Quyuan Luo – Introduction of Publications

Journal Articles

  1. Quyuan Luo, Changle Li, Tom H. Luan, Weisong Shi and Weigang Wu, “Self-Learning based Computation Offloading for Internet of Vehicles: Model and Algorithm,” accepted to appear in IEEE Transactions on Wireless Communications, March 2021.

  2. 摘要简介 本文针对车联网边缘计算(vehicular edge computing,VEC)中集中式资源分配方案不能满足大规模车辆任务需求的挑战,利用博弈论提出了一种自学习分布式计算卸载方案。 在没有任何集中控制器的情况下,车辆可以快速、高效地进行最优计算卸载决策,有效降低了系统响应时间和开销。
    项目号: This work was supported in part by the Fundamental Research Funds for the Central Universities, in part by the National Natural Science Foundation of China under Grant No. U1801266 and No. 61731017, in part by the scholarship from China Scholarship Council.

  3. Quyuan Luo, Changle Li, Tom H. Luan and Weisong Shi, “Minimizing the Delay and Cost of Computation Offloading for Vehicular Edge Computing,” accepted to appear in IEEE Transactions on Services Computing, March 2021. [pdf ]

  4. 摘要简介: 在多辆车同时竞争通信和计算资源的情况下,如何有效地调度边缘资源以使系统效益最大化是车联网边缘计算(vehicular edge computing,VEC)中的关键问题。 本文旨在从多目标优化的角度对VEC的延迟和卸载计算成本进行详细分析,利用帕累托优化的思想,提出一种基于粒子群算法的计算卸载算法, 得到多目标优化问题的帕累托解。基于仿真结果,对帕累托最优解的时延与代价之间的关系进行了全面的分析和讨论。
    项目号: This work was supported in part by the scholarship from China Scholarship Council, in part by the National Natural Science Foundation of China under Grant No. U1801266.

  5. Quyuan Luo, Changle Li, Tom H. Luan and Weisong Shi, “Collaborative Data Scheduling for Vehicular Edge Computing via Deep Reinforcement Learning,” IEEE Internet of Things Journal, vol. 7, no. 10, pp. 9637-9650, 2020. [pdf ]

  6. 摘要简介: 车联网边缘计算(vehicular edge computing,VEC)中通信与计算需求日益增多,使得如何有效调度VEC中资源成为一项巨大的挑战。 为此,本文首先提出一种VEC中的通信、计算、缓存、协同计算的统一架构。在此架构下,通过考虑任务数据的剩余生存时间和缓存状态, 构建了车端和边缘服务器端的多队列数据缓存模型。为了保证数据能够在时延约束下完成计算,提出了一个使系统开销最小的任务数据调度问题。 然后将该任务数据的调度问题转化为了一种增强学习问题,以反映不同数据调度操作与开销的关系。由于采取不同调度动作得到不同的开销反馈, 最优调度策略将通过与环境的不停的交互被学习到。最后通过真实的车辆轨迹数据,仿真验证了所提算法的性能。 结果表明该方法有效降低了数据计算成本,并有助于在延迟约束条件下完成数据计算。
    项目号: This work was supported by the National Natural Science Foundation of China (U1801266), National Key R&D Program of China (2019YFB1600100), Key R&D Program of Shaanxi (2018ZDXM-GY-038, 2018ZDCXL-GY-04-02), the Youth Innovation Team of Shaanxi Universities, the Science and Technology Projects of Xi'an, China (201809170CX11JC12), and the China Scholarship Council.

  7. Quyuan Luo, Changle Li, Tom H. Luan and Weisong Shi, “EdgeVCD: Intelligent Algorithm Inspired Content Distribution in Vehicular Edge Computing Network,” IEEE Internet of Things Journal, vol. 7, no. 6, pp. 5562-5579, 2020. [pdf ]

  8. 摘要简介: 车联网边缘计算(vehicular edge computing,VEC)中日益增长的服务需求、有限的通信资源以及用车辆用户和内容的差异性使得内容分发成为一项挑战。 本文提出EdgeVCD,一种智能算法启发的内容分发方法。具体来说,首先提出一种二维重要性评估方法权衡车辆用户优先级和内容优先级。 其次,在资源受限的VEC中,提出内容分发系统效用最大化的优化问题。为了解决该复杂的优化问题,我们提出了一种基于模糊逻辑的的最优内容复制车辆选择算法, 选择最合适的内容分发协作车辆。并将优化问题分解成在每一个道路单元内的非线性整型规划问题,并提出一种启发式的免疫克隆算法求解该问题, 该算法收敛性好。最后通过仿真验证了所提EdgeVCD的性能。
    项目号: This work was supported by the National Natural Science Foundation of China (U1801266), National Key R&D Program of China (2019YFB1600100), Key R&D Program of Shaanxi (2018ZDXM-GY-038, 2018ZDCXL-GY-04-02), the Youth Innovation Team of Shaanxi Universities, the Science and Technology Projects of Xi'an, China (201809170CX11JC12), and the China Scholarship Council.

