期刊論文
| 學年 | 113 |
|---|---|
| 學期 | 1 |
| 出版（發表）日期 | 2024-11-12 |
| 作品名稱 | 6G Technology for Indoor Localization by Deep Learning with Attention Mechanism |
| 作品名稱（其他語言） | |
| 著者 | Chien-Ching Chiu; Hung-Yu Wu; Po-Hsiang Chen; Chen-En Chao; Eng Hock Lim |
| 單位 | |
| 出版者 | |
| 著錄名稱、卷期、頁數 | Applied Sciences (14)22, 10395 |
| 摘要 | This paper explores 6G technology for indoor positioning, focusing on accuracy and reliability using convolutional neural networks (CNN) with channel state information (CSI). Indoor positioning is critical for smart applications and the Internet of Things (IoT). 6G is expected to significantly enhance positioning performance through the use of higher frequency bands, such as terahertz frequencies with wider bandwidth. Preliminary results show that 6G-based systems are expected to achieve centimeter-level positioning accuracy due to the integration of advanced artificial intelligence algorithms and terahertz frequencies. In addition, this paper also investigates the impact of self-attention (SA) and channel attention (CA) mechanisms on indoor positioning systems. The combination of these attention mechanisms with conventional CNNs has been proposed to further improve the accuracy and robustness of localization systems. CNN with SA demonstrates a 50% reduction in RMSE compared to CNN by capturing spatial dependencies more effectively. |
| 關鍵字 | indoor localization; indoor positioning; internet of things; channel state information; fingerprint; 6G technology; terahertz frequencies; self-attention; channel attention |
| 語言 | en_US |
| ISSN | 2076-3417 |
| 期刊性質 | 國外 |
| 收錄於 | SCI |
| 產學合作 | |
| 通訊作者 | |
| 審稿制度 | 否 |
| 國別 | CHE |
| 公開徵稿 | |
| 出版型式 | ,電子版 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/127511 ) |