Deep Learning-Based Site-Specific Channel Modeling and Inference

TL;DR

This paper introduces a deep learning framework that uses satellite imagery to accurately predict complete wireless channel impulse responses for site-specific modeling, surpassing traditional methods.

Contribution

It develops a novel deep learning network with a dual-branch pipeline and recurrent module to reconstruct detailed channel parameters from satellite images.

Findings

Achieves high-quality CIR reconstruction with PDP Average Cosine Similarity > 0.96

Establishes a joint dataset of channel measurements and satellite images

Demonstrates effectiveness in unseen scenarios

Abstract

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.