  9. Changle Li, Quyuan Luo, Guoqiang Mao, Min Sheng and Jiandong Li, “Vehicle-Mounted Base Station for Connected and Autonomous Vehicles: Opportunities and Challenges,” IEEE Wireless Communications, vol. 26, no. 4, pp. 30-36, 2019. [pdf ]

  10. 摘要简介: 针对网联无人驾驶车辆产生的海量数据给当前通信网络以及即将到来的5G通信网络带来的巨大挑战,本文提出了一种新型的基于车载基站的方法及网络架构, 通过在网联无人驾驶车辆上安装车载基站,将通信与计算在最靠近数据源端进行融合,可以实现网联无人驾驶车辆海量传感器数据的及时处理和传输、 高精度3D地图下载、视频实时回传和为乘客提供高速网络接入,车载基站除了满足自身车辆安全行驶外,还可以辅助其他无线通信。 最后,通过仿真验证了所提基于车载基站的通信网络架构的吞吐量提高了一个数量级,时延降到毫秒级别,极大的提高了道路安全。
    项目号: The authors would like to thank the support from the National Natural Science Foundation of China under Grant No. U1801266, No. 61571350 and No. 61601344, Key Research and Development Program of Shaanxi (Contract No. 2018ZDXM-GY-038, 2018ZDCXL-GY-04-02), The Youth Innovation Team of Shaanxi Universities, and The Science and Technology Projects of Xi'an, China (201809170CX11JC12).

  11. Quyuan Luo, Changle Li, Tom H. Luan and Yingyou Wen, “Optimal Utility of Vehicles in LTE-V Scenario: An Immune Clone Based Spectrum Allocation Approach,” IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 5, pp. 1942-1953, 2019. [pdf ]

  12. 摘要简介: 针对车联网中频谱资源有限,不能满足高速率的应用需求问题,本文利用二维优先级模型将车辆类型和业务类型分类,利用图着色模型和认知无线电思想管理频谱间干扰, 最后利用免疫克隆算法,高效分配频谱资源。仿真结果表明,相比于传统的遗传算法,该算法能更优地分配频谱以及有更好的收敛性。
    项目号: This work was supported in part by the National Natural Science Foundation of China under Grant 61571350 and Grant 61601344, in part by the Key Research and Development Program of Shaanxi under Grant 2017KW-004, Grant 2017ZDXM-GY-022, and Grant 2018ZDXM-GY-038, and in part by the 111 Project under Grant B08038.

  13. Quyuan Luo, Xuelian Cai, Tom H. Luan and Qiang Ye, “Fuzzy Logic-Based Integrity-Oriented File Transfer for Highway VehicularCommunications,” EURASIP Journal on Wireless Communications and Networking, 2018, 2018(1):3. [pdf ]

  14. 摘要简介: 针对高速公路上的文件传输不完整问题,提出了一种基于分簇和协作的高完整性传输方案。 并且考虑到多个因素影响协作车的选择,找到一种合适的决策算法一般来说是NP难问题,本文运用模糊逻辑进行决策, 综合考虑两车之间的连接时间、相对速度和距离,将其模糊化为语言量,通过模糊推理以及解模糊过程将语言量转化为精确的数值量, 输出每辆车作为协作车的合适度值,最终选择具有最大合适度的车作为协作车。保证协作车是最合适的。 仿真结果表明,运用模糊逻辑的方案比现有方案有更高文件传输的能力。
    项目号: This work was supported by the National Natural Science Foundation of China under Grant No. 61401334 and No. 61571350, Key Research and Development Program of Shaanxi (Contract No. 2017KW-004, 2017ZDXM-GY-022), and the 111 Project (B08038).

Conference Proceedings

  1. Quyuan Luo, Changle Li, Qiang Ye, Tom H. Luan, Lina Zhu and Xiaolei Han, “CFT: A Cluster-based File Transfer Scheme for Highway VANETs,” in Proceedings of IEEE International Conference on Communications (ICC), May 21-25, 2017, Paris, France, pp.1-6. [pdf ]

  2. 摘要简介: 针对高速公路上的文件传输不完整问题,提出了一种基于分簇和协作的高完整性传输方案。 该方案不需要路设的参与和协助,完全自主。仿真结果表明,相比于现有方案,该方案能完整传输用户所需的文件, 并且能大大提高文件传输的大小。
    项目号: This work was supported by the National Natural Science Foundation of China under Grant No. 61571350, No. 61401334 and No. 61601344, the Fundamental Research Funds for the Central Universities (BDY021403, XJS16013 and JB160112), and the 111 Project (B08038).