Study of channel state information feedback based on artificial intelligence
Accurate acquisition of CSI (channel state information) is the key to the performance of massive MIMO.In current communication systems, when the reciprocity of uplink and downlink is not ideal, codebook-based CSI feedback is used for downlink CSI acquisition.With the increase of antenna scale, codeb...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2022-03-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2022051/ |
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| _version_ | 1841529003406524416 |
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| author | Qiuping HUANG Xiaofeng LIU Qiubin GAO Zhengxuan LIU Liqiang JIN Shaohui SUN |
| author_facet | Qiuping HUANG Xiaofeng LIU Qiubin GAO Zhengxuan LIU Liqiang JIN Shaohui SUN |
| author_sort | Qiuping HUANG |
| collection | DOAJ |
| description | Accurate acquisition of CSI (channel state information) is the key to the performance of massive MIMO.In current communication systems, when the reciprocity of uplink and downlink is not ideal, codebook-based CSI feedback is used for downlink CSI acquisition.With the increase of antenna scale, codebook-based CSI feedback needs more and more overhead.The CSI feedback compression method based on AI (artificial intelligence) was presented, and the standardization impact, communication process and challenges of CSI feedback based on AI were analyzed.Besides, evaluation results were provided.The evaluation results show that compared with codebook-based CSI feedback based on frequency domain basis vector compression, CSI feedback based on AI can significantly reduce the feedback cost at the same feedback accuracy. |
| format | Article |
| id | doaj-art-350957de7f8a4a2dba2be768f0cdb1f9 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2022-03-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-350957de7f8a4a2dba2be768f0cdb1f92025-01-15T03:26:50ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012022-03-0138748359809884Study of channel state information feedback based on artificial intelligenceQiuping HUANGXiaofeng LIUQiubin GAOZhengxuan LIULiqiang JINShaohui SUNAccurate acquisition of CSI (channel state information) is the key to the performance of massive MIMO.In current communication systems, when the reciprocity of uplink and downlink is not ideal, codebook-based CSI feedback is used for downlink CSI acquisition.With the increase of antenna scale, codebook-based CSI feedback needs more and more overhead.The CSI feedback compression method based on AI (artificial intelligence) was presented, and the standardization impact, communication process and challenges of CSI feedback based on AI were analyzed.Besides, evaluation results were provided.The evaluation results show that compared with codebook-based CSI feedback based on frequency domain basis vector compression, CSI feedback based on AI can significantly reduce the feedback cost at the same feedback accuracy.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2022051/massive MIMOAICSI feedback |
| spellingShingle | Qiuping HUANG Xiaofeng LIU Qiubin GAO Zhengxuan LIU Liqiang JIN Shaohui SUN Study of channel state information feedback based on artificial intelligence Dianxin kexue massive MIMO AI CSI feedback |
| title | Study of channel state information feedback based on artificial intelligence |
| title_full | Study of channel state information feedback based on artificial intelligence |
| title_fullStr | Study of channel state information feedback based on artificial intelligence |
| title_full_unstemmed | Study of channel state information feedback based on artificial intelligence |
| title_short | Study of channel state information feedback based on artificial intelligence |
| title_sort | study of channel state information feedback based on artificial intelligence |
| topic | massive MIMO AI CSI feedback |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2022051/ |
| work_keys_str_mv | AT qiupinghuang studyofchannelstateinformationfeedbackbasedonartificialintelligence AT xiaofengliu studyofchannelstateinformationfeedbackbasedonartificialintelligence AT qiubingao studyofchannelstateinformationfeedbackbasedonartificialintelligence AT zhengxuanliu studyofchannelstateinformationfeedbackbasedonartificialintelligence AT liqiangjin studyofchannelstateinformationfeedbackbasedonartificialintelligence AT shaohuisun studyofchannelstateinformationfeedbackbasedonartificialintelligence |