Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (6A): 601-605.doi: 10.11896/jsjkx.210200114

• Computer Network • Previous Articles     Next Articles

Optimization of Offloading Decisions in D2D-assisted MEC Networks

FANG Tao1, YANG Yang1, CHEN Jia-xin2   

  1. 1 College of Communications Engineering,Army Engineering University of PLA,Nanjing 210007,China
    2 College of Electronic and Information Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China
  • Online:2022-06-10 Published:2022-06-08
  • About author:FANG Tao,born in 1993,postgraduate.His main research interests include game theory,D2D technology and machine learning.
    YANG Yang,born in 1983,associate professor.Her main research interests include UAV-assisted communications and dynamic spectrum sharing in wireless communications.
  • Supported by:
    National Natural Science Foundations of China(61671474),Jiangsu Provincial Natural Science Fund for Outstanding Young Scholars(BK20180028),Jiangsu Provincial Natural Science Fund for Excellent Young Scholars(BK20170089) and Postgraduate Research and Practice Innovation Program of Jiangsu Province(KYCX19_0188).

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Abstract: Mobile edge computing(MEC) can provide convenient service for users due to the properties of resource subsidence.To further reduce the offloading pressure caused by massive users and smart applications,this paper utilizes device-to-device(D2D) technology to achieve reasonable utilization of idle local resources.That is,the compute-intensive users can offload their complex tasks to their idle neighbors in the proximity by D2D communications besides the local computing.First,the problem is formulated to minimize the aggregate delay of all users.Then,to analyze the resource competition among users and reduce the complexity,game theory is introduced and the multi-user cooperation offloading game is proposed.The proposed game is proved to be an exact potential game with at least one pure-strategy nash equilibrium(NE).Meanwhile,this paper proposes a better reply based distributed offloading algorithm to obtain the desired solution.Finally,simulation results show that the formulated game model and the proposed algorithm can decrease the network delay and average delay effectively,which validates the feasibility and effectiveness of our work.

Key words: Delay minimization, Device-to-Device, Game theory, Mobile edge computing, Offloading decision

CLC Number: 

  • TN929.5
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