Low Complexity Channel Prediction Using TFOS-ELM Method for Massive MIMO Systems
Multiple-input multiple-output (MIMO) technology can potentially help to achieve high data rates for multiuser communication. To achieve better performance, the channel state information (CSI) is estimated by the pilot. However, the estimated CSI cannot be used in downlinks when the mobile speed is...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9004576/ |
| _version_ | 1818379608401117184 |
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| author | Tongtong Cheng Yigang He Wei He Luqiang Shi Yongbo Sui Yuan Huang |
| author_facet | Tongtong Cheng Yigang He Wei He Luqiang Shi Yongbo Sui Yuan Huang |
| author_sort | Tongtong Cheng |
| collection | DOAJ |
| description | Multiple-input multiple-output (MIMO) technology can potentially help to achieve high data rates for multiuser communication. To achieve better performance, the channel state information (CSI) is estimated by the pilot. However, the estimated CSI cannot be used in downlinks when the mobile speed is very high, since it becomes outdated due to the rapid channel variation. In a massive MIMO system, the issue of outdated CSI is serious when using traditional techniques. Therefore, in order to obtain accurate CSI, the prediction of future CSI is required. In this paper, a low complexity online extreme learning machine (ELM) is proposed for the online prediction of the fast fading channel. First, we introduce the structure of the online sequential extreme learning machine (OS-ELM) and combine the training process of the OS-ELM with a forgetting mechanism (FM) to predict fast changing channels. Second, we use the truncated polynomial expansion (TPE) to reduce the computational complexity of the OS-ELM with the FM (FOS-ELM). In addition, the performance of the proposed algorithm is verified through simulation results, and we apply channel prediction in the precoding process. It is shown that the communication quality is improved by our channel prediction algorithm. |
| first_indexed | 2024-12-14T02:05:29Z |
| format | Article |
| id | doaj.art-c20a32cdb20d41a3a1e2ad136224dbc8 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T02:05:29Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c20a32cdb20d41a3a1e2ad136224dbc82022-12-21T23:20:54ZengIEEEIEEE Access2169-35362020-01-018366813669010.1109/ACCESS.2020.29752989004576Low Complexity Channel Prediction Using TFOS-ELM Method for Massive MIMO SystemsTongtong Cheng0https://orcid.org/0000-0002-6603-4903Yigang He1https://orcid.org/0000-0002-6642-0740Wei He2https://orcid.org/0000-0002-9289-1888Luqiang Shi3https://orcid.org/0000-0003-3901-1535Yongbo Sui4https://orcid.org/0000-0001-9555-7669Yuan Huang5https://orcid.org/0000-0002-7887-0095Electrical Engineering Department, Hefei University of Technology, Hefei, ChinaElectrical Engineering Department, Hefei University of Technology, Hefei, ChinaElectrical Engineering Department, Hefei University of Technology, Hefei, ChinaElectrical Engineering Department, Hefei University of Technology, Hefei, ChinaElectrical Engineering Department, Hefei University of Technology, Hefei, ChinaElectrical Engineering Department, Hefei University of Technology, Hefei, ChinaMultiple-input multiple-output (MIMO) technology can potentially help to achieve high data rates for multiuser communication. To achieve better performance, the channel state information (CSI) is estimated by the pilot. However, the estimated CSI cannot be used in downlinks when the mobile speed is very high, since it becomes outdated due to the rapid channel variation. In a massive MIMO system, the issue of outdated CSI is serious when using traditional techniques. Therefore, in order to obtain accurate CSI, the prediction of future CSI is required. In this paper, a low complexity online extreme learning machine (ELM) is proposed for the online prediction of the fast fading channel. First, we introduce the structure of the online sequential extreme learning machine (OS-ELM) and combine the training process of the OS-ELM with a forgetting mechanism (FM) to predict fast changing channels. Second, we use the truncated polynomial expansion (TPE) to reduce the computational complexity of the OS-ELM with the FM (FOS-ELM). In addition, the performance of the proposed algorithm is verified through simulation results, and we apply channel prediction in the precoding process. It is shown that the communication quality is improved by our channel prediction algorithm.https://ieeexplore.ieee.org/document/9004576/Massive MIMOOS-ELMTFOS-ELMchannel predictionprecodinglow complexity |
| spellingShingle | Tongtong Cheng Yigang He Wei He Luqiang Shi Yongbo Sui Yuan Huang Low Complexity Channel Prediction Using TFOS-ELM Method for Massive MIMO Systems IEEE Access Massive MIMO OS-ELM TFOS-ELM channel prediction precoding low complexity |
| title | Low Complexity Channel Prediction Using TFOS-ELM Method for Massive MIMO Systems |
| title_full | Low Complexity Channel Prediction Using TFOS-ELM Method for Massive MIMO Systems |
| title_fullStr | Low Complexity Channel Prediction Using TFOS-ELM Method for Massive MIMO Systems |
| title_full_unstemmed | Low Complexity Channel Prediction Using TFOS-ELM Method for Massive MIMO Systems |
| title_short | Low Complexity Channel Prediction Using TFOS-ELM Method for Massive MIMO Systems |
| title_sort | low complexity channel prediction using tfos elm method for massive mimo systems |
| topic | Massive MIMO OS-ELM TFOS-ELM channel prediction precoding low complexity |
| url | https://ieeexplore.ieee.org/document/9004576/ |
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