Accurate cross-band channel prediction is essential for 6G networks, particularly in the upper mid-band (FR3, 7--24 GHz), where penetration loss and blockage are severe. Although ray tracing (RT) provides high-fidelity modeling, it remains computationally intensive, and high-frequency data acquisition is costly. To address these challenges, we propose CIR-UNext, a deep learning framework designed to predict 7 GHz channel impulse responses (CIRs) by leveraging abundant 3.5 GHz CIRs. The framework integrates an RT-based dataset pipeline with attention U-Net (AU-Net) variants for gain and phase prediction. The proposed AU-Net-Aux model achieves a median gain error of 0.58 dB and a phase prediction error of 0.27 rad on unseen complex environments. Furthermore, we extend CIR-UNext into a foundation model, Channel2ComMap, for throughput prediction in MIMO-OFDM systems, demonstrating superior performance compared with existing approaches. Overall, CIR-UNext provides an efficient and scalable solution for cross-band prediction, enabling applications such as localization, beam management, digital twins, and intelligent resource allocation in 6G networks.

翻译：精确的跨频段信道预测对于6G网络至关重要，尤其是在中高频段（FR3，7--24 GHz），该频段的穿透损耗与遮挡效应极为显著。尽管射线追踪（RT）能提供高保真度的建模，但其计算开销巨大，且高频数据采集成本高昂。为应对这些挑战，我们提出了CIR-UNext——一种深度学习框架，旨在利用丰富的3.5 GHz信道冲激响应（CIR）数据来预测7 GHz频段的CIR。该框架将基于RT的数据集处理流程与注意力U-Net（AU-Net）的变体相结合，分别用于增益与相位预测。所提出的AU-Net-Aux模型在未见过的复杂环境中实现了0.58 dB的中值增益误差与0.27弧度的相位预测误差。此外，我们将CIR-UNext扩展为基础模型Channel2ComMap，用于MIMO-OFDM系统的吞吐量预测，其性能优于现有方法。总体而言，CIR-UNext为跨频段预测提供了高效且可扩展的解决方案，可支持6G网络中的定位、波束管理、数字孪生及智能资源分配等应用。