Deep Learning Based Site-Specific Channel Inference Using Satellite Images

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, while existing AI-based approaches using satellite image are confined 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 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.