无人机协同车载边缘网络中任务卸载策略

doi:10.16182/j.issn1004731x.joss.23-0307

摘要/Abstract

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(sixth generation，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。

关键词: 任务卸载, 车载边缘网络, 无人机, 6G, 强化学习

Abstract:

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.

Key words: task offloading, vehicular computing networks, UAV, 6G(sixth generation), reinforcement learning

中图分类号:

TP273

胡峰,谷海洋,林军 . 无人机协同车载边缘网络中任务卸载策略[J]. 系统仿真学报, 2023, 35(11): 2373-2384.

Hu Feng,Gu Haiyang,Lin Jun . UAV-enabled Task Offloading Strategy for Vehicular Edge Computing Networks[J]. Journal of System Simulation, 2023, 35(11): 2373-2384.

图/表 7

图1

图2

表1

仿真参数表

参数	含义	取值	参数	含义	取值
$M$	资源总数目	15	$K$	资源最大数目	8~15
$N U$	无人机数目	2	$T U$	无人机资源数目	5~10
$λ t$	任务到达速率	5	$μ t$	任务离开速率	5
$λ v$	车辆到达速率	10	$T 0$	本地计算时间	20
$μ v$	车辆离开速率	10	$P$	车辆发射功率	5
$E$	能量开销	20	$η$	卸载失败惩罚	20
$γ$	传输时延价格	2	$α$	收益折扣率	0.1
$ζ e$	节省能量价格	2	$ζ t$	节省时延价格	2
$ω e$	节省能量权重	0.5	$ω t$	节省时延权重	0.5

表1

图3

图4

图5

图6

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