ArticleSensors (Basel, Switzerland)2021
Deep Reinforcement Learning for UAV Trajectory Design Considering Mobile Ground Users.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Joint Optimization of Packet Survivability and Aerodynamic Energy for Dynamic UAV Activation in VANETs via Deep Q-Networks.Sensors (Basel, Switzerland) · 2026Article
- MURM-A*: An Improved A* Within Comprehensive Path-Planning Scheme for Cellular-Connected Multi-UAVs Based on Radio Map and Complex Network.Sensors (Basel, Switzerland) · 2026Article
- Research on the Total Channel Capacities Pertaining to Two Coverage Layouts for Three-Dimensional, UAV-Assisted Ad Hoc Networks.Sensors (Basel, Switzerland) · 2023Article
- Handover Management for Drones in Future Mobile Networks-A Survey.Sensors (Basel, Switzerland) · 2022Review
- Mobility Management of Unmanned Aerial Vehicles in Ultra-Dense Heterogeneous Networks.Sensors (Basel, Switzerland) · 2022Review
- Parallel Cooperative Coevolutionary Grey Wolf Optimizer for Path Planning Problem of Unmanned Aerial Vehicles.Sensors (Basel, Switzerland) · 2022Article
- Deep Q-Learning-Based Transmission Power Control of a High Altitude Platform Station with Spectrum Sharing.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
A network composed of unmanned aerial vehicles (UAVs), serving as base stations (UAV-BS network), is emerging as a promising component in next-generation communication systems. In the UAV-BS network, the optimal positioning of a UAV-BS is an essential requirement to establish line-of-sight (LoS) links for ground users. A novel deep Q-network (DQN)-based learning model enabling the optimal deployment of a UAV-BS is proposed. Moreover, without re-learning of the model and the acquisition of the path information of ground users, the proposed model presents the optimal UAV-BS trajectory while ground users move. Specifically, the proposed model optimizes the trajectory of a UAV-BS by maximizing the mean opinion score (MOS) for ground users who move to various paths. Furthermore, the proposed model is highly practical because, instead of the locations of individual mobile users, an average channel power gain is used as an input parameter. The accuracy of the proposed model is validated by comparing the results of the model with those of a mathematical optimization solver.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.