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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.28083 |
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| _version_ | 1866911586481864704 |
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| author | Song, Junzhe He, Ruisi Yang, Mi Zhang, Zhengyu Gao, Shuaiqi Ai, Bo Zhong, Zhangdui |
| author_facet | Song, Junzhe He, Ruisi Yang, Mi Zhang, Zhengyu Gao, Shuaiqi Ai, Bo Zhong, Zhangdui |
| contents | Site-specific channel inference plays a critical role in the design and evaluation of next-generation wireless communication systems by considering the surrounding propagation environment. However, traditional methods are unscalable. Recently, satellite imagery has emerged as a valuable modality containing rich propagation information for AI-based channel prediction. However, existing approaches using these images are limited to predicting large-scale fading parameters, lacking the capacity to reconstruct the complete channel impulse response (CIR). To address this limitation, we propose a deep learning-based site-specific channel modeling and inference framework using satellite images to predict structured Tapped Delay Line (TDL) parameters. We first establish a joint channel-satellite dataset based on measurements. Then, a novel deep learning network is developed to reconstruct the channel parameters. Specifically, a cross-attention-fused dual-branch pipeline extracts macroscopic and microscopic environmental features, while a recurrent tracking module captures the long-term dynamic evolution of multipath components. Experimental results demonstrate that the proposed method achieves high-quality reconstruction of the CIR in unseen scenarios, with a Power Delay Profile (PDP) Average Cosine Similarity exceeding 0.96. This work provides a pathway toward site-specific channel inference for future dynamic wireless networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_28083 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Deep Learning-Based Site-Specific Channel Modeling and Inference Song, Junzhe He, Ruisi Yang, Mi Zhang, Zhengyu Gao, Shuaiqi Ai, Bo Zhong, Zhangdui Image and Video Processing Site-specific channel inference plays a critical role in the design and evaluation of next-generation wireless communication systems by considering the surrounding propagation environment. However, traditional methods are unscalable. Recently, satellite imagery has emerged as a valuable modality containing rich propagation information for AI-based channel prediction. However, existing approaches using these images are limited to predicting large-scale fading parameters, lacking the capacity to reconstruct the complete channel impulse response (CIR). To address this limitation, we propose a deep learning-based site-specific channel modeling and inference framework using satellite images to predict structured Tapped Delay Line (TDL) parameters. We first establish a joint channel-satellite dataset based on measurements. Then, a novel deep learning network is developed to reconstruct the channel parameters. Specifically, a cross-attention-fused dual-branch pipeline extracts macroscopic and microscopic environmental features, while a recurrent tracking module captures the long-term dynamic evolution of multipath components. Experimental results demonstrate that the proposed method achieves high-quality reconstruction of the CIR in unseen scenarios, with a Power Delay Profile (PDP) Average Cosine Similarity exceeding 0.96. This work provides a pathway toward site-specific channel inference for future dynamic wireless networks. |
| title | Deep Learning-Based Site-Specific Channel Modeling and Inference |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2603.28083 |