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Deep Reinforcement Learning‐Based Intelligent Resource Management in Multi‐UAVs‐Assisted MEC Emergency Communication System.

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Title: Deep Reinforcement Learning‐Based Intelligent Resource Management in Multi‐UAVs‐Assisted MEC Emergency Communication System.
Authors: Lin, Yuanmo1,2 (AUTHOR), Xu, Zhiyong1 (AUTHOR) njxzy123@163.com, Li, Jianhua1 (AUTHOR), Wang, Jingyuan1 (AUTHOR), Li, Cheng1 (AUTHOR)
Source: IET Communications (Wiley-Blackwell). Jan2025, Vol. 19 Issue 1, p1-14. 14p.
Subjects: Reinforcement learning, Edge computing, Dynamic spectrum access, Resource management, Drone aircraft, Emergency communication systems, Distributed computing
Abstract: This paper investigates a multi unmanned aerial vehicles (UAVs) assisted mobile edge computing (MEC) emergency communication system in which each UAV acts as a mobile MEC server for computing tasks offloaded by ground sensor users. Considering the stochastic dynamic characteristics of multi‐UAVs‐assisted MEC systems and the precision of spectrum resources, the deep reinforcement learning (DRL) algorithm and the non‐orthogonal multiple access (NOMA) techniques are introduced. Specifically, we design an offloading algorithm based on a multi‐agent deep deterministic policy gradient that jointly optimizes the UAVs' flight trajectories, the sensors' offloading powers, and the dynamic spectrum access to maximize the number of successfully offloaded tasks. The algorithm employs the Gumbel‐Softmax method to effectively control both the discrete sensor access action and the continuous offloading power action. Sufficient simulation results show that the proposed algorithm performs significantly better than other benchmark algorithms. [ABSTRACT FROM AUTHOR]
Copyright of IET Communications (Wiley-Blackwell) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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PubType: Academic Journal
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  Data: Deep Reinforcement Learning‐Based Intelligent Resource Management in Multi‐UAVs‐Assisted MEC Emergency Communication System.
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  Data: <searchLink fieldCode="JN" term="%22IET+Communications+%28Wiley-Blackwell%29%22">IET Communications (Wiley-Blackwell)</searchLink>. Jan2025, Vol. 19 Issue 1, p1-14. 14p.
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  Data: <searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22Edge+computing%22">Edge computing</searchLink><br /><searchLink fieldCode="DE" term="%22Dynamic+spectrum+access%22">Dynamic spectrum access</searchLink><br /><searchLink fieldCode="DE" term="%22Resource+management%22">Resource management</searchLink><br /><searchLink fieldCode="DE" term="%22Drone+aircraft%22">Drone aircraft</searchLink><br /><searchLink fieldCode="DE" term="%22Emergency+communication+systems%22">Emergency communication systems</searchLink><br /><searchLink fieldCode="DE" term="%22Distributed+computing%22">Distributed computing</searchLink>
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  Data: This paper investigates a multi unmanned aerial vehicles (UAVs) assisted mobile edge computing (MEC) emergency communication system in which each UAV acts as a mobile MEC server for computing tasks offloaded by ground sensor users. Considering the stochastic dynamic characteristics of multi‐UAVs‐assisted MEC systems and the precision of spectrum resources, the deep reinforcement learning (DRL) algorithm and the non‐orthogonal multiple access (NOMA) techniques are introduced. Specifically, we design an offloading algorithm based on a multi‐agent deep deterministic policy gradient that jointly optimizes the UAVs' flight trajectories, the sensors' offloading powers, and the dynamic spectrum access to maximize the number of successfully offloaded tasks. The algorithm employs the Gumbel‐Softmax method to effectively control both the discrete sensor access action and the continuous offloading power action. Sufficient simulation results show that the proposed algorithm performs significantly better than other benchmark algorithms. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of IET Communications (Wiley-Blackwell) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1049/cmu2.70063
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      – Code: eng
        Text: English
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        PageCount: 14
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    Subjects:
      – SubjectFull: Reinforcement learning
        Type: general
      – SubjectFull: Edge computing
        Type: general
      – SubjectFull: Dynamic spectrum access
        Type: general
      – SubjectFull: Resource management
        Type: general
      – SubjectFull: Drone aircraft
        Type: general
      – SubjectFull: Emergency communication systems
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      – SubjectFull: Distributed computing
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      – TitleFull: Deep Reinforcement Learning‐Based Intelligent Resource Management in Multi‐UAVs‐Assisted MEC Emergency Communication System.
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            NameFull: Lin, Yuanmo
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            NameFull: Li, Jianhua
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              M: 01
              Text: Jan2025
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              Y: 2025
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