location:Home > 2024 Vol.7 Oct.N05 > Computation offloading for mobile edge computing with Cloud-Edge-End Collaboration based on reinforcement learning

2024 Vol.7 Oct.N05

  • Title: Computation offloading for mobile edge computing with Cloud-Edge-End Collaboration based on reinforcement learning
  • Name: Sibin Liu,Wen Chen,Yuxiao Yang,Wenjing Hu
  • Company: School of Information Science and Technology, Donghua University, Shanghai 201620 China
  • Abstract:

    Computational offloading is a hot research issue in mobile edge computing. However, most existing studies do not focus on computational offloading in the presence of interference between cellular networks. Neglecting co-channel interference can lead to inappropriate offloading decisions. To address this problem, this paper formulates the computational offloading problem as an integer nonlinear programming (INP) problem and proposes a Deep Deterministic Policy Gradient (DDPG)-based reinforcement learning algorithm to optimize the offloading policy. Simulation results show that the method performs well in reducing the total system cost compared to the benchmark methods.

     


  • Keyword: Mobile edge computing; Computational offloading; Reinforcement learning;
  • DOI: 10.12250/jpciams2024090105
  • Citation form: Sibin Liu,Wen Chen,Yuxiao Yang,Wenjing Hu.Computation offloading for mobile edge computing with Cloud-Edge-End Collaboration based on reinforcement learning[J]. Computer Informatization and Mechanical System,2024,Vol.7,pp. 20-23
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[3] ZHANG J, TIAN H, XIONG Kun, et al. Fair multi-party private set intersection protocol based on cloud server [J] Computer Applications, 2023, 43 (9): 2806-2811.


Tsuruta Institute of Medical Information Technology
Address:[502,5-47-6], Tsuyama, Tsukuba, Saitama, Japan TEL:008148-28809 fax:008148-28808 Japan,Email:jpciams@hotmail.com,2019-09-16