |
Zhang et al. | Energy-efficient trajectory optimization for UAV-assisted IoT networks | |
Yang et al. | Privacy-preserving federated learning for UAV-enabled networks: Learning-based joint scheduling and resource management | |
Song et al. | Evolutionary multi-objective reinforcement learning based trajectory control and task offloading in UAV-assisted mobile edge computing | |
Ding et al. | Trajectory design and access control for air–ground coordinated communications system with multiagent deep reinforcement learning | |
Do et al. | Deep reinforcement learning for energy-efficient federated learning in UAV-enabled wireless powered networks | |
CN110736478B (en) | A path planning and task assignment scheme for UAV-assisted mobile cloud perception | |
Shi et al. | A novel deep Q-learning-based air-assisted vehicular caching scheme for safe autonomous driving | |
CN110380776B (en) | Internet of things system data collection method based on unmanned aerial vehicle | |
CN113905347A (en) | A cloud-side-end collaboration method for air-ground integrated power Internet of things | |
Liu et al. | Deep-reinforcement-learning-based optimal transmission policies for opportunistic UAV-aided wireless sensor network | |
Wang et al. | Ensuring threshold AoI for UAV-assisted mobile crowdsensing by multi-agent deep reinforcement learning with transformer | |
Taimoor et al. | Holistic resource management in UAV-assisted wireless networks: An optimization perspective | |
Zhao et al. | Adaptive multi-UAV trajectory planning leveraging digital twin technology for urban IIoT applications | |
Chen et al. | Traffic prediction-assisted federated deep reinforcement learning for service migration in digital twins-enabled MEC networks | |
Manalastas et al. | Where to go next?: A realistic evaluation of AI-assisted mobility predictors for HetNets | |
Romaniuk et al. | Objective control functions of FANET communication nodes of land-air network | |
CN117119489A (en) | Deployment and resource optimization method of wireless energy supply network based on multi-unmanned aerial vehicle assistance | |
Chen et al. | DRL based partial offloading for maximizing sum computation rate of FDMA-based wireless powered mobile edge computing | |
Wei et al. | DRL-based energy-efficient trajectory planning, computation offloading, and charging scheduling in UAV-MEC network | |
Wang et al. | Trajectory planning of UAV-enabled data uploading for large-scale dynamic networks: A trend prediction based learning approach | |
Zhang et al. | Completion time minimization for data collection in a UAV-enabled IoT network: A deep reinforcement learning approach | |
Wang et al. | Energy efficiency optimization of IRS and UAV-assisted wireless powered edge networks | |
Wu et al. | UAV-Mounted RIS-Aided Mobile Edge Computing System: A DDQN-Based Optimization Approach | |
CN118764879A (en) | A RIS-assisted multi-UAV energy-efficient and fair communication coverage method |