Institute of Computing Technology, Chinese Academy IR
Multi-Antenna Channel Interpolation via Tucker Decomposed Extreme Learning Machine | |
Zhang, Han1; Ai, Bo2; Xu, Wenjun3; Xu, Li4; Cui, Shuguang1 | |
2019-07-01 | |
发表期刊 | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
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ISSN | 0018-9545 |
卷号 | 68期号:7页码:7160-7163 |
摘要 | Channel interpolation is an essential technique for providing high-accuracy estimation of the channel state information for wireless systems design where the frequency-space structural correlations of multi-antenna channel are typically hidden in matrix or tensor forms. In this correspondence paper, a modified extreme learning machine (ELM) that can process tensorial data, or ELM model with tensorial inputs (TELM), is proposed to handle the channel interpolation task. The TELM inherits many good properties from ELMs. Based on the TELM, the Tucker decomposed extreme learning machine is proposed for further improving the performance. Furthermore, we establish a theoretical argument to measure the interpolation capability of the proposed learning machines. Experimental results verify that our proposed learning machines can achieve comparable mean squared error (MSE) performance against the traditional ELMs but with 15% shorter running time, and outperform the other methods for a 20% margin measured in MSE for channel interpolation. |
关键词 | Channel interpolation extreme learning machine tensor decomposition |
DOI | 10.1109/TVT.2019.2913865 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Science Foundation[CNS-1824553] ; National Science Foundation[DMS-1622433] ; National Science Foundation[AST-1547436] ; National Science Foundation[ECCS-1659025] |
WOS研究方向 | Engineering ; Telecommunications ; Transportation |
WOS类目 | Engineering, Electrical & Electronic ; Telecommunications ; Transportation Science & Technology |
WOS记录号 | WOS:000476775000077 |
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/2XEOYT63/4474 |
专题 | 中国科学院计算技术研究所期刊论文_英文 |
通讯作者 | Cui, Shuguang |
作者单位 | 1.Univ Calif Davis, Dept Elect & Comp Engn, Davis, CA 95616 USA 2.Beijing Jiaotong Univ, State Key Lab Rail Traff Control & Safety, Beijing 100044, Peoples R China 3.Beijing Univ Posts & Telecommun, Minist Educ, Key Lab Universal Wireless Commun, Beijing 100876, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China |
推荐引用方式 GB/T 7714 | Zhang, Han,Ai, Bo,Xu, Wenjun,et al. Multi-Antenna Channel Interpolation via Tucker Decomposed Extreme Learning Machine[J]. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY,2019,68(7):7160-7163. |
APA | Zhang, Han,Ai, Bo,Xu, Wenjun,Xu, Li,&Cui, Shuguang.(2019).Multi-Antenna Channel Interpolation via Tucker Decomposed Extreme Learning Machine.IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY,68(7),7160-7163. |
MLA | Zhang, Han,et al."Multi-Antenna Channel Interpolation via Tucker Decomposed Extreme Learning Machine".IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 68.7(2019):7160-7163. |
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