Can Wireless Environmental Information Decrease Pilot Overhead: A CSI Prediction Example
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866911986274533376 |
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| author | Shi, Lianzheng Zhang, Jianhua Yu, Li Zhang, Yuxiang Zhang, Zhen Cai, Yichen Liu, Guangyi |
| author_facet | Shi, Lianzheng Zhang, Jianhua Yu, Li Zhang, Yuxiang Zhang, Zhen Cai, Yichen Liu, Guangyi |
| contents | Channel state information (CSI) is crucial for massive multi-input multi-output (MIMO) system. As the antenna scale increases, acquiring CSI results in significantly higher system overhead. In this letter, we propose a novel channel prediction method which utilizes wireless environmental information with pilot pattern optimization for CSI prediction (WEI-CSIP). Specifically, scatterers around the mobile station (MS) are abstracted from environmental information using multiview images. Then, an environmental feature map is extracted by a convolutional neural network (CNN). Additionally, the deep probabilistic subsampling (DPS) network acquires an optimal fixed pilot pattern. Finally, a CNN-based channel prediction network is designed to predict the complete CSI, using the environmental feature map and partial CSI. Simulation results show that the WEI-CSIP can reduce pilot overhead from 1/5 to 1/8, while improving prediction accuracy with normalized mean squared error reduced to 0.0113, an improvement of 83.2% compared to traditional channel prediction methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_06558 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Can Wireless Environmental Information Decrease Pilot Overhead: A CSI Prediction Example Shi, Lianzheng Zhang, Jianhua Yu, Li Zhang, Yuxiang Zhang, Zhen Cai, Yichen Liu, Guangyi Signal Processing Channel state information (CSI) is crucial for massive multi-input multi-output (MIMO) system. As the antenna scale increases, acquiring CSI results in significantly higher system overhead. In this letter, we propose a novel channel prediction method which utilizes wireless environmental information with pilot pattern optimization for CSI prediction (WEI-CSIP). Specifically, scatterers around the mobile station (MS) are abstracted from environmental information using multiview images. Then, an environmental feature map is extracted by a convolutional neural network (CNN). Additionally, the deep probabilistic subsampling (DPS) network acquires an optimal fixed pilot pattern. Finally, a CNN-based channel prediction network is designed to predict the complete CSI, using the environmental feature map and partial CSI. Simulation results show that the WEI-CSIP can reduce pilot overhead from 1/5 to 1/8, while improving prediction accuracy with normalized mean squared error reduced to 0.0113, an improvement of 83.2% compared to traditional channel prediction methods. |
| title | Can Wireless Environmental Information Decrease Pilot Overhead: A CSI Prediction Example |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2408.06558 |