Dynamic computing offloading strategy in LEO constellation edge computing network

GAO Yufang; JI Zhi; ZHAO Kanglian; LI Wenfeng; HU Peicong

doi:10.11959/j.issn.1000-436x.2024065

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Dynamic computing offloading strategy in LEO constellation edge computing network

Topics：Low Earth Orbit Satellite Internet | 更新时间：2024-08-09

- Dynamic computing offloading strategy in LEO constellation edge computing network
- Journal on Communications Vol. 45, Issue 7, Pages: 61-69(2024)
- 作者机构：
  
  1.南京大学电子科学与工程学院,江苏南京 210046
  2.陆军工程大学通信工程学院,江苏南京 210007
  3.中国人民解放军63893部队,河南洛阳471003
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(62131012);The Fundamental Research Funds for the Central Universities(021014380187)
- DOI：10.11959/j.issn.1000-436x.2024065
  CLC： TN92
- Received：12 October 2023，
  
  Revised：2024-01-14，
  
  Published：25 July 2024
- 稿件说明：
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高玉芳,姬智,赵康僆等.LEO星座边缘计算网络中的动态计算卸载策略[J].通信学报,2024,45(07):61-69.

GAO Yufang,JI Zhi,ZHAO Kanglian,et al.Dynamic computing offloading strategy in LEO constellation edge computing network[J].Journal on Communications,2024,45(07):61-69.
高玉芳,姬智,赵康僆等.LEO星座边缘计算网络中的动态计算卸载策略[J].通信学报,2024,45(07):61-69. DOI： 10.11959/j.issn.1000-436x.2024065.

GAO Yufang,JI Zhi,ZHAO Kanglian,et al.Dynamic computing offloading strategy in LEO constellation edge computing network[J].Journal on Communications,2024,45(07):61-69. DOI： 10.11959/j.issn.1000-436x.2024065.

摘要

在卫星边缘计算网络中，当过多用户通过同一信道接入卫星时，产生的同信道干扰会导致边缘计算性能下降。为了解决该难题，在动态环境低地球轨道（LEO）星座边缘计算网络的系统模型下，提出了一种基于随机博弈的多用户计算卸载策略。在考虑用户的自私性、星地信道的随机特性和地面用户接入的动态特性的前提下，从博弈论的角度，将动态环境下地面用户的卸载决策过程表述为随机博弈，证明了所制定的随机博弈等价于具有至少一个纳什均衡（NE）的加权势博弈，并且NE最小化系统开销。为了在动态环境下以分布式方式达到NE，基于随机学习设计了一种智能随机学习算法，以高效达到所提随机博弈的NE。仿真结果表明，与基准算法相比，所提算法能够显著降低同信道干扰和系统开销，并达到接近最优的性能。

Abstract

In satellite edge computing networks

when too many ground users access the satellite through the same channel

the resulting co-channel interference will lead to edge computing performance degradation. To address this problem

a multi-user computing offloading strategy based on stochastic game was proposed under the system model of dynamic environment low earth orbit constellation edge computing network. On the premise of considering the selfishness of users

the stochastic characteristics of the satellite-ground channel and the dynamic nature of ground user access

from the perspective of game theory

the offloading decision-making process of ground users in the dynamic environment was formulated as a stochastic game. It was proved that the formulated stochastic game was equivalent to a weighted potential game with at least one Nash Equilibrium (NE)

and the NE minimized the system overhead. In order to achieve NE in a distributed manner under dynamic environment

an intelligent stochastic learning algorithm based on the stochastic learning was designed to efficiently achieve NE for the proposed stochastic game. Simulation results show that compared to the benchmark algorithm

the proposed algorithm can significantly reduce the co-channel interference and the system overhead

and achieve near-optimal performance.

关键词

Keywords

references

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