Computation Offloading Strategy Based on Stackelberg Game and DRL

Authors

Xianwei Zhou, School of Software, South China Normal University, Foshan 528225, China;Follow
Qixu Gong, School of Software, South China Normal University, Foshan 528225, China;
Songsen Yu, School of Software, South China Normal University, Foshan 528225, China;Follow

Abstract

Abstract: To achieve the optimal computation offloading strategy for two kinds of MEC users in 5G hybrid private network, Stackelberg game is used to build the model of the competition for MEC server resources of two kinds of users, andthe strategies of complete information game and partially incomplete information game are researched respectively. It is proved that there is only one Nash equilibrium solution in the complete information scenario. In the incomplete information scenario, the environment is modeled as POMDP, and a two-stage deep reinforcement learning(TSDRL) is proposed to obtain the optimal computation offloading strategy. Simulation results show the proposed algorithm having a total reduction of 20.81% time delay and 3.38 % energy consumption compared with the D-DRL algorithm and can effectively improve the user QoE(quality of experience).

Recommended Citation

Zhou, Xianwei; Gong, Qixu; and Yu, Songsen (2023) "Computation Offloading Strategy Based on Stackelberg Game and DRL," Journal of System Simulation: Vol. 35: Iss. 2, Article 14.
DOI: 10.16182/j.issn1004731x.joss.21-1118
Available at: https://dc-china-simulation.researchcommons.org/journal/vol35/iss2/14

First Page

372

Revised Date

2022-01-07

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.21-1118

Last Page

385

CLC

TP393.01

Recommended Citation

Xianwei Zhou, Qixu Gong, Songsen Yu. Computation Offloading Strategy Based on Stackelberg Game and DRL[J]. Journal of System Simulation, 2023, 35(2): 372-385.

Corresponding Author

Songsen Yu,yss8109@163.com

DOI

10.16182/j.issn1004731x.joss.21-1118