车联网中基于NOMA-MEC的卸载策略研究

张海波; 刘香渝; 荆昆仑; 刘开健; 贺晓帆

doi:10.11999/JEIT200017

车联网中基于NOMA-MEC的卸载策略研究

doi: 10.11999/JEIT200017

张海波^{1, 2},
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)，长江学者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
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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 ZHANG^{1, 2},
Xiangyu LIU^{1, 2
, ,},
Kunlun JING^{1, 2},
Kaijian LIU^{1, 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。
- 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.
- Internet of Vehicles (IoV) /
- Mobile Edge Computing (MEC) /
- Non-Orthogonal Multiple Access (NOMA) /
- Offloading mechanism

HTML全文

图 1 系统模型图

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图 2 深度学习网络模型图

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图 3 任务大小与能耗关系图

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图 4 用户数目与总时延关系图

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图 5 带宽与用户容量关系图

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图 6 时延和能耗要求不同时用户数目与总开销关系图

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图 7 λ = 0.5时用户数目与总开销关系图

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表 1 基于DQN的合作博弈算法

输入：车辆的请求卸载任务集 ${Q_v} = \{ {S_v},{D_v}\}$ 以及各用户功率，　　　 $i \in \{ 1,2, ···, {{k} }\}$
输出：最优功率分配策略
(1) 初始化：用户集合
(2) for $i = 1;i < k;i + +$
(3) 根据式(1)求出各用户时延 $t = \{ {t_1},{t_2}, ···, {t_k}\}$
(4) end for
(5) for $i = 1;i < k;i + +$
(6) $v = [\ ]$
(7) if ${{t(i)} } \ge {{t(n)} }$ ：
(8)　　将该用户添加至新的用户集合 $v$
(9) end if
(10) end for
(11) 利用第1阶段在更新后的用户集合 $v$ 求出与车辆用户 $n$ 匹配的　　信道
(12) 根据第2、第3两个阶段算出奖励函数，通过多次迭代求出　　最优功率分配策略
(13) end

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表 2 混合NOMA-MEC的卸载机制

初始化车辆用户 $v$ 的请求卸载任务 ${q_v} = \{ {s_v},{d_v}\}$ ，信道容量Q
定义该用户的最佳能耗容忍区间 $(0 - {{\rm{e}}^{\max }})$ 、时延能耗的权衡因　子 $\lambda$
(1) 根据表1的功率分配策略分别求出基于OMA, NOMA, 　　 NOMA-MEC的能耗
(2) 令 $G$ 为优化目标函数
(3) define OMA=O, NOMA=N, NOMA–MEC=NM
(4) if ${\rm{sum}}\left( {{Q_v}} \right) > = {Q_{\max }}$ :
(5) if ${{\rm{e}}^v} < = {{\rm{e}}^{\max }}$ :
(6) if $t_N^v < = t_{{\rm{NM}}}^v$ or用户成本函数 $G > = \lambda t_N^vp_N^v + (1 - \lambda )t_N^v$ :
(7) return N
(8) else:
(9) return NM
(10) else:
(11) return NM
(12) else:
(13) return O

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表 3 部分系统仿真参数表

部分系统仿真参数	数值
请求卸载任务数据 ${S_v}$	200～2000 kB
请求卸载任务时延容忍度 ${D_v}$	0.01～3 s
用户噪声功率 ${p_v}$	–114 dBm
任务卸载传输功率 $p$	20～25 dBm
迭代次数 ${I_{{\rm{dd}}} }$	1000
传输带宽 $W$	10～20 MHz
任务传输距离 ${d_v}$	50～500 m

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表 4 DQN相关的参数

DQN相关参数	数值
记忆池大小(Memory pool size)	500
批大小(Batch size)	32
探索率(Exploration probability)	0.1
学习率(Learning rate)	0.001

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