Research on NOMA-MEC-Based Offloading Strategy in Internet of Vehicles

Haibo ZHANG; Xiangyu LIU; Kunlun JING; Kaijian LIU; Xiaofan HE

doi:10.11999/JEIT200017

Volume 43 Issue 4

Apr. 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(4): 1072-1079

Haibo ZHANG, Xiangyu LIU, Kunlun JING, Kaijian LIU, Xiaofan HE. Research on NOMA-MEC-Based Offloading Strategy in Internet of Vehicles[J]. Journal of Electronics & Information Technology, 2021, 43(4): 1072-1079. doi: 10.11999/JEIT200017

Citation:

Haibo ZHANG, Xiangyu LIU, Kunlun JING, Kaijian LIU, Xiaofan HE. Research on NOMA-MEC-Based Offloading Strategy in Internet of Vehicles[J]. Journal of Electronics & Information Technology, 2021, 43(4): 1072-1079. doi: 10.11999/JEIT200017

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Research on NOMA-MEC-Based Offloading Strategy in Internet of Vehicles

doi: 10.11999/JEIT200017

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)

Received Date: 2020-01-03
Rev Recd Date: 2021-01-04

Available Online: 2021-01-08

Publish Date: 2021-04-20

Abstract

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

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References

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