mlsanityData to 7 Oct 2026

Path Planning for UAV-Mounted Mobile Edge Computing with Deep Reinforcement Learning

arXiv 2001.10268 ↗First posted 28 Jan 2020cs.IT eess.SP math.ITcited by 19

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Abstract (arXiv)

In this letter, we study an unmanned aerial vehicle (UAV)-mounted mobile edge computing network, where the UAV executes computational tasks offloaded from mobile terminal users (TUs) and the motion of each TU follows a Gauss-Markov random model. To ensure the quality-of-service (QoS) of each TU, the UAV with limited energy dynamically plans its trajectory according to the locations of mobile TUs. Towards this end, we formulate the problem as a Markov decision process, wherein the UAV trajectory and UAV-TU association are modeled as the parameters to be optimized. To maximize the system reward and meet the QoS constraint, we develop a QoS-based action selection policy in the proposed algorithm based on double deep Q-network. Simulations show that the proposed algorithm converges more quickly and achieves a higher sum throughput than conventional algorithms.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Jan 2020.

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Citing papers per year: 2021: 3; 2022: 6; 2023: 2; 2024: 3; 2025: 5.

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  1. 2022Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Surveycited by 27
  2. 2023Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problemscited by 15
  3. 2022Deep Reinforcement Learning for Trajectory Path Planning and Distributed Inference in Resource-Constrained UAV Swarmscited by 5
  4. 2022Asynchronous Curriculum Experience Replay: A Deep Reinforcement Learning Approach for UAV Autonomous Motion Control in Unknown Dynamic Environmentscited by 4
  5. 2022UAV-Assisted Space-Air-Ground Integrated Networks: A Technical Review of Recent Learning Algorithmscited by 3
  6. 2025Decision Transformers for RIS-Assisted Systems with Diffusion Model-Based Channel Acquisitioncited by 3
  7. 2021Multi-UAV Mobile Edge Computing and Path Planning Platform based on Reinforcement Learningcited by 2
  8. 2022Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligencecited by 2
  9. 2024Two-Timescale Synchronization and Migration for Digital Twin Networks: A Multi-Agent Deep Reinforcement Learning Approachcited by 2
  10. 2025Enhancing Disaster Resilience with UAV-Assisted Edge Computing: A Reinforcement Learning Approach to Managing Heterogeneous Edge Devicescited by 2
  11. 2025Conditional Diffusion Model-Driven Generative Channels for Double RIS-Aided Wireless Systemscited by 2
  12. 2022Collaborative Intelligent Reflecting Surface Networks with Multi-Agent Reinforcement Learningcited by 1
7 more
  1. 2025The Emergence of Deep Reinforcement Learning for Path Planningcited by 1
  2. 2025AirFed: A Federated Graph-Enhanced Multi-Agent Reinforcement Learning Framework for Multi-UAV Cooperative Mobile Edge Computingcited by 1
  3. 2021Multi-Agent Path Planning based on MPC and DDPGcited by 0
  4. 2021Computation Rate Maximum for Mobile Terminals in UAV-assisted Wireless Powered MEC Networks with Fairness Constraintcited by 0
  5. 2023Trip Planning for Autonomous Vehicles with Wireless Data Transfer Needs Using Reinforcement Learningcited by 0
  6. 2024Resource Efficient Asynchronous Federated Learning for Digital Twin Empowered IoT Networkcited by 0
  7. 2024Adversarial Federated Consensus Learning for Surface Defect Classification Under Data Heterogeneity in IIoTcited by 0