The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Multi-Agent Reinforcement Learning Based Resource Management in MEC- and UAV-Assisted Vehicular Networks
Abstract
In this paper, we investigate multi-dimensional resource management for unmanned aerial vehicles (UAVs) assisted vehicular networks. To efficiently provide on-demand resource access, the macro eNodeB and UAV, both mounted with multi-access edge computing (MEC) servers, cooperatively make association decisions and allocate proper amounts of resources to vehicles. Since there is no central controller, we formulate the resource allocation at the MEC servers as a distributive optimization problem to maximize the number of offloaded tasks while satisfying their heterogeneous quality-of-service (QoS) requirements, and then solve it with a multi-agent deep deterministic policy gradient (MADDPG)-based method. Through centrally training the MADDPG model offline, the MEC servers, acting as learning agents, then can rapidly make vehicle association and resource allocation decisions during the online execution stage. From our simulation results, the MADDPG-based method can converge within 200 training episodes, comparable to the single-agent DDPG (SADDPG)-based one. Moreover, the proposed MADDPG-based resource management scheme can achieve higher delay/QoS satisfaction ratios than the SADDPG-based and random schemes.
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Cites
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Deep Reinforcement Learning Based Resource Allocation for V2V Communications
Ye, H., Li, G. Y., & Juang, B.-H. F. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2897134 · Cited by 870
Computation Rate Maximization in UAV-Enabled Wireless-Powered Mobile-Edge Computing Systems
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Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks
Nasir, Y. S., & Guo, D. (2019) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2019.2933973 · Cited by 660
Multi-Agent Reinforcement Learning-Based Resource Allocation for UAV Networks
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Learning-Based Computation Offloading for IoT Devices With Energy Harvesting
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Cited by
Resource Scheduling in Edge Computing: A Survey
Luo, Q., Hu, S., Li, C., Li, G., & Shi, W. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3106401 · Cited by 588
Optimizing Federated Learning in Distributed Industrial IoT: A Multi-Agent Approach
Zhang, W., Yang, D., Wu, W., Peng, H., Zhang, N., Zhang, H., & Shen, X. (2021) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2021.3118352 · Cited by 300
Self-citationRADiT: Resource Allocation in Digital Twin-Driven UAV-Aided Internet of Vehicle Networks
Hazarika, B., Singh, K., Li, C.-P., Schmeink, A., & Tsang, K. F. (2023) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2023.3310048 · Cited by 109
Network Slicing-Based Learning Techniques for IoV in 5G and Beyond Networks
Hamdi, W., Ksouri, C., Bulut, H., & Mosbah, M. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3372083 · Cited by 63
Distributed resource scheduling in edge computing: Problems, solutions, and opportunities
Sahni, Y., Cao, J., Yang, L., & Wang, S. (2022) · Computer Networks · doi:10.1016/j.comnet.2022.109430 · Cited by 38
Metadata
- Authors
- Peng, H., & Shen, X.
- Venue
- IEEE Journal on Selected Areas in Communications
- Published
- 2021-01
- Volume
- 39
- Issue
- 1
- Pages
- 131-141
- ISSN
- 0733-8716 (ISSN-L)
- 1558-0008
- OpenAlex
- W3105649577
- Cited by
- 521
- References
- 36
- FWCI
- 36.64
- Type
- journal article
- Language
- en
- Open access
- closed
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