Computer Science ›› 2022, Vol. 49 ›› Issue (6): 3-11.doi: 10.11896/jsjkx.220100249

• Smart IoT Technologies and Applications Empowered by 6G • Previous Articles     Next Articles

PPO Based Task Offloading Scheme in Aerial Reconfigurable Intelligent Surface-assisted Edge Computing

XIE Wan-cheng1, LI Bin1,2, DAI Yue-yue3   

  1. 1 School of Computer and Software,Nanjing University of Information Science and Technology,Nanjing 210044,China
    2 Key Lab of Broadband Wireless Communication and Sensor Network Technology (Nanjing University of Posts and Telecommunications), Ministry of Education,Nanjing 210003,China
    3 Research Center of 6G Mobile Communications and School of Cyber Science and Engineering,Huazhong University of Science and Technology,Wuhan 430074,China
  • Received:2022-01-26 Revised:2022-03-10 Online:2022-06-15 Published:2022-06-08
  • About author:XIE Wan-cheng,born in 2001,postgra-duate,is a student member of China Computer Federation.His main research interests include IoT and edge intelligence.
    LI Bin,born in 1987,Ph.D,associate professor,is a member of China Computer Federation.His main research interests include IoT and edge intelligence.
  • Supported by:
    National Natural Science Foundation of China(62101277),National Natural Science Foundation of Jiangsu Pro-vince(BK20200822),Natural Science Foundation of Jiangsu Higher Education Institutions of China(20KJB510036),Open Research Fund of Key Lab of Broadband Wireless Communication and Sensor Network Technology(Nanjing University of Posts and Telecommunications) and Ministry of Education (JZNY202103).

Abstract: In order to compensate the performance loss caused by obstacle blocking in mobile edge computing (MEC) system in 6G-enabled “intelligent Internet of Things”,this paper proposes a partial task offloading scheme supported by aerial reconfigurable intelligent surface (RIS).Firstly,we investigate the joint design of the RIS phase shift vector,the proportion of offloading task,time slot allocation,the transmit power of users and the position of UAV,formulating a non-convex problem for minimization of the total energy consumption of users.Then,the original non-convex problem is decomposed into four subproblems,and the proximal policy optimization (PPO) method in deep reinforcement learning (DRL) is utilized to provide time slot allocation.The alternative optimization (AO) is leveraged to decouple the original problem into four subproblems,including the RIS phase shift design,the convex optimization of transmit power and offloading task amount,and the UAV altitude optimization.Simulation results show that the proposed PPO model can be trained quickly,the total energy consumption of users can be reduced by about 23% and 5.3%,compared with the fully-offload strategy and fixed-UAV-height strategy,respectively.

Key words: Deep reinforcement learning, Mobile edge computing, Reconfigurable intelligent surface, Task offloading, Unmanned aerial vehicle

CLC Number: 

  • TN929.5
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