UAV-assisted MEC offloading strategy with peak AOI boundary optimization:A method based on DDQN  

作　　者：Zhixiong Chen Jiawei Yang Zhenyu Zhou 

机构地区：[1]School of Electrical and Electronic Engineering,North China Electric Power University,Baoding,071003,China [2]Hebei Key Laboratory of Power Internet of Things Technology,North China Electric Power University,Baoding,7003,Hebe,China [3]State Grid Shaanxi Electric Power Co.,Ltd.Information and Communication Company,Xian,710048,Shanxi,China

出　　处：《Digital Communications and Networks》2024年第6期1790-1803,共14页数字通信与网络(英文版)

基　　金：supported in part by the National Natural Science Foundation of China(Nos.61601182);in part by the Fundamental Research Funds for the Central Universities under Grant 2023MS113。

摘　　要：In response to the requirements for large-scale device access and ultra-reliable and low-latency communication in the power internet of things,unmanned aerial vehicle-assisted multi-access edge computing can be used to realize flexible access to power services and update large amounts of information in a timely manner.By considering factors such as machine communication traffic,MAC competition access,and information freshness,this paper develops a cross-layer computing framework in which the peak Age of Information(Ao I)provides a statistical delay boundary in the finite blocklength regime.We also propose a deep machine learning-based multi-access edge computing offloading algorithm.First,a traffic arrival model is established in which the time interval follows the Beta distribution,and then a business service model is proposed based on the carrier sense multiple access with collision avoidance algorithm.The peak Ao I boundary performance of multiple access is evaluated according to stochastic network calculus theory.Finally,an unmanned aerial vehicle-assisted multilevel offloading model with cache is designed,in which the peak Ao I violation probability and energy consumption provide the optimization goals.The optimal offloading strategy is obtained using deep reinforcement learning.Compared with baseline schemes based on non-cooperative game theory with stochastic learning automata and random edge unloading,the proposed algorithm improves the overall performance by approximately 3.52%and 20.73%,respectively,and provides superior deterministic offloading performance by using the peak Ao I boundary.

关 键 词：Power internet of things Ultra-reliable low-latency communication Unmanned aerial vehicle Multi-access edge computing Age of information Stochastic network calculus Deep reinforcement learning 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]