Computer Science ›› 2022, Vol. 49 ›› Issue (2): 304-311.doi: 10.11896/jsjkx.210100157

• Computer Network • Previous Articles     Next Articles

Task Offloading,Migration and Caching Strategy in Internet of Vehicles Based on NOMA-MEC

ZHANG Hai-bo, ZHANG Yi-feng, LIU Kai-jian   

  1. School of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China
  • Received:2021-01-21 Revised:2021-05-06 Online:2022-02-15 Published:2022-02-23
  • About author:ZHANG Hai-bo,born in 1979,Ph.D,associate professor.His main research interests include vehicular networks and edge computing.
    ZHANG Yi-feng,born in 1995,postgraduate.His main research interests include vehicular networks and edge computing
  • Supported by:
    National Natural Science Foundation of China (61801065,61601071),Program for Changjiang Scholars and Innovative Research Team in University (IRT16R72),General Project on Foundation and Cutting-Edge Research Plan of Chongqing (cstc2018jcyjAX0463),Chongqing Innovation and Entrepreneurship Project for Returned Chinese Scholars(cx2020059).

Abstract: In the internet of vehicles systems that combining mobile edge computing (MEC) with non-orthogonal multiple access (NOMA) technology,to solve the high latency problem when user processes computationally intensive and latency-sensitive task,a strategy of task offloading,migration and cache optimization based on game theory and Q learning is proposed.Firstly,the mo-del of offloading delay,migration delay and cache delay of the internet of vehicles task based on NOMA-MEC is established.Se-condly,we use the cooperative game method to obtain the optimal user group to optimize the offloading delay.Finally,in order to avoid local optima,the Q learning algorithm is utilized to optimize the joint delay of the migration cache in the user group.The simulation results show that compared with other solutions,the proposed algorithm can effectively improve the offloading efficiency and reduce the task delay by about 22% to 43%.

Key words: Mobile edge computing, NOMA, Vehicular network

CLC Number: 

  • TN929
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