边缘计算中基于QoE感知的任务卸载：势博弈方法

师子琦; 谢刚; 范文浩; 刘元安

doi:10.12263/DZXB.20241053

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边缘计算中基于QoE感知的任务卸载：势博弈方法

学术论文 | 更新时间：2025-10-16

- 边缘计算中基于QoE感知的任务卸载：势博弈方法
- QoE-Aware Task Offloading in Edge Computing Environment: Potential Game Approach
- 电子学报 2025年53卷第6期页码：1943-1957
- 作者机构：
  
  1.北京邮电大学信息与通信工程学院，北京 100876
  2.北京邮电大学电子工程学院，北京 100876
- 作者简介：
  
  [ "师子琦男，现为北京邮电大学信息与通信工程学院硕士研究生.主要研究方向为边缘计算以及博弈论. E-mail: shiziqi@bupt.edu.cn" ]
  [ "谢刚男，现为北京邮电大学信息与通信工程学院副教授.主要研究方向为无线通信关键技术. E-mail: xiegang@bupt.edu.cn" ]
  [ "范文浩男，博士，现为北京邮电大学电子工程学院副教授.主要研究方向为边缘计算、通感算智融合网络以及物联网技术. E-mail: whfan@bupt.edu.cn" ]
  [ "刘元安男，现为北京邮电大学电子工程学院教授，博士生导师.主要研究方向为电磁场与微波技术.E-mail: yuliu@bupt.edu.cn" ]
- 基金信息：
  
  北京市自然科学基金-海淀原始创新联合基金(L242080);国家自然科学基金(62271086)
- DOI：10.12263/DZXB.20241053
  中图分类号： TN929.5;TP391.9
- 收稿：2024-11-23，
  
  修回：2025-03-11，
  
  纸质出版：2025-06-25
- 稿件说明：
移动端阅览
师子琦, 谢刚, 范文浩, 等. 边缘计算中基于QoE感知的任务卸载：势博弈方法[J]. 电子学报, 2025, 53(06): 1943-1957.

SHI Zi-qi, XIE Gang, FAN Wen-hao, et al. QoE-Aware Task Offloading in Edge Computing Environment: Potential Game Approach[J]. Acta Electronica Sinica, 2025, 53(06): 1943-1957.
师子琦, 谢刚, 范文浩, 等. 边缘计算中基于QoE感知的任务卸载：势博弈方法[J]. 电子学报, 2025, 53(06): 1943-1957. DOI：10.12263/DZXB.20241053

SHI Zi-qi, XIE Gang, FAN Wen-hao, et al. QoE-Aware Task Offloading in Edge Computing Environment: Potential Game Approach[J]. Acta Electronica Sinica, 2025, 53(06): 1943-1957. DOI：10.12263/DZXB.20241053

摘要

随着5G的广泛应用，边缘计算技术被用于任务卸载和处理，基于博弈论的边缘计算策略成为当前研究领域中的热点.本文以最大化用户体验质量（Quality of Experience，QoE）为目标，研究时间约束条件下的多用户任务卸载问题.本文首先从通信模型、计算模型和时间约束三个方面建立系统模型，然后将优化问题转换为博弈问题，给出并证明存在纳什均衡解.本文提出了一种分布式多用户卸载算法（Distributed Multi-User Offloading，DMUO），首次实现了多用户在单时隙内同步更新策略，显著降低了计算开销并提升了收敛速度.理论分析表明，DMUO算法能够收敛至纳什均衡解，并给出了迭代次数的上限.此外，通过分析最坏情况策略与最优解的性能差距，验证了算法的鲁棒性.仿真实验表明，DMUO算法具有优异的收敛性和系统性能，证明了其在大规模边缘计算环境中的可扩展性和实际适用性.

Abstract

With the widespread adoption of 5G technology

the importance of edge computing in task offloading and processing has become increasingly prominent. Consequently

this trend has led to the emergence of game theory-based edge computing strategies as a research hot spot. This paper aims to maximize the quality of experience (QoE) by studying the multi-user task offloading problem under time constraints. A system model is constructed from three aspects: communication model

computation model

and time constraints. The optimization problem is first transformed into a game-theoretic problem

and thereafter

the existence of a Nash equilibrium solution is proven. This paper proposes a distributed multi-user offloading (DMUO) algorithm

which enables multiple users to simultaneously update their policies within a single time slot for the first time

significantly reducing computational overhead and improving convergence speed. Theoretical analysis not only demonstrates that the DMUO algorithm converges to the Nash equilibrium solution

but also provides an upper bound on the number of iterations.Furthermore

the robustness of the algorithm is verified by analyzing the performance gap between the worst-case strategy and the optimal solution. Simulation results show that the DMUO algorithm exhibits excellent convergence and system performance

proving its scalability and practical applicability in large-scale edge computing environments

关键词

Keywords

references

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