Si Li
ABSTRACT
Unmanned Aerial Vehicles (UAVs) equipped with Multi-Access Edge Computing (MEC) servers can assist Terminal Devices (TDs) in offloading data tasks. In this paper, we investigate a resource allocation and trajectory optimization problem of multiple UAVs assisting TDs in task computation, and our main goal is to improve the task computation efficiency of the system to meet the high-quality experience of TDs. We consider the fairness of TD's computing data volume and the fairness of UAV energy consumption. The problem is transformed into a Partially Observable Markov Decision Process (POMDP). The large action space generated during the UAV flight and resource allocation decision-making process leads to a policy overfitting problem for Multi-Agent Proximal Policy Optimization (MAPPO) method. Policy overfitting causes the UAV to update the policy gradient in the suboptimization direction, preventing it from exploring better flight trajectories. To meet this challenge, we propose a novel method of policy regularization, NV-MAPPO. Simulation results show that NV-MAPPO has significant advantages in latency and energy consumption.
- Zhou F, Hu R, Mobile Edge Computing in Unmanned Aerial Vehicle Networks. IEEE Wireless Communications, 2019, 27(2): 140-146.Google Scholar
- Mao Y, You C, A Survey on Mobile Edge Computing: The Communication Perspective. IEEE Communications Surveys & Tutorials, 2017, 19(8): 2322-2358.Google ScholarCross Ref
- Ren T, Niu J, Enabling Efficient Scheduling in Large-Scale UAV-Assisted Mobile-Edge Computing via Hierarchical Reinforcement Learning. IEEE Internet of Things Journal, 2021, 9(6): 7095-7109.Google Scholar
- Liu L, Wang A, Multi objective Optimization for Improving Throughput and Energy Efficiency in UAV-Enabled IoT. IEEE Internet of Things Journal, 2022, 9(11): 20763-20777.Google ScholarCross Ref
- Li A, Dai L, Resource allocation for multi-UAV-assisted mobile edge computing to minimize weighted energy consumption. IET Communications, 2022, 16(2): 2070-2081.Google ScholarDigital Library
- Ji J, Zhu K, Energy Consumption Minimization in UAV-Assisted Mobile-Edge Computing Systems: Joint Resource Allocation and Trajectory Design. IEEE Internet of Things Journal, 2021, 8(5): 8570-8584.Google ScholarCross Ref
- Ei N, Alsenwi M, Energy-Efficient Resource Allocation in Multi-UAV-Assisted Two-Stage Edge Computing for Beyond 5G Networks. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(9): 16421-16432.Google ScholarDigital Library
- Lu W, Mo Y, Secure Transmission for Multi-UAV-Assisted Mobile Edge Computing Based on Reinforcement Learning. IEEE Transactions on Network Science and Engineering, 2023, 10(7): 1270-1282.Google ScholarCross Ref
- Chang H, Chen Y, Multi-UAV Mobile Edge Computing and Path Planning Platform Based on Reinforcement Learning. IEEE Transactions on Emerging Topics in Computational Intelligence, 2021, 6(13): 489-498.Google Scholar
- Zhou X, Huang L, Computation Bits Maximization in UAV-Assisted MEC Networks with Fairness Constraint. IEEE Internet of Things Journal, 2022, 9(4): 20997-21009.Google ScholarCross Ref
- Lee, J, Vasilis F. Energy Consumption Fairness for Multiple Flying Base Stations. 2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications, 2020: 1-5.Google Scholar
- Doshi A, A Deep Reinforcement Learning Framework for Contention-Based Spectrum Sharing. IEEE Journal on Selected Areas in Communications, 2021, 39(9): 2526-2540.Google ScholarCross Ref
- He Y, Gan Y, Fairness-Based 3-D Multi-UAV Trajectory Optimization in Multi-UAV-Assisted MEC System. IEEE Internet of Things Journal, 2023, 10(6): 11383-11395.Google Scholar
- Zhu C, Zhang G, Fairness-Aware Task Loss Rate Minimization for Multi-UAV Enabled Mobile Edge Computing. IEEE Wireless Communications Letters, 2023, 12(6): 94-98.Google ScholarCross Ref
- Sun H, Zhou F, Joint Offloading and Computation Energy Efficiency Maximization in a Mobile Edge Computing System. IEEE Transactions on Vehicular Technology, 2019, 68(2): 3052-3056.Google Scholar
Index Terms
- Fairness-Aware Computation Efficiency Maximization for Multi-UAV-Enabled MEC System
Recommendations
Joint optimization of trajectory and resource allocation in cellular-connected multi-UAV MEC networks
AbstractUnmanned aerial vehicles (UAVs)-based mobile edge computing (MEC) has been introduced as a promising model for enhanced edge communications in the future of integrated air-sky-earth and sea communications. However, in UAV-assisted mobile edge ...
Joint Computation Offloading and Communication Design for Secure UAV-Enabled MEC Systems
2021 IEEE Wireless Communications and Networking Conference (WCNC)In this paper, we investigate a secure unmanned aerial vehicle (UAV) enabled mobile edge computing (MEC) system, where multiple ground users and a UAV collaborate to complete computing tasks in the presence of multiple eavesdroppers on the ground with ...
Collaborative offloading for UAV-enabled time-sensitive MEC networks
AbstractRecently, unmanned aerial vehicle (UAV) acts as the aerial mobile edge computing (MEC) node to help the battery-limited Internet of Things (IoT) devices relieve burdens from computation and data collection, and prolong the lifetime of operating. ...
Comments