Task offloading and resource allocation in vehicle heterogeneous networks with MEC

Haibo ZHANG; Qiuji LUAN; Jiang ZHU; Fangwei LI

doi:10.11959/j.issn.2096-3750.2018.00062

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Task offloading and resource allocation in vehicle heterogeneous networks with MEC

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

- Task offloading and resource allocation in vehicle heterogeneous networks with MEC
- Chinese Journal on Internet of Things Vol. 2, Issue 3, Pages: 36-43(2018)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-3750.2018.00062
  CLC： TN929.5
- Published：30 September 2018，
  
  Published Online：2018-09，
- 稿件说明：
移动端阅览
HAIBO ZHANG, QIUJI LUAN, JIANG ZHU, et al. Task offloading and resource allocation in vehicle heterogeneous networks with MEC. [J]. Chinese journal on internet of things, 2018, 2(3): 36-43.
DOI：

HAIBO ZHANG, QIUJI LUAN, JIANG ZHU, et al. Task offloading and resource allocation in vehicle heterogeneous networks with MEC. [J]. Chinese journal on internet of things, 2018, 2(3): 36-43. DOI： 10.11959/j.issn.2096-3750.2018.00062.

摘要

针对移动边缘计算（MEC）提供IT服务环境和云计算能力带来的高带宽、低时延优势，结合LTE免授权频谱（LTE-U）技术，研究了车辆异构网络中基于 MEC 的任务卸载模式决策和资源分配问题。考虑链路差异化要求，即车辆到路边单元（V2I）链路的高容量和车辆到车辆（V2V）链路的高可靠性，将用户服务质量（QoS）建模为容量与时延的组合形式。首先采用改进的 K-means 算法依据不同的 QoS 对请求车辆进行聚类，从而确定通信模式，其次利用基于无竞争期（CFP）的LTE-U，结合载波聚合（CA）技术，使用分布式Q-Learning算法进行信道和功率分配。仿真结果表明，所提机制可以在保证V2V链路可靠性的同时，使得V2I遍历容量最大化。

Abstract

Based on the advantages of high-bandwidth and low-latency brought by mobile edge computing (MEC)

which could provide IT service environment and cloud computing capability

combined with the long-term evolution unlicensed (LTE-U) technology

the task offloading decision and resource allocation issues in vehicle heterogeneous network were studied.Considering the link differentiation requirements

which were the high capacity of vehicle-to-roadside unit (V2I) links and the super reliability of vehicle-to-vehicle (V2V) links

quality of service (QoS) was modeled as the combination of capacity and latency.Firstly

the improved K-means algorithm was used to cluster the request vehicles according to different QoS to determine the communication mode.Secondly

the LTE-U technology based on non-competition period (CFP) which was combined with carrier aggregation (CA) technology

and the distribution Q-Learning algorithm were adopted to allocate the channel and power.The simulation results show that the proposed mechanism can maximize the V2I link traversal capacity while ensuring the reliability of the V2I link.

关键词

移动边缘计算LTE-U车联网任务卸载资源分配

Keywords

mobile edge computingLTE-Uvehicle networktask offloadingresource allocation

references

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