دورية أكاديمية
Model‐driven neural network based for HPO‐MIMO channel estimation
| العنوان: | Model‐driven neural network based for HPO‐MIMO channel estimation |
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| المؤلفون: | Yi Gong, Yujia Liu, Fanke Meng, Zhan Xu |
| المصدر: | Electronics Letters, Vol 60, Iss 11, Pp n/a-n/a (2024) |
| بيانات النشر: | Wiley, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Electrical engineering. Electronics. Nuclear engineering |
| مصطلحات موضوعية: | channel estimation, HPO‐MIMO, deep learning, Electrical engineering. Electronics. Nuclear engineering, TK1-9971 |
| الوصف: | Abstract Integrated Sensing and Communications (ISAC) need to process data streams in high‐speed sensor data acquisition or high‐speed wireless communications. To process the data can require more computing and communication resources, resulting in higher power consumption. Halved‐Phase Only Multiple Input Multiple Output (HPO‐MIMO) communication technology can solve this problem by using low‐power nonlinear detection devices. In ISAC, Channel Estimation (CE) technology can provide key channel characteristics and state information for sensing and collaborative work of perception and communication tasks. However, HPO‐MIMO system cannot realize CE using traditional receiver schemes because of the missing amplitude. In order to solve this problem, two HPO‐MIMO CE schemes based on model‐driven deep learning are proposed in this paper. The proposed schemes include a Densely Residual Network (DRN) and a Inception‐Resnet (IR), which is suitable for the case of sufficient data and insufficient data, respectively. The simulation results show that the performance of DRN based scheme is better than that of IR based scheme when the data amount is sufficient, and the performance of IR based scheme is better when the dataset is small. In addition, the proposed CE schemes work well with a range of antenna sizes and distances. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1350-911X 0013-5194 |
| Relation: | https://doaj.org/toc/0013-5194; https://doaj.org/toc/1350-911X |
| DOI: | 10.1049/ell2.13209 |
| URL الوصول: | https://doaj.org/article/83dae7298b744461ace47c2e7f7955d9 |
| رقم الأكسشن: | edsdoj.83dae7298b744461ace47c2e7f7955d9 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 1350911X 00135194 |
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| DOI: | 10.1049/ell2.13209 |