基于深度强化学习的V2X频谱资源管理方法

武明虎; 金波; 赵楠; 王茹

doi:10.13756/j.gtxyj.2023.03.012

您当前的位置：

首页 >

文章列表页 >

基于深度强化学习的V2X频谱资源管理方法

无线通信技术 | 更新时间：2023-05-25

- 基于深度强化学习的V2X频谱资源管理方法
- Spectrum Resourse Management Method of V2X based on Deep Reinforcement Learning
- 光通信研究 2023年第3期页码：71-78
- 作者机构：
  
  湖北工业大学电气与电子工程学院，武汉　430068
- 作者简介：
  
  武明虎（1975-），男，湖北武汉人。教授，博士，主要研究方向为智能电网和图像处理等。
  赵楠，教授。E-mail：nzhao@mail.hbut.edu.cn
- 基金信息：
  
  湖北省重点研发计划资助项目(2021BGD013)
- DOI：10.13756/j.gtxyj.2023.03.012
  中图分类号： TN92
- 纸质出版日期：2023-06-10，
  
  收稿日期：2022-06-21，
  
  修回日期：2022-12-13，
扫描看全文
武明虎，金波，赵楠，等.基于深度强化学习的V2X频谱资源管理方法[J].光通信研究，2023(3):71-78.

Wu M H, Jin B, Zhao N, et al. Spectrum Resourse Management Method of V2X based on Deep Reinforcement Learning[J]. Study on Optical Communications, 2023(3):71-78.
武明虎，金波，赵楠，等.基于深度强化学习的V2X频谱资源管理方法[J].光通信研究，2023(3):71-78. DOI： 10.13756/j.gtxyj.2023.03.012.

Wu M H, Jin B, Zhao N, et al. Spectrum Resourse Management Method of V2X based on Deep Reinforcement Learning[J]. Study on Optical Communications, 2023(3):71-78. DOI： 10.13756/j.gtxyj.2023.03.012.

摘要

针对车辆对一切（V2X）通信所面临的频谱稀缺问题，文章提出了一种深度强化学习方法对V2X频谱资源进行管理。首先，建立单个车辆对基础设施链路的V2X通信模型，结合频谱子带和传输功率等约束条件，构建优化问题以最大化V2X通信网络综合效率；其次，考虑到优化问题的非凸性，将其建模为马尔可夫决策过程；接着，引入基于竞争构架Q网络（Dueling-DQN）算法，以获得最优频谱子带选择和传输功率分配策略，使V2X通信网络综合效率最大化；最后，通过Tensorflow软件平台进行实验仿真，以验证所提方法的性能。实验结果表明，Dueling-DQN算法与其他算法相比，能够获得更高的链路性能和V2X通信网络效率。

Abstract

Aiming at the problem of spectrum scarcity faced by Vehicle to Everything (V2X) communication

a deep reinforcement learning method is proposed to manage V2X spectrum resources. Firstly

the V2X communication model of a single vehicle to infrastructure link is established. Combined with the constraints such as frequency spectrum subband and transmission power

the optimization problem is constructed to maximize the comprehensive efficiency of V2X communication network. Secondly

considering the non-convexity of the optimization problem

the communication model can be regarded as a Markov decision process. Then

the Dueling-Deep Q Network (Dueling-DQN) algorithm is introduced to obtain the optimal spectrum subband selection and transmission power allocation strategy to maximize the comprehensive efficiency of V2X communication network. Finally

the simulation is carried out on tensorflow software platform to verify the performance of the proposed method. The simulation results show that Dueling-DQN algorithm can obtain higher link performance and V2X communication network efficiency compared with other algorithm.

关键词

车辆对一切通信深度强化学习频谱分配传输功率分配车辆对一切通信网络综合效率

Keywords

V2X communicationdeep reinforcement learningspectrum allocationtransmission power distributionV2X communication networkcomprehensive efficiency

references

Sun W, Yuan D, Ström E G, et al. Cluster-based Radio Resource Management for D2D-supported Safety-Critical V2X Communications[J]. IEEE Transactions on Wireless Communications, 2016, 15(4): 2756-2769.

Liang L, Xie S, Li G Y, et al. Graph-based Resource Sharing in Vehicular Communication[J]. IEEE Transactions on Wireless Communications, 2018, 17(7):4579-4592.

Wei Y, Yu F R, Song M, et al. User Scheduling and Resource Allocation in HetNets with Hybrid Energy Supply: An Actor-critic Reinforcement Learning Approach[J]. IEEE Transactions on Wireless Communications, 2018, 17(1):680-692.

Kim M, Lee S, Lim J, et al. Unexpected Collision Avoidance Driving Strategy Using Deep Reinforcement Learning[J]. IEEE Access, 2020, 8:17243-17252.

Sun Y, Peng M, Zhou Y, et al. Application of Machine Learning in Wireless Networks:Key Techniques and Open Issues[J]. IEEE Communications Surveys & Tutorials, 2019, 21(4):3072-3108.

Wu C, Yoshinaga T, Ji Y, et al. Computational Intelligence Inspired Data Delivery for Vehicle-to-roadside Communications[J]. IEEE Transactions on Vehicular Technology, 2018, 67(12):12038-12048.

Sun Y, Peng M, Poor H V. A Distributed Approach to Improving Spectral Efficiency in Uplink Device-to-device-enabled Cloud Radio Access Networks[J]. IEEE Transactions on Communications, 2018, 66(12): 6511-6526.

Yan S, Zhang X, Xiang H, et al. Joint Access Mode Selection and Spectrum Allocation for Fog Computing based Vehicular Networks[J]. IEEE Access, 2019, 7:17725-17735.

Wen Q, Hu B J, Zheng L. Outage-Constrained Device-to-device Links Reuse Maximization and Its Application in Platooning[J]. IEEE Wireless Communications Letters, 2019, 8(6):1635-1638.

Chen S, Hu J, Shi Y, et al. LTE-V: A TD-LTE-based V2X Solution for Future Vehicular Network[J]. IEEE Internet of Things Journal, 2016, 3(6):997-1005.

Liang L, Ye H, Li G Y. Spectrum Sharing in Vehicular Networks based on Multi-agent Reinforcement Learning[J]. IEEE Journal on Selected Areas in Communications, 2019, 37(10):2282-2292.

Zhang X, Peng M, Yan S, et al. Deep-reinforcement-learning-based Mode Selection and Resource Allocation for Cellular V2X Communications[J]. IEEE Internet of Things Journal, 2020, 7(7):6380-6391.

更多指标>

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

数据中心光互连网络拓扑可重构研究

电力光通信网中风险均衡路由算法研究