Offloading strategy with edge optimization of time delay and energy consumption in integrated satellite-terrestrial relay network

Meinan ZHANG; Mingqi ZHANG; Fei DING; Hengheng ZHUANG; Hairong MA

doi:10.11959/j.issn.2096-3750.2022.00289

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Offloading strategy with edge optimization of time delay and energy consumption in integrated satellite-terrestrial relay network

Theory and Technology | 更新时间：2024-06-05

- Offloading strategy with edge optimization of time delay and energy consumption in integrated satellite-terrestrial relay network
- Chinese Journal on Internet of Things Vol. 6, Issue 3, Pages: 124-132(2022)
- 作者机构：
  
  1. 南京邮电大学江苏省宽带无线通信和物联网重点实验室，江苏南京 210003
  2. 南京邮电大学物联网学院，江苏南京 210003
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-3750.2022.00289
  CLC： TP319
- Online First：2022-09，
  
  Published：30 September 2022
- 稿件说明：
移动端阅览
Meinan ZHANG, Mingqi ZHANG, Fei DING, et al. Offloading strategy with edge optimization of time delay and energy consumption in integrated satellite-terrestrial relay network[J]. Chinese Journal on Internet of Things, 2022, 6(3): 124-132.
DOI：

Meinan ZHANG, Mingqi ZHANG, Fei DING, et al. Offloading strategy with edge optimization of time delay and energy consumption in integrated satellite-terrestrial relay network[J]. Chinese Journal on Internet of Things, 2022, 6(3): 124-132. DOI： 10.11959/j.issn.2096-3750.2022.00289.

摘要

星地融合中继网络（ISTRN

integrated satellite-terrestrial relay network）是下一代无线通信系统的必要组成部分，对加快建成我国空天地一体化网络系统具有重要的现实意义。传统 ISTRN 架构大量的信令需要转发到地面控制中心进行处理，这增加了网络控制和管理的时延。提出了一种新型云雾计算架构，在地面接入和中心云之间构建分区域的边缘雾计算层，提高业务流管理和控制的灵活性。在该云雾网络框架下，设计了基于Q-learning的边缘计算卸载策略，以时延与能耗作为卸载性能的评价指标。仿真实验结果表明，与Min-min算法和回溯算法相比，基于Q-learning计算卸载算法的时延与能耗性能更优，且可以在时延与能耗的联合优化之间达成平衡。

Abstract

The integrated satellite-terrestrial relay network (ISTRN) is a necessary part of the next-generation wireless communication system

and has important practical significance for accelerating the construction of my country's air-space-terrestrial integrated network system.In the traditional ISTRN architecture

a large amount of signaling needs to be forwarded to the ground control center for processing

which increases the delay of network control and management.A new cloud fog computing architecture was proposed

which constructs a sub-regional edge fog computing layer between the ground access and the central cloud to improve the flexibility of business flow management and control.Under the cloud network framework

a Q-learning based edge computing offloading strategy was designed

and the offloading performance was evaluated by time delay and energy consumption.Simulation results show that

compared with Min-min algorithm and backtracking algorithm

Q-learning based computational offload algorithm has better performance in terms of time delay and energy consumption

and can achieve a balance between the joint optimization of time delay and energy consumption.

关键词

Keywords

references

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