刘香渝1, 2,,,
荆昆仑1, 2,
刘开健1, 2,
贺晓帆3
1.重庆邮电大学通信与信息工程学院 重庆 400065
2.移动通信技术重庆市重点实验室 重庆 400065
3.武汉大学电子信息学院 武汉 430000
基金项目:国家自然科学基金(61801065, 61601071)，****和创新团队发展计划基金(IRT16R72)，重庆市基础与前沿项目(cstc2018jcyjAX0463)，重庆市留创计划创新类资助项目(cx2020059)
详细信息
作者简介:张海波：男，1979年生，副教授，研究方向为无线资源管理
刘香渝：女，1997年生，硕士生，研究方向为车联网资源管理
荆昆仑：男，1995年生，硕士，研究方向为移动边缘计算
刘开健：女，1981年生，讲师，研究方向为最优化算法
贺晓帆：男，1985年生，教授，研究方向为无线资源优化
通讯作者:刘香渝 lxyyuanna@qq.com
中图分类号:TN915计量
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被引次数:0
出版历程
收稿日期:2020-01-03
修回日期:2021-01-04
网络出版日期:2021-01-08
刊出日期:2021-04-20
Research on NOMA-MEC-Based Offloading Strategy in Internet of Vehicles
Haibo ZHANG1, 2,Xiangyu LIU1, 2,,,
Kunlun JING1, 2,
Kaijian LIU1, 2,
Xiaofan HE3
1. School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2. Chongqing Key Laboratory of Mobile Communications Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
3. School of Electronic Information, Wuhan University, Wuhan 430000, China
Funds:The National Natural Science Foundation of China (61801065, 61601071), The Program for Changjiang Scholars and Innovative Research Team in University (IRT16R72), The General Project on Foundation and Cutting-edge Research Plan of Chongqing (cstc2018jcyjAX0463), Chongqing Innovation and Entrepreneurship Project for Returned Chinese Scholars(cx2020059)
摘要
摘要:随着车联网(IoV)的迅猛发展，请求进行任务卸载的汽车终端用户也逐渐增长，而基于移动边缘计算(MEC)的通信网络能够有效地解决任务卸载在上行传输时延较高的挑战，但是该网络模型同时也面临着信道资源不足的问题。该文引入的非正交多址(NOMA)技术相较于正交多址(OMA)能够在相同的信道资源条件下为更多的用户提供任务卸载，同时考虑到任务卸载过程中多方面的影响因子，提出了混合NOMA-MEC卸载策略。该文设计了一种基于深度学习网络(DQN)的博弈算法，帮助车辆用户进行信道选择，并通过神经网络多次迭代学习，为用户提供最优的功率分配策略。仿真结果表明，该文所提出的混合NOMA-MEC卸载策略能够有效地优化多用户卸载的时延以及能耗，最大限度保证用户效益。
关键词:车联网/
移动边缘计算/
非正交多址/
卸载机制
Abstract:With the rapid development of the Internet of Vehicles (IoV), the number of cars and users requesting tasks offloading is also increasing. The Mobile Edge Computing (MEC) can effectively solve the challenge of high offload transmission delays for task offloading in communication network, but there still is a problem that the channel resources are insufficient in the network model. Compared with traditional Orthogonal Multiple Access (OMA), the technology of Non-Orthogonal Multiple Access (NOMA) can service more users with task offload under the same channel resource conditions. In this paper, considering the multiple aspects of task offloading impact factor, a mixed unloading strategy based on NOMA-MEC is proposed. A game algorithm based on Deep Q-learning Network (DQN) is designed to make channel selection for vehicle users and provide an optimal power allocation strategy through multiple iterative learning of neural networks. The simulation results show that the proposed hybrid NOMA-MEC offloading strategy can effectively optimize the multi-user offloading delay and energy consumption and ensure maximize the benefits of users.
Key words:Internet of Vehicles (IoV)/
Mobile Edge Computing (MEC)/
Non-Orthogonal Multiple Access (NOMA)/
Offloading mechanism
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