Deep learning for compressed sensing based sparse channel estimation in FDD massive MIMO systems
For FDD massive multi-input multi-output (MIMO) downlink system, a novel deep learning method for compressed sensing based sparse channel estimation was proposed, which was called convolutional compressed sensing network (ConCSNet).In the ConCSNet, the convolutional neural network was utilized to so...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2021-08-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021128/ |
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| _version_ | 1841539319091691520 |
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| author | Yuan HUANG Yigang HE Yuting WU Tongtong CHENG Yongbo SUI Shuguang NING |
| author_facet | Yuan HUANG Yigang HE Yuting WU Tongtong CHENG Yongbo SUI Shuguang NING |
| author_sort | Yuan HUANG |
| collection | DOAJ |
| description | For FDD massive multi-input multi-output (MIMO) downlink system, a novel deep learning method for compressed sensing based sparse channel estimation was proposed, which was called convolutional compressed sensing network (ConCSNet).In the ConCSNet, the convolutional neural network was utilized to solve the inverse transformation process from measurement vector y to signal h and solve the underdetermined optimization problem through data-driven method without sparsity.Simulation results show that the algorithm can recover the channel state information in massive MIMO Systems with unknown sparsity more quickly and accurately. |
| format | Article |
| id | doaj-art-ee590707a6574bfebc2dcd157fb37f57 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2021-08-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-ee590707a6574bfebc2dcd157fb37f572025-01-14T07:22:18ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2021-08-0142616959743269Deep learning for compressed sensing based sparse channel estimation in FDD massive MIMO systemsYuan HUANGYigang HEYuting WUTongtong CHENGYongbo SUIShuguang NINGFor FDD massive multi-input multi-output (MIMO) downlink system, a novel deep learning method for compressed sensing based sparse channel estimation was proposed, which was called convolutional compressed sensing network (ConCSNet).In the ConCSNet, the convolutional neural network was utilized to solve the inverse transformation process from measurement vector y to signal h and solve the underdetermined optimization problem through data-driven method without sparsity.Simulation results show that the algorithm can recover the channel state information in massive MIMO Systems with unknown sparsity more quickly and accurately.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021128/wireless communicationFDD massive MIMO systemsparse channel estimationdeep learning |
| spellingShingle | Yuan HUANG Yigang HE Yuting WU Tongtong CHENG Yongbo SUI Shuguang NING Deep learning for compressed sensing based sparse channel estimation in FDD massive MIMO systems Tongxin xuebao wireless communication FDD massive MIMO system sparse channel estimation deep learning |
| title | Deep learning for compressed sensing based sparse channel estimation in FDD massive MIMO systems |
| title_full | Deep learning for compressed sensing based sparse channel estimation in FDD massive MIMO systems |
| title_fullStr | Deep learning for compressed sensing based sparse channel estimation in FDD massive MIMO systems |
| title_full_unstemmed | Deep learning for compressed sensing based sparse channel estimation in FDD massive MIMO systems |
| title_short | Deep learning for compressed sensing based sparse channel estimation in FDD massive MIMO systems |
| title_sort | deep learning for compressed sensing based sparse channel estimation in fdd massive mimo systems |
| topic | wireless communication FDD massive MIMO system sparse channel estimation deep learning |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021128/ |
| work_keys_str_mv | AT yuanhuang deeplearningforcompressedsensingbasedsparsechannelestimationinfddmassivemimosystems AT yiganghe deeplearningforcompressedsensingbasedsparsechannelestimationinfddmassivemimosystems AT yutingwu deeplearningforcompressedsensingbasedsparsechannelestimationinfddmassivemimosystems AT tongtongcheng deeplearningforcompressedsensingbasedsparsechannelestimationinfddmassivemimosystems AT yongbosui deeplearningforcompressedsensingbasedsparsechannelestimationinfddmassivemimosystems AT shuguangning deeplearningforcompressedsensingbasedsparsechannelestimationinfddmassivemimosystems |