Abstract
Accurate channel estimation is an essential factor in determining the efficiency of a wireless communication system. Moreover, in Reconfigurable Intelligent Surfaces(RIS)-Assisted wireless networks using millimeter wave (mmWave), it is crucial to optimize each RIS element's phase shift. Therefore, in this paper, we propose a channel estimation method in TDD-based wireless communication system using auto encoder in RIS-Assisted wireless networks. The trade-off relationship between channel estimation accuracy and the number of pilot signals is optimized when performing channel estimation. Through denoising autoencoder and Average Percentage of Zeros(APoZ), we find the optimal pilot pattern considering not only the number of pilots but also the location. As a result of the experiment, the proposed method has little difference in performance or outperforms the full neural network without pruning.
| Original language | English |
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| Title of host publication | APNOMS 2022 - 23rd Asia-Pacific Network Operations and Management Symposium |
| Subtitle of host publication | Data-Driven Intelligent Management in the Era of beyond 5G |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9784885523397 |
| DOIs | |
| Publication status | Published - 2022 |
| Event | 23rd Asia-Pacific Network Operations and Management Symposium, APNOMS 2022 - Takamatsu, Japan Duration: 28 Sept 2022 → 30 Sept 2022 |
Publication series
| Name | APNOMS 2022 - 23rd Asia-Pacific Network Operations and Management Symposium: Data-Driven Intelligent Management in the Era of beyond 5G |
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Conference
| Conference | 23rd Asia-Pacific Network Operations and Management Symposium, APNOMS 2022 |
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| Country/Territory | Japan |
| City | Takamatsu |
| Period | 28/09/22 → 30/09/22 |
Bibliographical note
Publisher Copyright:© 2022 IEICE.
Keywords
- Channel Estimation
- Deep Learning
- Reconfigurable Intelligent Surfaces
- mm Wave