Self-attention mechanism-based CSI eigenvector feedback for massive MIMO
Massive multiple-input multiple-output (MIMO) system can provide satisfying gain of spectrum efficiency for 5G and future wireless communication systems.In frequency-division duplex (FDD) mode, downlink channel state information (CSI) needs to be accurately fed back to the base station side to obtai...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2023-11-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2023247/ |
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| _version_ | 1841533661742104576 |
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| author | Bei YANG Xin LIANG Hang YIN Zheng JIANG Xiaoming SHE |
| author_facet | Bei YANG Xin LIANG Hang YIN Zheng JIANG Xiaoming SHE |
| author_sort | Bei YANG |
| collection | DOAJ |
| description | Massive multiple-input multiple-output (MIMO) system can provide satisfying gain of spectrum efficiency for 5G and future wireless communication systems.In frequency-division duplex (FDD) mode, downlink channel state information (CSI) needs to be accurately fed back to the base station side to obtain this gain.To improve the feedback accuracy of downlink CSI eigenvector, a self-attention mechanism-based CSI feedback method named SA-CsiNet was proposed.SA-CsiNet respectively deployed self-attention modules at the encoder and the decoder to achieve feature extraction and reconstruction of CSI.Experimental results show that compared with codebook-based and conventional deep learning-based CSI feedback approaches, SA-CsiNet provides higher reconstruction accuracy of CSI. |
| format | Article |
| id | doaj-art-b28d0aff2d344575b7362c520bc16c4d |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2023-11-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-b28d0aff2d344575b7362c520bc16c4d2025-01-15T02:57:58ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012023-11-013912813659559609Self-attention mechanism-based CSI eigenvector feedback for massive MIMOBei YANGXin LIANGHang YINZheng JIANGXiaoming SHEMassive multiple-input multiple-output (MIMO) system can provide satisfying gain of spectrum efficiency for 5G and future wireless communication systems.In frequency-division duplex (FDD) mode, downlink channel state information (CSI) needs to be accurately fed back to the base station side to obtain this gain.To improve the feedback accuracy of downlink CSI eigenvector, a self-attention mechanism-based CSI feedback method named SA-CsiNet was proposed.SA-CsiNet respectively deployed self-attention modules at the encoder and the decoder to achieve feature extraction and reconstruction of CSI.Experimental results show that compared with codebook-based and conventional deep learning-based CSI feedback approaches, SA-CsiNet provides higher reconstruction accuracy of CSI.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2023247/channel feedbackmassive MIMOself-attention mechanismdeep learning |
| spellingShingle | Bei YANG Xin LIANG Hang YIN Zheng JIANG Xiaoming SHE Self-attention mechanism-based CSI eigenvector feedback for massive MIMO Dianxin kexue channel feedback massive MIMO self-attention mechanism deep learning |
| title | Self-attention mechanism-based CSI eigenvector feedback for massive MIMO |
| title_full | Self-attention mechanism-based CSI eigenvector feedback for massive MIMO |
| title_fullStr | Self-attention mechanism-based CSI eigenvector feedback for massive MIMO |
| title_full_unstemmed | Self-attention mechanism-based CSI eigenvector feedback for massive MIMO |
| title_short | Self-attention mechanism-based CSI eigenvector feedback for massive MIMO |
| title_sort | self attention mechanism based csi eigenvector feedback for massive mimo |
| topic | channel feedback massive MIMO self-attention mechanism deep learning |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2023247/ |
| work_keys_str_mv | AT beiyang selfattentionmechanismbasedcsieigenvectorfeedbackformassivemimo AT xinliang selfattentionmechanismbasedcsieigenvectorfeedbackformassivemimo AT hangyin selfattentionmechanismbasedcsieigenvectorfeedbackformassivemimo AT zhengjiang selfattentionmechanismbasedcsieigenvectorfeedbackformassivemimo AT xiaomingshe selfattentionmechanismbasedcsieigenvectorfeedbackformassivemimo |