无人机协同车载边缘网络中任务卸载策略  

UAV-enabled Task Offloading Strategy for Vehicular Edge Computing Networks

作　　者：胡峰[1] 谷海洋 林军[3] Hu Feng;Gu Haiyang;Lin Jun(Nanjing Vocational College of Information Technology,Nanjing 210023,China;China Aerospace Science and Industry Corporation Limited,Beijing 100048,China;Nanjing University,Nanjing 210023,China)

机构地区：[1]南京信息职业技术学院,江苏南京210023 [2]中国航天科工集团有限公司,北京100048 [3]南京大学,江苏南京210023

出　　处：《系统仿真学报》2023年第11期2373-2384,共12页Journal of System Simulation

基　　金：国家自然科学基金(2174084);江苏省重点研发计划-重点项目(BE2019003-4);江苏省高技能人才重点项目(2022157)。

摘　　要：随着智能车辆配备越来越多的传感器,从而产生爆炸式增长的传感数据,给车载通信和计算带来了严峻挑战。另外,现代道路布局呈现出三维结构,传统的车联网系统架构无法实现全覆盖和无缝计算。对此,提出一种面向第六代通信技术(sixthgeneration,6G)场景的无人机协同车载边缘网络任务卸载策略。通过车辆和无人机构建灵活智能的车载边缘计算网络,为时延敏感、计算密集型车载任务提供三维边缘计算服务,保障海量车载传感数据及时地处理和融合,最后基于强化学习算法思想,获取网络中最优任务卸载策略。As intelligent vehicles are equipped with more and more sensors,the explosive growth of sensor data is generated,which brings severe challenges to vehicular communication and computing.In addition,the modern road presents a three-dimensional structure,and the system architecture of traditional vehicular networks cannot guarantee full coverage and seamless computing.A task offloading strategy for UAV-assisted and 6G-enabled(Sixth Generation)vehicular edge computing networks is proposed.Furthermore,a flexible and intelligent vehicular edge computing mode is composed by vehicles and UAVs,which provide three-dimensional edge computing services for delay-sensitive and computation-intensive vehicular tasks,and ensure timely processing and fusion of massive sensor data.Finally,the optimal task offloading strategy in the network is obtained by an algorithm based on reinforcement learning.

关 键 词：任务卸载 车载边缘网络 无人机 6G 强化学习 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置] TP391[自动化与计算机技术—控制科学与工程]