Institute of Computing Technology, Chinese Academy IR
Resource Allocation and Computation Offloading in a Millimeter-Wave Train-Ground Network | |
Li, Linqian1,2; Niu, Yong1,3; Mao, Shiwen4; Ai, Bo1,2; Zhong, Zhangdui1,2; Wang, Ning5; Chen, Yali6 | |
2022-10-01 | |
发表期刊 | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY |
ISSN | 0018-9545 |
卷号 | 71期号:10页码:10615-10630 |
摘要 | In this paper, we consider an mmWave-based train-ground communication system in the high-speed railway (HSR) scenario, where the computation tasks of users can be partially offloaded to the rail-side base station (BS) or the mobile relays (MRs) deployed on the roof of the train. The MRs operate in the full-duplex (FD) mode to achieve high spectrum utilization. We formulate the problem of minimizing the average task execution latency of all users, under local device and MRs energy consumption constraints. We propose a joint resource allocation and computation offloading scheme (JRACO) to solve the problem. It consists of a resource allocation and computation offloading (RACO) algorithm and an MR Energy constraint algorithm. RACO utilizes the matching game theory to iterate between two subproblems, i.e., data segmentation and user association and sub-channel allocation. With the RACO results, the MR energy constraint algorithm ensures that the MR energy consumption constraint is satisfied. Extensive simulations validate that JRACO can effectively reduce the average latency and increase the number of served users compared with three baseline schemes. |
关键词 | Resource management Task analysis Rail transportation Energy consumption Communication systems Broadband communication Millimeter wave communication Full-duplex (FD) communications millimeter-wave (mmWave) communications mobile edge computing (MEC) resource allocation train-ground communications |
DOI | 10.1109/TVT.2022.3185331 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | Fundamental Research Funds for the Central Universities, China[2022JBXT001] ; Fundamental Research Funds for the Central Universities, China[2022JBQY004] ; National Natural Science Foundation of China[61801016] ; National Natural Science Foundation of China[61725101] ; National Natural Science Foundation of China[61961130391] ; National Natural Science Foundation of China[U1834210] ; National Key Research and Development Program[2021YFB2900301] ; State Key Laboratory of Rail Traffic Control and Safety[RCS2021ZT009] ; Beijing Jiaotong University ; Southeast University[2021D09] ; National Key R&D Program of China[2020YFB1806903] ; Fundamental Research Funds for the Central Universities[2020JBM089] ; Project of China Shenhua[GJNY-20-01-1] ; NSF[ECCS-1923717] |
WOS研究方向 | Engineering ; Telecommunications ; Transportation |
WOS类目 | Engineering, Electrical & Electronic ; Telecommunications ; Transportation Science & Technology |
WOS记录号 | WOS:000870332400033 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.204/handle/2XEOYT63/19926 |
专题 | 中国科学院计算技术研究所期刊论文 |
通讯作者 | Niu, Yong |
作者单位 | 1.Beijing Jiaotong Univ, State Key Lab Rail Traff Control & Safety, Beijing 100044, Peoples R China 2.Beijing Jiaotong Univ, Beijing Engn Res Ctr High Speed Railway Broadband, Beijing 100044, Peoples R China 3.Southeast Univ, Natl Mobile Commun Res Lab, Nanjing 211189, Peoples R China 4.Auburn Univ, Dept Elect & Comp Engn, Auburn, AL 36849 USA 5.Zhengzhou Univ, Sch Informat Engn, Zhengzhou 450001, Peoples R China 6.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China |
推荐引用方式 GB/T 7714 | Li, Linqian,Niu, Yong,Mao, Shiwen,et al. Resource Allocation and Computation Offloading in a Millimeter-Wave Train-Ground Network[J]. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY,2022,71(10):10615-10630. |
APA | Li, Linqian.,Niu, Yong.,Mao, Shiwen.,Ai, Bo.,Zhong, Zhangdui.,...&Chen, Yali.(2022).Resource Allocation and Computation Offloading in a Millimeter-Wave Train-Ground Network.IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY,71(10),10615-10630. |
MLA | Li, Linqian,et al."Resource Allocation and Computation Offloading in a Millimeter-Wave Train-Ground Network".IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 71.10(2022):10615-10630. |
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