基于势博弈的车载边缘计算信道分配方法
Potential Game Based Channel Allocation for Vehicular Edge Computing
查看参考文献24篇
文摘
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针对车载边缘计算环境中,边缘节点在为不同数据传输任务分配信道时产生的同信道干扰(Co-Channel Interferences,CCI)问题,本文形式化定义了车载边缘计算信道分配问题,致力于为不同数据传输任务合理分配信道,最大化数据传输任务的完成率.利用势博弈模型将全局优化的信道分配问题转化为边缘节点间的分布式信道分配博弈,并证明了信道分配博弈中纳什均衡的存在性.提出了基于激励的概率更新策略选择(Incentive-based Probability Update and Strategy Selection)算法,根据迭代中所选策略的激励值更新策略选择概率,并分析算法结果收敛至纳什均衡.最后,通过仿真实验验证了本文算法的收敛性以及收敛结果纳什均衡的有效性,且在任务完成率及信道利用效率上优于现有代表性算法. |
其他语种文摘
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In vehicular edge computing environments, the Co-channel interferences (CCI) is a critical problem when edge nodes allocate channels for different data transmission tasks. This article formulates the problem of channel allocation in vehicular edge computing, aiming at allocating sub-channels for different data transmission tasks and maximizing the ratio of successful data transmission. We transform the global optimization problem of channel allocation into a channel allocation potential game, and prove the existence of nash equilibrium. We propose an Incentive-based probability update and strategy selection algorithm,which updates the strategy selection probability according to the incentive value of the selected strategy in each iteration, and further analyzes the Nash equilibrium converge of the algorithm. Finally,we verify the convergence of the proposed algorithm and the effectiveness of the Nash equilibrium. The experimental results show that the proposed algorithm outperforms existing representative algorithms in terms of the ratio of successful data transmission and channel utilization efficiency. |
来源
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电子学报
,2021,49(5):851-860 【核心库】
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DOI
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10.12263/DZXB.20200994
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关键词
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车载边缘计算
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信道分配
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势博弈
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分布式调度
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地址
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1.
重庆大学计算机学院, 重庆, 400044
2.
东南大学计算机科学与工程学院, 江苏, 南京, 210000
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语种
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中文 |
文献类型
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研究性论文 |
ISSN
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0372-2112 |
学科
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电子技术、通信技术 |
基金
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国家重点研发计划
;
国家自然科学基金
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文献收藏号
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CSCD:6982167
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参考文献 共
24
共2页
|
1.
Shi W S. Edge computing: Vision and challenges.
IEEE Internet of Things Journal,2016,3(5):637-646
|
CSCD被引
144
次
|
|
|
|
2.
Dai Y Y. Deep reinforcement learning and permissioned blockchain for content caching in vehicular edge computing and networks.
IEEE Transactions on Vehicular Technology,2020,69(4):4312-4324
|
CSCD被引
7
次
|
|
|
|
3.
Guo H Z. Intelligent task offloading in vehicular edge computing networks.
IEEE Wireless Communications,2020,27(4):126-132
|
CSCD被引
2
次
|
|
|
|
4.
张彦. 边缘智能驱动的车联网.
物联网学报,2018,2(4):40-48
|
CSCD被引
7
次
|
|
|
|
5.
Liu K. Cooperative data scheduling in hybrid vehicular ad hoc networks: VANET as a software defined network.
IEEE/ACM Transactions on Networking,2016,24(3):1759-1773
|
CSCD被引
11
次
|
|
|
|
6.
彭鑫. 基于路径时延模型的车联网数据分发方案.
电子学报,2017,45(9):2195-2201
|
CSCD被引
4
次
|
|
|
|
7.
Liu C H. Adaptive offloading for time-critical tasks in heterogeneous Internet of vehicles.
IEEE Internet of Things Journal,2020,7(9):7999-8011
|
CSCD被引
3
次
|
|
|
|
8.
Kadhim A J. Energy-efficient multicastrouting protocol based on SDN and fog computing for vehicular networks.
Ad Hoc Networks,2019,84:68-81
|
CSCD被引
3
次
|
|
|
|
9.
张德干. 一种面向高速路车联网场景的自适应路由方法.
电子学报,2020,48(1):172-179
|
CSCD被引
5
次
|
|
|
|
10.
Ning Z L. Mobile edge computingenabled 5G vehicular networks: Toward the integration of communication and computing.
IEEE Vehicular Technology Magazine,2019,14(1):54-61
|
CSCD被引
7
次
|
|
|
|
11.
Xiao K. Efficient fog-assisted heterogeneous data services in software defined VANETs.
Journal of Ambient Intelligence and Humanized Computing,2021,12:261-273
|
CSCD被引
1
次
|
|
|
|
12.
Zhang K. Artificial intelligence inspired transmission scheduling in cognitive vehicular communications and networks.
IEEE Internet of Things Journal,2019,6(2):1987-1997
|
CSCD被引
5
次
|
|
|
|
13.
Eshteiwi K. Impact of Cochannel interference and vehicles as obstacles on full-duplex V2V cooperative wireless network.
IEEE Transactions on Vehicular Technology,2020,69(7):7503-7517
|
CSCD被引
1
次
|
|
|
|
14.
Patra M. Improving delay and energy efficiency of vehicular networks using mobile femto access points.
IEEE Transactions on Vehicular Technology,2017,66(2):1496-1505
|
CSCD被引
6
次
|
|
|
|
15.
Lee K. Learning-based joint optimization of transmit power and harvesting time in wireless-powered networks with Co-channel interference.
IEEE Transactions on Vehicular Technology,2020,69(3):3500-3504
|
CSCD被引
3
次
|
|
|
|
16.
Liu K. A hierarchical architecture for the future Internet of vehicles.
IEEE Communications Magazine,2019,57(7):41-47
|
CSCD被引
2
次
|
|
|
|
17.
Sadek A K. Distributed relay-assignment protocols for coverage expansion in cooperative wireless networks.
IEEE Transactions on Mobile Computing,2010,9(4):505-515
|
CSCD被引
1
次
|
|
|
|
18.
Wyner A. Recent results in the Shannon theory.
IEEE Transactions on Information Theory,1974,20(1):2-10
|
CSCD被引
3
次
|
|
|
|
19.
Sun Z M. Non-cooperative game of throughput and hash length for adaptive merkle tree in mobile wireless networks.
IEEE Transactions on Vehicular Technology,2019,68(5):4625-4650
|
CSCD被引
2
次
|
|
|
|
20.
Zhang T T. Joint allocation of spectral and power resources for non-cooperative wireless localization networks.
IEEE Transactions on Communications,2016,64(9):3733-3745
|
CSCD被引
1
次
|
|
|
|
|