AdapCsiNet: Environment-Adaptive CSI Feedback via Scene Graph-Aided Deep Learning
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908320213762048 |
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| author | Liu, Jiayi Guo, Jiajia Cui, Yiming Wen, Chao-Kai Jin, Shi |
| author_facet | Liu, Jiayi Guo, Jiajia Cui, Yiming Wen, Chao-Kai Jin, Shi |
| contents | Accurate channel state information (CSI) is critical for realizing the full potential of multiple-antenna wireless communication systems. While deep learning (DL)-based CSI feedback methods have shown promise in reducing feedback overhead, their generalization capability across varying propagation environments remains limited due to their data-driven nature. Existing solutions based on online training improve adaptability but impose significant overhead in terms of data collection and computational resources. In this work, we propose AdapCsiNet, an environment-adaptive DL-based CSI feedback framework that eliminates the need for online training. By integrating environmental information -- represented as a scene graph -- into a hypernetwork-guided CSI reconstruction process, AdapCsiNet dynamically adapts to diverse channel conditions. A two-step training strategy is introduced to ensure baseline reconstruction performance and effective environment-aware adaptation. Simulation results demonstrate that AdapCsiNet achieves up to 46.4% improvement in CSI reconstruction accuracy and matches the performance of online learning methods without incurring additional runtime overhead. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_10798 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AdapCsiNet: Environment-Adaptive CSI Feedback via Scene Graph-Aided Deep Learning Liu, Jiayi Guo, Jiajia Cui, Yiming Wen, Chao-Kai Jin, Shi Information Theory Signal Processing Accurate channel state information (CSI) is critical for realizing the full potential of multiple-antenna wireless communication systems. While deep learning (DL)-based CSI feedback methods have shown promise in reducing feedback overhead, their generalization capability across varying propagation environments remains limited due to their data-driven nature. Existing solutions based on online training improve adaptability but impose significant overhead in terms of data collection and computational resources. In this work, we propose AdapCsiNet, an environment-adaptive DL-based CSI feedback framework that eliminates the need for online training. By integrating environmental information -- represented as a scene graph -- into a hypernetwork-guided CSI reconstruction process, AdapCsiNet dynamically adapts to diverse channel conditions. A two-step training strategy is introduced to ensure baseline reconstruction performance and effective environment-aware adaptation. Simulation results demonstrate that AdapCsiNet achieves up to 46.4% improvement in CSI reconstruction accuracy and matches the performance of online learning methods without incurring additional runtime overhead. |
| title | AdapCsiNet: Environment-Adaptive CSI Feedback via Scene Graph-Aided Deep Learning |
| topic | Information Theory Signal Processing |
| url | https://arxiv.org/abs/2504.10798 |