Uplink Assisted MIMO Channel Feedback Method Based on Deep Learning
In order to solve the problem wherein too many base station antennas are deployed in a massive multiple-input–multiple-output system, resulting in high overhead for downlink channel state information feedback, this paper proposes an uplink-assisted channel feedback method based on deep learning. The...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/25/8/1131 |
| _version_ | 1797584834613215232 |
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| author | Qingli Liu Jiaxu Sun Peiling Wang |
| author_facet | Qingli Liu Jiaxu Sun Peiling Wang |
| author_sort | Qingli Liu |
| collection | DOAJ |
| description | In order to solve the problem wherein too many base station antennas are deployed in a massive multiple-input–multiple-output system, resulting in high overhead for downlink channel state information feedback, this paper proposes an uplink-assisted channel feedback method based on deep learning. The method applies the reciprocity of the uplink and downlink, uses uplink channel state information in the base station to help users give feedback on unknown downlink information, and compresses and restores the channel state information. First, an encoder–decoder structure is established. The encoder reduces the network depth and uses multi-resolution convolution to increase the accuracy of channel state information extraction while reducing the number of computations relating to user equipment. Afterward, the channel state information is compressed to reduce feedback overhead in the channel. At the decoder, with the help of the reciprocity of the uplink and downlink, the feature extraction of the uplink’s magnitudes is carried out, and the downlink channel state information is integrated into a channel state information feature matrix, which is restored to its original size. The simulation results show that compared with CSINet, CRNet, CLNet, and DCRNet, indoor reconstruction precision was improved by an average of 16.4%, and outside reconstruction accuracy was improved by an average of 21.2% under all compressions. |
| first_indexed | 2024-03-10T23:58:22Z |
| format | Article |
| id | doaj.art-79e4d63fa8fe4cf29b37f7ee3611b8e0 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T23:58:22Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-79e4d63fa8fe4cf29b37f7ee3611b8e02023-11-19T00:59:01ZengMDPI AGEntropy1099-43002023-07-01258113110.3390/e25081131Uplink Assisted MIMO Channel Feedback Method Based on Deep LearningQingli Liu0Jiaxu Sun1Peiling Wang2Communication and Network Laboratory, Dalian University, Dalian 116622, ChinaCommunication and Network Laboratory, Dalian University, Dalian 116622, ChinaCommunication and Network Laboratory, Dalian University, Dalian 116622, ChinaIn order to solve the problem wherein too many base station antennas are deployed in a massive multiple-input–multiple-output system, resulting in high overhead for downlink channel state information feedback, this paper proposes an uplink-assisted channel feedback method based on deep learning. The method applies the reciprocity of the uplink and downlink, uses uplink channel state information in the base station to help users give feedback on unknown downlink information, and compresses and restores the channel state information. First, an encoder–decoder structure is established. The encoder reduces the network depth and uses multi-resolution convolution to increase the accuracy of channel state information extraction while reducing the number of computations relating to user equipment. Afterward, the channel state information is compressed to reduce feedback overhead in the channel. At the decoder, with the help of the reciprocity of the uplink and downlink, the feature extraction of the uplink’s magnitudes is carried out, and the downlink channel state information is integrated into a channel state information feature matrix, which is restored to its original size. The simulation results show that compared with CSINet, CRNet, CLNet, and DCRNet, indoor reconstruction precision was improved by an average of 16.4%, and outside reconstruction accuracy was improved by an average of 21.2% under all compressions.https://www.mdpi.com/1099-4300/25/8/1131massive MIMOCSI feedbackdeep learningmultipath reciprocity |
| spellingShingle | Qingli Liu Jiaxu Sun Peiling Wang Uplink Assisted MIMO Channel Feedback Method Based on Deep Learning Entropy massive MIMO CSI feedback deep learning multipath reciprocity |
| title | Uplink Assisted MIMO Channel Feedback Method Based on Deep Learning |
| title_full | Uplink Assisted MIMO Channel Feedback Method Based on Deep Learning |
| title_fullStr | Uplink Assisted MIMO Channel Feedback Method Based on Deep Learning |
| title_full_unstemmed | Uplink Assisted MIMO Channel Feedback Method Based on Deep Learning |
| title_short | Uplink Assisted MIMO Channel Feedback Method Based on Deep Learning |
| title_sort | uplink assisted mimo channel feedback method based on deep learning |
| topic | massive MIMO CSI feedback deep learning multipath reciprocity |
| url | https://www.mdpi.com/1099-4300/25/8/1131 |
| work_keys_str_mv | AT qingliliu uplinkassistedmimochannelfeedbackmethodbasedondeeplearning AT jiaxusun uplinkassistedmimochannelfeedbackmethodbasedondeeplearning AT peilingwang uplinkassistedmimochannelfeedbackmethodbasedondeeplearning |